Article

Glomerulosclerosis Identification in Whole Slide Images using Semantic Segmentation

Article

Glomerulosclerosis Identification in Whole Slide Images using Semantic Segmentation

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Abstract

Background and objective: Glomeruli identification, i.e., detection and characterization, is a key procedure in many nephropathology studies. In this paper, semantic segmentation based on convolutional neural networks (CNN) is proposed to detect glomeruli using Whole Slide Imaging (WSI) follows by a classification CNN to divide the glomeruli into normal and sclerosed. Methods: Comparison between U-Net and SegNet CNNs is performed for pixel-level segmentation considering both a two and three class problem, that is, a) non-glomerular and glomerular structures and b) non-glomerular normal glomerular and sclerotic structures. The two class semantic segmentation result is then used for a CNN classification where glomerular regions are divided into normal and global sclerosed glomeruli. Results: These methods were tested on a dataset composed of 47 WSIs belonging to human kidney sections stained with Periodic Acid Schiff (PAS). The best approach was the SegNet for two class segmentation follows by a fine-tuned AlexNet network to characterize the glomeruli. 98.16% of accuracy was obtained with this process of consecutive CNNs (SegNet-AlexNet) for segmentation and classification. Conclusion: The results obtained demonstrate that the sequential CNN segmentation-classification strategy achieves higher accuracy reducing misclassified cases and therefore being the methodology proposed for glomerulosclerosis detection.

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... The transfer learning technique used in this study was taken from several machine learning models after reviewing various methods by considering the procedures and work results obtained. Bueno et al. [8] used UNet and SegNet to divide the glomerulus into three groups based on segmentation pixels. The data processing findings demonstrate that data prediction from the train data is accurate to 98.16 percent. ...
... The experimental data consisted of 5095 biomolecular pictures of the kidneys in png format, each of which was derived from 2926 photos of the segmentation data conducted by Bueno et al. [8,12]. These data can be used to benchmark the assessment data in a test classification system that divides the data into normal glomeruli and sclerosed glomeruli. ...
... When compared to numerous newer techniques, the VGG method's performance is deemed steady. This outcome was demonstrated by the method's adaptability, which allowed new methods to be created [8]. The simplicity of the VGG approach influenced other methods that could enhance analytical findings without entirely altering the existing architecture. ...
Article
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The rising global incidence of chronic kidney disease necessitates the development of image categorization of renal glomeruli. COVID-19 has been shown to enter the glomerulus, a tissue structure in the kidney. This study observes the differences between focal-segmental, normal and sclerotic renal glomerular tissue diseases. The splitting and combining of allied and multivariate models was accomplished utilizing a combined technique using existing models. In this study, model combinations are created by using a high-accuracy accuracy-based model to improve other models. This research exhibits excellent accuracy and consistent classification results on the ResNet101V2 combination using a mix of transfer learning methods, with the combined model on ResNet101V2 showing an accuracy of up to 97 percent with an F1-score of 0.97, compared to other models. However, this study discovered that the anticipated time required was higher than the model employed in general, which was mitigated by the usage of high-performance computing in this study.
... Bukowy et al 12 used region-based convolutional neural net (R-CNN) and CNN for final classification as glomerulus or background objects, and achieved an average precision and recall of 96.94% and 96.79%, respectively. With the development of deep-learning methods, DeepLab-V2, 17 SegNet, 18 and U-Net 18,19 have been used to deal with segmentation tasks in semantic segmentation. Object detection tasks can detect and classify glomeruli, but the boundary of glomeruli is unknown. ...
... Bukowy et al 12 used region-based convolutional neural net (R-CNN) and CNN for final classification as glomerulus or background objects, and achieved an average precision and recall of 96.94% and 96.79%, respectively. With the development of deep-learning methods, DeepLab-V2, 17 SegNet, 18 and U-Net 18,19 have been used to deal with segmentation tasks in semantic segmentation. Object detection tasks can detect and classify glomeruli, but the boundary of glomeruli is unknown. ...
... An ideal model should be able to identify and segment all types of glomeruli. Most of the existing models analyzing human kidney tissues for glomeruli detection and segmentation are based on a single disease, such as diabetic nephropathy, 16 transplant biopsies, 17 and IgA nephropathy, 19 except in case of Bueno et al, 18 who used 47 wholeslide images (WSIs) from AIDPATH kidney database. ...
Article
Glomeruli instance segmentation from pathologic images is a fundamental step in the automatic analysis of renal biopsies. Glomerular histologic manifestations vary widely among diseases and cases, and several special staining methods are necessary for pathologic diagnosis. A robust model is needed to segment and classify glomeruli with different staining methods and apply in cases with various glomerular pathologic changes. Herein, pathologic images from renal biopsy slides stained with three basic special staining methods were used to build the data sets. The snapshot group included 1970 glomeruli from 516 patients, and the whole-slide image group included 8665 glomeruli from 148 patients. Cascade Mask region-based convolutional neural net architecture was trained to detect, classify, and segment glomeruli into three categories: i) GN, structural normal; ii) global sclerosis; and iii) glomerular with other lesions. In the snapshot group, total glomeruli, GN, global sclerosis, and glomerular with other lesions achieved an F1 score of 0.914, 0.896, 0.681, and 0.756, respectively, which were comparable with those in the whole-slide image group (0.940, 0.839, 0.806, and 0.753, respectively). Among the three categories, GN achieved the best instance segmentation effect in both groups, as determined by average precision, average recall, F1 score, and Mask mean Intersection over Union. The present model segments and classifies multistained glomeruli with efficiency and robustness. It can be applied as the first step for more detailed glomerular histologic analysis.
... Glomerulosclerosis is referred to as nonspecific histological damage partially or entirely affecting the glomerulus, resulting in its very last form in the glomeruli capillary collapse (Jefferson and Shankland, 2014). To quantify glomerulosclerosis, pathologists assessed the percentage of sclerotic glomeruli among all the glomerular structures identified in each renal biopsy (Bueno et al., 2020). Regarding tubules, their pattern of injury is called tubular atrophy, and consist of the thickening of their walls and the reduction of their lumens. ...
... Our approach is able to accurately detect the contours of kidney structures even in the presence of severe atrophy. Our algorithm is validated on 830 PAS stained images and outperforms all the state-ofart methods (Gallego et al., 2018;Kannan et al., 2019;Kawazoe et al., 2018;Bueno et al., 2020;Altini et al., 2020). Implementing this algorithm in pathology departments' daily clinical practice could represent a breakthrough in pre-transplant kidney assessment. ...
... Recently, several automated methods were proposed for quantitative assessment of kidney histological slides (Gallego et al., 2018;Kawazoe et al., 2018;Kannan et al., 2019;Bueno et al., 2020;Altini et al., 2020). Different approaches have been used to detect glomerular structures automatically, but there is still a lack of studies about the detection of tubular structures. ...
Article
In kidney transplantations, pathologists evaluate the architecture of both glomeruli, interstitium and tubules to assess the nephron status. An accurate assessment of glomerulosclerosis and tubular atrophy is crucial for determining kidney acceptance, which is currently based on the pathologists’ histological evaluations on renal biopsies in addition to clinical data. In this work, we present an automated algorithm, called RENTAG (Robust EvaluatioN of Tubular Atrophy & Glomerulosclerosis), for the segmentation and classification of glomerular and tubular structures in histopathological images. The proposed novel strategy combines the accuracy of a level-set with the semantic segmentation of convolutional neural networks to detect the glomeruli and tubules contours. In the TEST set, our method exhibited excellent performance in both glomeruli (dice score: 0.9529) and tubule (dice score: 0.9174) detection and outperformed all the compared methods. To the best of our knowledge, the RENTAG algorithm is the first fully automated method capable of quantifying glomerulosclerosis and tubular atrophy in digital histological images. The developed software can be employed for the analysis of pre-transplantation biopsies to support the pathologists' diagnostic activity.
... Contrary to the numerous works published for glomeruli detection and classification [1,8,9,7], only a few works exist that propose a complete pipeline for classification of WSIs into LN classes using glomeruli features. Additionally, they only consider limited number of glomeruli classes: sclerosed v/s normal [2] or glomerular v/s non-glomerular [5] , which is insufficient for LN classification. the glomeruli classification and kidney-level classification. ...
... Although there are many approaches towards segmenting glomeruli [2,5,1,8,9,7], they use very deep architectures for this task. We formulated the problem of extracting glomeruli as an objectdetection task. ...
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Systemic lupus erythematosus (SLE) is an autoimmune disease in which the immune system of the patient starts attacking healthy tissues of the body. Lupus Nephritis (LN) refers to the inflammation of kidney tissues resulting in renal failure due to these attacks. The International Society of Nephrology/Renal Pathology Society (ISN/RPS) has released a classification system based on various patterns observed during renal injury in SLE. Traditional methods require meticulous pathological assessment of the renal biopsy and are time-consuming. Recently, computational techniques have helped to alleviate this issue by using virtual microscopy or Whole Slide Imaging (WSI). With the use of deep learning and modern computer vision techniques, we propose a pipeline that is able to automate the process of 1) detection of various glomeruli patterns present in these whole slide images and 2) classification of each image using the extracted glomeruli features.
... Several deep learning and machine learning approaches have been used in renal pathology. Convolutional neural networks (CNN) have been applied to WSI for distinguishing sclerosed from non-sclerosed glomeruli 23,24 . Hermsen et al. used deep learning to segment several histologic structures on PAS stained tissue, including glomeruli, proximal and distal tubules, atrophic tubules and blood vessels 25 . ...
... In this study, we constructed a predictive model to classify patients' levels of kidney function with dichotomized eGFR at 60 as well as predicting whether eGFR is increased or decreased in one year. Several studies have shown that artificial intelligence and machine learning methods are useful in solving diagnostic decision-making problems in CKD 23,27,46,47 . Xiao et al. investigated several statistical, machine learning, and neural network approaches for predicting the severity of CKD. ...
Article
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Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging. To discover previously unknown features which predict patient outcomes and overcome substantial interobserver variability, we developed an unsupervised bag-of-words model. Our study applied to the C-PROBE cohort of patients with chronic kidney disease (CKD). 107,471 histopathology images were obtained from 161 biopsy cores and identified important morphological features in biopsy tissue that are highly predictive of the presence of CKD both at the time of biopsy and in one year. To evaluate the performance of our model, we estimated the AUC and its 95% confidence interval. We show that this method is reliable and reproducible and can achieve 0.93 AUC at predicting glomerular filtration rate at the time of biopsy as well as predicting a loss of function at one year. Additionally, with this method, we ranked the identified morphological features according to their importance as diagnostic markers for chronic kidney disease. In this study, we have demonstrated the feasibility of using an unsupervised machine learning method without human input in order to predict the level of kidney function in CKD. The results from our study indicate that the visual dictionary, or visual image pattern, obtained from unsupervised machine learning can predict outcomes using machine-derived values that correspond to both known and unknown clinically relevant features.
... Simon et al [10] used an adaptation of the local binary patterns (LBP) image feature vector to train a SVM model for glomerular recognition. The convolutional neural network (CNN) has also been used to locate glomeruli, classify glomerular hypercellularity lesions, and identify glomerulosclerosis [11][12][13]. Lutnick et al. [14] used a 'human-in-the-loop' to reduce the annotation burden and establish an artificial intelligence pipeline for the segmentation of human and mouse kidney. Ginley et al. [15] achieved the classification of diabetic glomerulosclerosis by using a recurrent neural network (RNN) architecture. ...
... In order to present a mathematical formula for its approach behavior, the simulation of its contraction model can be expressed by Eq. (7) -Eq. (12). ...
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To improve the diagnosis of Lupus Nephritis (LN), in this paper, a multilevel LN image segmentation method is developed based on a swarm intelligence algorithm (SIA). The search of the optimal threshold set is key to multilevel thresholding image segmentation (MLTIS). It is well known that SIAs are more efficient than the traditional poor lift method because of the high complexity in finding the optimal threshold, especially when performing image partitioning at high threshold levels. However, SIAs tend to obtain the poor quality of the found segmentation thresholds and fall into local optima in the process of segmentation. Therefore, by combining an improved slime mould algorithm (ASMA), this paper proposes an ASMA-based MLTIS approach, where ASMA is mainly implemented by introducing the position update mechanism of the artificial bee colony into it. To prove the superiority of the ASMA-based MLTIS method, we first conducted a comparison experiment between ASMA and 11 peers using 30 test functions. The experimental results fully demonstrate that ASMA can obtain high-quality solutions and does not suffer from premature convergence. Moreover, using standard images and LN images, we compared the ASMA-based MLTIS method with other peers and evaluated the segmentation results using three evaluation indicators called PSNR, SSIM, and FSIM. The proposed ASMA was shown to be an excellent swarm intelligence optimization method that can maintain a delicate balance during the segmentation process of LN images, and thus the ASMA-based MLTIS method has great potential to be used as an image segmentation method for LN images.
... As these works achieved superior results in the segmentation of renal structures, we also adopted U-Net as our baseline architecture. In fact, 10 out of 18 works in Table 1 use only U-Net (Jayapandian et al., 2021;Davis et al., 2021;Hermsen et al., 2019;Jha et al., 2021;Bueno et al., 2020), variations of U-Net (Gadermayr et al., 2019;Bouteldja et al., 2021) or combine it with other methods (Mei et al., 2020;Zeng et al., 2020;de Bel et al., 2018). The remaining works explore other DL-based segmentation approaches such as one DL network: Mask-RCNN (Jiang et al., 2021) and DeepLabV2 Ginley et al., 2020); two separate DL networks: MaskRCNN and FastRCNN (Altini et al., 2020a), and SegNet and DeepLabV3+ (Altini et al., 2020b); three separate DL networks: Mask-RCNN, U-Net, and DeepLabV3 (Jha et al., 2021); a combination of two DL networks: SegNet and AlexNet (Bueno et al., 2020); and finally pipelines that combine DL approaches with conventional image processing methods (Marsh et al., 2018;Kannan et al., 2019;Ginley et al., 2019). ...
... In fact, 10 out of 18 works in Table 1 use only U-Net (Jayapandian et al., 2021;Davis et al., 2021;Hermsen et al., 2019;Jha et al., 2021;Bueno et al., 2020), variations of U-Net (Gadermayr et al., 2019;Bouteldja et al., 2021) or combine it with other methods (Mei et al., 2020;Zeng et al., 2020;de Bel et al., 2018). The remaining works explore other DL-based segmentation approaches such as one DL network: Mask-RCNN (Jiang et al., 2021) and DeepLabV2 Ginley et al., 2020); two separate DL networks: MaskRCNN and FastRCNN (Altini et al., 2020a), and SegNet and DeepLabV3+ (Altini et al., 2020b); three separate DL networks: Mask-RCNN, U-Net, and DeepLabV3 (Jha et al., 2021); a combination of two DL networks: SegNet and AlexNet (Bueno et al., 2020); and finally pipelines that combine DL approaches with conventional image processing methods (Marsh et al., 2018;Kannan et al., 2019;Ginley et al., 2019). ...
Article
The growing availability of scanned whole-slide images (WSIs) has allowed nephropathology to open new possibilities for medical decision-making over high-resolution images. Diagnosis of renal WSIs includes locating and identifying specific structures in the tissue. Considering the glomerulus as one of the first structures analyzed by pathologists, we propose here a novel convolutional neural network for glomerulus segmentation. Our end-to-end network, named DS-FNet, combines the strengths of semantic segmentation and semantic boundary detection networks via an attention-aware mechanism. Although we trained the proposed network on periodic acid-Schiff (PAS)-stained WSIs, we found that our network was capable to segment glomeruli on WSIs stained with different techniques, such as periodic acid-methenamine silver (PAMS), hematoxylin-eosin (HE), and Masson trichrome (TRI). To assess the performance of the proposed method, we used three public data sets: HuBMAP (available in a Kaggle competition), a subset of the NEPTUNE data set, and a novel challenging data set, called WSI_Fiocruz. We compared the DS-FNet with six other deep learning networks: original U-Net, our attention version of U-Net called AU-Net, U-Net++, U-Net3Plus, ResU-Net, and DeepLabV3+. Results showed that DS-FNet achieved equivalent or superior results on all data sets: On the HuBMAP data set, it reached a dice score (DSC) of 95.05%, very close to the first place (95.15%); on the NEPTUNE and WSI_Fiocruz data sets, DS-FNet obtained the highest average DSC, whether on PAS-stained images or images stained with other techniques. To the best we know, this is the first work to show consistently high performance in a one-to-many-stain glomerulus segmentation following a thorough protocol on data sets from different medical labs.
... Glomerular phenotyping [17] is a fundamental task for efficient diagnosis and quantitative evaluations in renal pathology. Recently, deep learning techniques have played increasingly important roles in renal pathology to reduce the clinical working load of pathologists and enable large-scale population based research [3,[8][9][10]16]. Due to the lack of publicly available annotated dataset for renal pathology, the related deep learning approaches are still limited on a small-scale [13]. ...
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Unsupervised learning algorithms (e.g., self-supervised learning, auto-encoder, contrastive learning) allow deep learning models to learn effective image representations from large-scale unlabeled data. In medical image analysis, even unannotated data can be difficult to obtain for individual labs. Fortunately, national-level efforts have been made to provide efficient access to obtain biomedical image data from previous scientific publications. For instance, NIH has launched the Open-i search engine that provides a large-scale image database with free access. However, the images in scientific publications consist of a considerable amount of compound figures with subplots. To extract and curate individual subplots, many different compound figure separation approaches have been developed, especially with the recent advances in deep learning. However, previous approaches typically required resource extensive bounding box annotation to train detection models. In this paper, we propose a simple compound figure separation (SimCFS) framework that uses weak classification annotations from individual images. Our technical contribution is three-fold: (1) we introduce a new side loss that is designed for compound figure separation; (2) we introduce an intra-class image augmentation method to simulate hard cases; (3) the proposed framework enables an efficient deployment to new classes of images, without requiring resource extensive bounding box annotations. From the results, the SimCFS achieved a new state-of-the-art performance on the ImageCLEF 2016 Compound Figure Separation Database. The source code of SimCFS is made publicly available at https://github.com/hrlblab/ImageSeperation.
... Kannan et al. 33 used Inception-V3 34 for the sliding window classification of glomeruli with a set of 885 patches from 275 trichrome stained biopsies, and reported MCC=0.63. Bueno et al. 35 trained U-net 6 with 47 PAS stained WSIs, and reported Accuracy=0.98. Gadermayr et al. 36 used 24 PAS-stained murine WSIs to train U-net 6 , repor�ng Precision=0.97 ...
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Background Image-based machine learning tools hold great promise for clinical applications in nephropathology and kidney research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often face prohibitive challenges in using these tools to their full potential, including the lack of technical expertise, suboptimal user interface, and limited computation power. Methods We have developed Histo-Cloud , a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. Results By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in murine models of aging, diabetic nephropathy, and HIV associated nephropathy. Conclusion The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Histo-Cloud is open source and adaptable for segmentation of any histological structure regardless of stain. Histo-Cloud will greatly accelerate and facilitate the generation of datasets for machine learning in the analysis of kidney histology, empowering computationally novice end-users to conduct deep feature analysis of tissue slides.
... The matura�on of convolutional neural networks (CNNs) 3 (a specialized subset of deep learning) for the analysis and segmentation of natural images has led to widespread adoption of this technology in the field of computa�onal pathology. CNNs have shown promising results for state of the art computa�onal pathology image analysis tasks including �ssue segmenta�on [4][5][6][7][8] , disease classifica�on [9][10][11][12][13] , and outcome prediction 14,15 . Training these networks is enhanced by access to diverse WSI datasets, as greater data variability is known to enhance model robustness 16 . ...
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The largest bottleneck to the development of convolutional neural network (CNN) models in the computational pathology domain is the collection and curation of diverse training datasets. Training CNNs requires large cohorts of image data, and model generalizability is dependent on training data heterogeneity. Including data from multiple centers enhances the generalizability of CNN based models, but this is hindered by the logistical challenges of sharing medical data. In this paper we explore the feasibility of training our recently developed cloud-based segmentation tool (Histo-Cloud) using federated learning. We show that a federated trained model to segment interstitial fibrosis and tubular atrophy (IFTA) using datasets from three institutions is comparable to a model trained by pooling the data on one server when tested on a fourth (holdout) institution's data. Further, training a model to segment glomeruli for a federated dataset (split by staining) demonstrates similar performance.
... In the year 2020, there came another deep learning methodology proposed by Bueno et al. (2020). The study focuses on the identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases. ...
Article
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Healthcare is the most important need of today’s era. Healthcare refers to the improvement of the human health by preventing, curing, diagnosing, recovering from a health hazard caused. Thus, to improve the health condition of a human system technology, such as machine learning, deep learning and artificial intelligence has come into play. The combination of artificial technology with the health sector has made a huge impact and success on the world. Curing millions of diseases, analysis of various infections, providing accurate test results and high-level maintenance check are now all possible with the evolution of technology. Every part of human body can now be diagnosed and analyze to study all kinds of tissues, blood vessels, organs, cells for improvement of health and curing of diseases. Research sector has been working with a continuous pace to accomplish various studies to identify different body organs and have a descriptive study for the identification of proper working mechanism of the human body. One such study has also shown a huge progress in the recent times, the identification of glomeruli in human kidney tissue. The tiny ball like structured which is composed of blood vessels that has an actively participation in the filtration of the blood to form urine. Thus, the paper presents a deep learning-based model formed for the identification of these glomeruli present in the human kidney. After implementing, the proposed model obtained an accuracy of 99.68% with a dice coefficient of 0.9060.
... The success of deep convolutional neural networks (CNNs) in recent years has spurred extensive research on their ap-plication in renal pathology, where detection and classification of glomeruli are critical for quantitative evaluation and precise diagnosis. Previously, Gallego et al. [10] used CNNbased classification, while Gloria et al. [11] utilized semantic segmentation, to achieve glomeruli detection in whole slide images. More recently, Yang et al. proposed an anchor-free detection strategy using circle representation [8] that is optimized for round glomeruli and demonstrates superior performance. ...
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Recent studies have demonstrated the diagnostic and prognostic values of global glomerulosclerosis (GGS) in IgA nephropathy, aging, and end-stage renal disease. However, the fine-grained quantitative analysis of multiple GGS subtypes (e.g., obsolescent, solidified, and disappearing glomerulosclerosis) is typically a resource extensive manual process. Very few automatic methods, if any, have been developed to bridge this gap for such analytics. In this paper, we present a holistic pipeline to quantify GGS (with both detection and classification) from a whole slide image in a fully automatic manner. In addition, we conduct the fine-grained classification for the sub-types of GGS. Our study releases the open-source quantitative analytical tool for fine-grained GGS characterization while tackling the technical challenges in unbalanced classification and integrating detection and classification.
... The segmentation of histological tissue structures from whole slide images (WSIs) is often an important first step for further downstream analysis and is therefore well explored in the literature [1][2][3][4][5][6]. The use of convolutional neural networks (CNNs) is currently considered the state of the art, however the generation of annotated training sets for segmentation of WSIs is time consuming and requires domain level expertise [7]. ...
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Segmentation of histology tissue whole side images is an important step for tissue analysis. Given enough annotated training data modern neural networks are capable accurate reproducible segmentation, however, the annotation of training datasets is time consuming. Techniques such as human in the loop annotation attempt to reduce this annotation burden, but still require a large amount of initial annotation. Semi-supervised learning, a technique which leverages both labeled and unlabeled data to learn features has shown promise for easing the burden of annotation. Towards this goal, we employ a recently published semi-supervised method: datasetGAN for the segmentation of glomeruli from renal biopsy images. We compare the performance of models trained using datasetGAN and traditional annotation and show that datasetGAN significantly reduces the amount of annotation required to develop a highly performing segmentation model. We also explore the usefulness of using datasetGAN for transfer learning and find that this greatly enhances the performance when a limited number of whole slide images are used for training.
... If aberrant cell growth is found early on, it may improve the patient's chances of survival [10]. In literature, although much work has been done for the detection of abnormality in blood cells, still there is a gap for improvement to detect the blood cells abnormality more accurately [11][12][13]. This research work accurately segments WBC and classifies its different types using deep features. ...
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White blood cells (WBC) are an important component of the immune mechanism, as they protect the human body from parasites, viruses, fungi, and bacteria. The number of blood cells provides significant information related to infections such as AIDS, leukemia, deficiencies of immune and autoimmune infections. To heal an infection on time, it is critical to recognize it early on. Therefore, a method is proposed to accurately segment and classify WBC at an early stage. The RGB image is converted into HSV after which dual thresholding is applied to the saturation component to segment WBC. The 1000 features are extracted from Alexnet to FC8 layer, the Logits layer is selected for feature extraction from mobilenetv2, the node_202 layer is utilized to extract the features from the shuffle net, and the FC1000 layer is chosen from the Resnet-18 model. Four feature vectors are obtained from transfer learning models; these feature vectors are combined serially and create the final optimized vector by a non-dominated sorting genetic algorithm (NSGA). The classification results are investigated on the fusion of Alexnet, shuffle net, Resnet-18, mobilenetv2, and the fusion of mobilenetv2, shuffle net, and Resnet-18 whereas mobilenetv2 features are fused independently. The final optimized feature vector is passed to classifiers including Naïve Bayes (NB), Decision tree (DT), Ensemble, Linear Discriminant Analysis (LDA), Support vector machine (SVM), and K nearest neighbor (KNN) to classify WBC. The method is tested on three publicly available datasets as LISC, ALL_IDB1, and ALL_IDB2. The method achieved the maximum of 1.00 accuracy to classify the blast/non-blast cells, 0.9992 accuracy on Basophil cells, and 1.00 accuracy on Lymphocyte, Neutrophil, Monocyte, Eosinophil, and mixture of these cells. When compared to existing modern approaches, the proposed method produces better outcomes.
... Moreover, fibrotic glomeruli can be confused easily with severely disordered glomeruli, and sclerotic glomeruli are more comparable to non-glomerular tissues, so the recall rate and accuracy are not high. There are many approaches for renal image segmentation to obtain the most detailed image quantification [18,24,35,36]. This pixel-level quantification provides precise spatial and quantitative measurements of objects at different scales. ...
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Accurate assessment of renal histopathology is crucial for the clinical management of patients with lupus nephritis (LN). However, the current classification system has poor interpathologist agreement. This paper proposes a deep convolutional neural network (CNN)-based system that detects and classifies glomerular pathological findings in LN. A dataset of 349 renal biopsy whole-slide images (WSIs) (163 patients with LN, periodic acid-Schiff stain, 3906 glomeruli) annotated by three expert nephropathologists was used. The CNN models YOLOv4 and VGG16 were employed to localise the glomeruli and classify glomerular lesions (slight/severe impairments or sclerotic lesions). An additional 321 unannotated WSIs from 161 patients were used for performance evaluation at the per-patient kidney level. The proposed model achieved an accuracy of 0.951 and Cohen’s kappa of 0.932 (95% CI 0.915–0.949) for the entire test set for classifying the glomerular lesions. For multiclass detection at the glomerular level, the mean average precision of the CNN was 0.807, with ‘slight’ and ‘severe’ glomerular lesions being easily identified (F1: 0.924 and 0.952, respectively). At the per-patient kidney level, the model achieved a high agreement with nephropathologist (linear weighted kappa: 0.855, 95% CI: 0.795–0.916, p < 0.001; quadratic weighted kappa: 0.906, 95% CI: 0.873–0.938, p < 0.001). The results suggest that deep learning is a feasible assistive tool for the objective and automatic assessment of pathological LN lesions.
... Moreover, Coudray confirmed that the deep learning model could predict six genetic mutations associated with cancer and assist doctors to detect the subtype and gene mutation in cancer diagnosis, with a high accuracy of 97%; the model code is published online [26] . Furthermore, AI has been used in the diagnosis of epithelial tumors, lung cancer, basal cell carcinoma, and glomerulosclerosis [27][28][29][30][31] . These developments emphasize the practicability of AI technology applied in pathology. ...
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Artificial intelligence (AI) is a new technical discipline that uses computer technology to research and develop the theory, method, technique, and application system for the simulation, extension, and expansion of human intelligence. With the assistance of new AI technology, the traditional medical environment has changed a lot. For example, a patient’s diagnosis based on radiological, pathological, endoscopic, ultrasonographic, and biochemical examinations has been effectively promoted with a higher accuracy and a lower human workload. The medical treatments during the perioperative period, including the preoperative preparation, surgical period, and postoperative recovery period, have been significantly enhanced with better surgical effects. In addition, AI technology has also played a crucial role in medical drug production, medical management, and medical education, taking them into a new direction. The purpose of this review is to introduce the application of AI in medicine and to provide an outlook of future trends.
... The CAMELYON challenge provided a stimulus for researchers and industry to focus on the actual impact of CPATH applications in pathology clinical practice. Current applications include tumor detection and classification (often by subtype 23,25-39 ), image segmentation [40][41][42][43][44][45][46][47][48][49][50] , cell detection and counting 51-55 , mitosis detection [56][57][58][59][60] , analysis of kidney transplant biopsies 20 and tumor grading 61-63 among others. An example of a CPATH application for automatic tissue segmentation using a combination of U-Net models 20 , as well as the corresponding ground truth, is shown in Fig. 1. Figure 1a shows a zoomed-in region of a periodic acid-Schiff-stained kidney biopsy, in which glomeruli, tubuli, capillaries and so on can be recognized. ...
... Each image was color normalized using a modified version of Reinhard's method for color normalization, which decreases color variability in WSIs. [7][8][9] Glomerular Structure Segmentation Sub-glomerular structures were automatically segmented for feature extraction and analysis. ...
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Pathologists use multiple microscopy modalities to assess renal biopsy specimens. Besides usual diagnostic features, some changes are too subtle to be properly defined. Computational approaches have the potential to systematically quantitate subvisual clues, provide pathogenetic insight, and link to clinical outcomes. To this end, a proof-of-principle study is presented demonstrating that explainable biomarkers through machine learning can distinguish between glomerular disorders at the light-microscopy level. The proposed system used image analysis techniques and extracted 233 explainable biomarkers related to color, morphology, and microstructural texture. Traditional machine learning was then used to classify minimal change disease (MCD), membranous nephropathy (MN), and thin basement membrane nephropathy (TBMN) diseases on a glomerular and patient-level basis. The final model combined the Gini feature importance set and linear discriminant analysis classifier. Six morphologic (nuclei-to-glomerular tuft area, nuclei-to-glomerular area, glomerular tuft thickness greater than ten, glomerular tuft thickness greater than three, total glomerular tuft thickness, and glomerular circularity) and four microstructural texture features (luminal contrast using wavelets, nuclei energy using wavelets, nuclei variance using color vector LBP, and glomerular correlation using GLCM) were, together, the best performing biomarkers. Accuracies of 77% and 87% were obtained for glomerular and patient-level classification, respectively. Computational methods, using explainable glomerular biomarkers, have diagnostic value and are compatible with our existing knowledge of disease pathogenesis. Furthermore, this algorithm can be applied to clinical datasets for novel prognostic and mechanistic biomarker discovery.
... 21 The Ivy GAP data constituted a significant advance in the field and has allowed the identification of signatures associated with different tumour regions. 21 However, it has limitations, as the results were highly dependent on the segmentation algorithms that were implemented for the segmentation of the different tumour regions [22][23][24][25] and inherently biased due to the small number (n = 32) of patient samples analysed. 21 Furthermore, although multiple sections/regions were processed for data augmentation and the creation of the Ivy GAP database, the small number of patients recruited to the project did not permit the identification of genetic signatures or key demographic characteristics associated with survival and/or particular molecular profiles of the tumours. ...
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Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking. We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions. We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival. This work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub: https://github.com/amin20/GBM_WSSM.
... Bukowy et al. [2] constructed Faster R-CNN with modified AlexNet model to detect glomeruli in patches. Previous works solve the problem of high-resolution processing through slicing each WSI into many small patches by sliding window method and then patch-wise searching the location of glomeruli [1,2,15,16]. In this way, nevertheless, it also takes a long time in processing a high-resolution WSI for invoking the locator frequently. ...
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In the diagnosis of chronic kidney disease, glomerulus as the blood filter provides important information for an accurate disease diagnosis. Thus automatic localization of the glomeruli is the necessary groundwork for future auxiliary kidney disease diagnosis, such as glomerular classification and area measurement. In this paper, we propose an efficient glomerular object locator in kidney whole slide image(WSI) based on proposal-free network and dynamic scale evaluation method. In the training phase, we construct an intensive proposal-free network which can learn efficiently the fine-grained features of the glomerulus. In the evaluation phase, a dynamic scale evaluation method is utilized to help the well-trained model find the most appropriate evaluation scale for each high-resolution WSI. We collect and digitalize 1204 renal biopsy microscope slides containing more than 41000 annotated glomeruli, which is the largest number of dataset to our best knowledge. We validate the each component of the proposed locator via the ablation study. Experimental results confirm that the proposed locator outperforms recently proposed approaches and pathologists by comparing F 1 and run time in localizing glomeruli from WSIs at a resolution of 0.25 μm/pixel and thus achieves state-of-the-art performance. Particularly, the proposed locator can be embedded into the renal intelligent auxiliary diagnosis system for renal clinical diagnosis by localizing glomeruli in high-resolution WSIs effectively.
... Chen et al. performed a large comprehensive study on a big annotated image dataset to evaluate the feasibility of U-Net for segmentation of 6 renal histologic primitives on 4 different stains [17]. Bueno et al. used CNN-based semantic segmentation and classification CNN to realize the detection of glomeruli and the classification of normal and sclerosed glomeruli for LM images, respectively [18]. ...
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The chronic kidney disease (CKD) accompanied by permanent kidney damage, has become a heavy burden for worldwide public health. Clinically, glomerular immunofluorescence (IF) images are widely-used to reveal the occurrence probability and type of CKD. In histopathological assessment for glomerular IF image, multiple descriptive indicators are used to characterize deposits from different aspects, which suggest associated kidney lesions. In this paper, we design a hierarchical feature fusion attention network (HFANet) to classify two main descriptive indicators, namely fluorescence intensity and distribution pattern. Through the hierarchical feature fusion attention (HFA) module, HFANet supplements deep semantic features using shallow texture features to maximize its feature extraction capability and efficiency of information fusion. Different from directly adding or concatenating, HFANet weighted concatenates the feature maps from different hierarchies to highlight more discriminative regions. Further, by integrating HFANet with the proposed intensity equalization (IE) algorithm, U-Net++, and Grad-CAM, a computer-aided diagnostic system for glomerular IF images is constructed. With this system, the classification accuracy of the fluorescence intensity and distribution pattern reaches 90.48% and 90.87%, respectively. Extensive comparative experiments and ablation studies demonstrate that HFANet outperforms other universal backbones with the help of HFA module, and the classification performance of the devised system is comparable to senior pathologists. The heatmap given by the system, which is similar to the classification evidence used by the clinicians, can be used as diagnostic reference and training material for pathologists. The systematic demonstration video is available in the supplementary material.
... Glomerular phenotyping (Koziell et al., 2002) is a fundamental task for efficient diagnosis and quantitative evaluations in renal pathology. Recently, deep learning techniques have played increasingly important roles in renal pathology to reduce clinical working load of pathologists and enable large-scale population based research (Gadermayr Bueno et al., 2020;Govind et al., 2018;Kannan et al., 2019;Ginley et al., 2019). Due to the lack of a publicly available dataset for renal pathology, it is appealing to extract large-scale glomerular images from public databases (e.g., NIH Open-i search engine) for downstream self-supervised or semi-supervised learning (Huo et al., 2021). ...
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With the rapid development of self-supervised learning (e.g., contrastive learning), the importance of having large-scale images (even without annotations) for training a more generalizable AI model has been widely recognized in medical image analysis. However, collecting large-scale task-specific unannotated data at scale can be challenging for individual labs. Existing online resources, such as digital books, publications, and search engines, provide a new resource for obtaining large-scale images. However, published images in healthcare (e.g., radiology and pathology) consist of a considerable amount of compound figures with subplots. In order to extract and separate compound figures into usable individual images for downstream learning, we propose a simple compound figure separation (SimCFS) framework without using the traditionally required detection bounding box annotations, with a new loss function and a hard case simulation. Our technical contribution is four-fold: (1) we introduce a simulation-based training framework that minimizes the need for resource extensive bounding box annotations; (2) we propose a new side loss that is optimized for compound figure separation; (3) we propose an intra-class image augmentation method to simulate hard cases; and (4) to the best of our knowledge, this is the first study that evaluates the efficacy of leveraging self-supervised learning with compound image separation. From the results, the proposed SimCFS achieved state-of-the-art performance on the ImageCLEF 2016 Compound Figure Separation Database. The pretrained self-supervised learning model using large-scale mined figures improved the accuracy of downstream image classification tasks with a contrastive learning algorithm. The source code of SimCFS is made publicly available at https://github.com/hrlblab/ImageSeperation.
... The experimental results with the highest accuracy were obtained on the most challenging datasets. Bueno et al. [12] achieved higher accuracy by using the sequential CNN segmentation-classification strategy. As a new training framework, the extreme learning machine (ELM) has been widely used in classification, identification, and diagnosis [13]. ...
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Since manual hemolysis test methods are given priority with practical experience and its cost is high, the characteristics of hemolysis images are studied. A hemolysis image detection method based on generative adversarial networks (GANs) and convolutional neural networks (CNNs) with extreme learning machine (ELM) is proposed. First, the image enhancement and data enhancement are performed on a sample set, and GAN is used to expand the sample data volume. Second, CNN is used to extract the feature vectors of the processed images and label eigenvectors with one-hot encoding. Third, the feature matrix is input to the map in the ELM network to minimize the error and obtain the optimal weight by training. Finally, the image to be detected is input to the trained model, and the image with the greatest probability is selected as the final category. Through model comparison experiments, the results show that the hemolysis image detection method based on the GAN-CNN-ELM model is better than GAN-CNN, GAN-ELM, GAN-ELM-L1, GAN-SVM, GAN-CNN-SVM, and CNN-ELM in accuracy and speed, and the accuracy rate is 98.91%.
... Animal model-derived samples were deployed in several studies, most of which dealt with glomerular detection [25][26][27][28][29][30][31][32][33], or unspecified human kidney biopsies with no reference to the pre-implantation setting or specific pathology [34][35][36][37][38]. Only three larger-sized studies applied segmentation CNN models to the detection and simultaneous classification of multiple renal structures, not only glomeruli but also different kinds of tubuli and vessels [39][40][41]. ...
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Background Transplant nephropathology is a highly specialized field of pathology comprising both the evaluation of organ donor biopsy for organ allocation and post-transplant graft biopsy for assessment of rejection or graft damage. The introduction of digital pathology with whole-slide imaging (WSI) in clinical research, trials and practice has catalyzed the application of artificial intelligence (AI) for histopathology, with development of novel machine-learning models for tissue interrogation and discovery. We aimed to review the literature for studies specifically applying AI algorithms to WSI-digitized pre-implantation kidney biopsy. Methods A systematic search was carried out in the electronic databases PubMed-MEDLINE and Embase until 25th September, 2021 with a combination of the key terms “kidney”, “biopsy”, “transplantation” and “artificial intelligence” and their aliases. Studies dealing with the application of AI algorithms coupled with WSI in pre-implantation kidney biopsies were included. The main theme addressed was detection and quantification of tissue components. Extracted data were: author, year and country of the study, type of biopsy features investigated, number of cases, type of algorithm deployed, main results of the study in terms of diagnostic outcome, and the main limitations of the study. Results Of 5761 retrieved articles, 7 met our inclusion criteria. All studies focused largely on AI-based detection and classification of glomerular structures and to a lesser extent on tubular and vascular structures. Performance of AI algorithms was excellent and promising. Conclusion All studies highlighted the importance of expert pathologist annotation to reliably train models and the need to acknowledge clinical nuances of the pre-implantation setting. Close cooperation between computer scientists and practicing as well as expert renal pathologists is needed, helping to refine the performance of AI-based models for routine pre-implantation kidney biopsy clinical practice.
... Specifically, convolutional neural networks (CNNs), which are the most popular deep learning-based techniques, are mainly used for the automated detection and morphometric analysis of histological components and in the prediction of renal disease prognosis. The applications of CNNs in renal pathology include glomerular counting [4][5][6][7][8], global glomerulosclerosis [9][10][11][12][13][14], podocyte morphometric analysis [14][15][16][17], the classification of diabetic glomerulosclerosis [18], IgA nephropathy [19,20], glomerular hypercellularity [21], several glomerular changes [22], kidney transplant pathology [23][24][25], interstitial fibrosis and tubular atrophy [10,11,14,[26][27][28], vascular detection [28], immunofluorescence staining patterns [29], and the classification of normal and abnormal structures in the renal cortex [4,[30][31][32] (Table 1). However, studies on the development of CNNs that can be successfully applied in the classification of normal and abnormal renal tubules [4,5,11,30], which remains a challenging domain even among renal pathologists, are scarce. ...
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Renal pathology is essential for diagnosing and assessing the severity and prognosis of kidney diseases. Deep learning-based approaches have developed rapidly and have been applied in renal pathology. However, methods for the automated classification of normal and abnormal renal tubules remain scarce. Using a deep learning-based method, we aimed to classify normal and abnormal renal tubules, thereby assisting renal pathologists in the evaluation of renal biopsy specimens. Consequently, we developed a U-Net-based segmentation model using randomly selected regions obtained from 21 renal biopsy specimens. Further, we verified its performance in multiclass segmentation by calculating the Dice coefficients (DCs). We used 15 cases of tubulointerstitial nephritis to assess its applicability in aiding routine diagnoses conducted by renal pathologists and calculated the agreement ratio between diagnoses conducted by two renal pathologists and the time taken for evaluation. We also determined whether such diagnoses were improved when the output of segmentation was considered. The glomeruli and interstitium had the highest DCs, whereas the normal and abnormal renal tubules had intermediate DCs. Following the detailed evaluation of the tubulointerstitial compartments, the proximal, distal, atrophied, and degenerated tubules had intermediate DCs, whereas the arteries and inflamed tubules had low DCs. The annotation and output areas involving normal and abnormal tubules were strongly correlated in each class. The pathological concordance for the glomerular count, t, ct, and ci scores of the Banff classification of renal allograft pathology remained high with or without the segmented images. However, in terms of time consumption, the quantitative assessment of tubulitis, tubular atrophy, degenerated tubules, and the interstitium was improved significantly when renal pathologists considered the segmentation output. Deep learning algorithms can assist renal pathologists in the classification of normal and abnormal tubules in renal biopsy specimens, thereby facilitating the enhancement of renal pathology and ensuring appropriate clinical decisions.
... A Convolutional Neural Network (CNN) was employed and trained on a small in-house collected dataset, composed of only 26 kidney biopsies. In [18], instead, a two-step deep learning approach was realized, segmenting and then classifying the segmented structures, using an AlexNetlike architecture, tested on 47 Whole Slide Images (WSIs). Similarly, in [19], some different staining modalities were compared in the framework of a classification model to predict diseased and non-diseased kidney tissues. ...
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Deep learning is widely applied in bioinformatics and biomedical imaging, due to its ability to perform various clinical tasks automatically and accurately. In particular, the application of deep learning techniques for the automatic identification of glomeruli in histopathological kidney images can play a fundamental role, offering a valid decision support system tool for the automatic evaluation of the Karpinski metric. This will help clinicians in detecting the presence of sclerotic glomeruli in order to decide whether the kidney is transplantable or not. In this work, we implemented a deep learning framework to identify and segment sclerotic and non-sclerotic glomeruli from scanned Whole Slide Images (WSIs) of human kidney biopsies. The experiments were conducted on a new dataset collected by both the Siena and Trieste hospitals. The images were segmented using the DeepLab V2 model, with a pre-trained ResNet101 encoder, applied to 512 × 512 patches extracted from the original WSIs. The results obtained are promising and show a good performance in the segmentation task and a good generalization capacity, despite the different coloring and typology of the histopathological images. Moreover, we present a novel use of the CD10 staining procedure, which gives promising results when applied to the segmentation of sclerotic glomeruli in kidney tissues.
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Purpose: Recent studies have demonstrated the diagnostic and prognostic values of global glomerulosclerosis (GGS) in IgA nephropathy, aging, and end-stage renal disease. However, the fine-grained quantitative analysis of multiple GGS subtypes (e.g., obsolescent, solidified, and disappearing glomerulosclerosis) is typically a resource extensive manual process. Very few automatic methods, if any, have been developed to bridge this gap for such analytics. We present a holistic pipeline to quantify GGS (with both detection and classification) from a whole slide image in a fully automatic manner. In addition, we conduct the fine-grained classification for the subtypes of GGS. Our study releases the open-source quantitative analytical tool for fine-grained GGS characterization while tackling the technical challenges in unbalanced classification and integrating detection and classification. Approach: We present a deep learning-based framework to perform fine-grained detection and classification of GGS, with a hierarchical two-stage design. Moreover, we incorporate the state-of-the-art transfer learning techniques to achieve a more generalizable deep learning model for tackling the imbalanced distribution of our dataset. This way, we build a highly efficient WSI-to-results GGS characterization pipeline. Meanwhile, we investigated the largest fine-grained GGS cohort as of yet with 11,462 glomeruli and 10,619 nonglomeruli, which include 7841 globally sclerotic glomeruli of three distinct categories. With these data, we apply deep learning techniques to achieve (1) fine-grained GGS characterization, (2) GGS versus non-GGS classification, and (3) improved glomeruli detection results. Results: For fine-grained GGS characterization, when pretrained on the larger dataset, our model can achieve a 0.778-macro- F 1 score, compared to a 0.746-macro- F 1 score when using the regular ImageNet-pretrained weights. On the external dataset, our best model achieves an area under the curve (AUC) score of 0.994 when tasked with differentiating GGS from normal glomeruli. Using our dataset, we are able to build algorithms that allow for fine-grained classification of glomeruli lesions and are robust to distribution shifts. Conclusion: Our study showed that the proposed methods consistently improve the detection and fine-grained classification performance through both cross validation and external validation. Our code and pretrained models have been released for public use at https://github.com/luyuzhe111/glomeruli.
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The quantitative detection, segmentation, and characterization of glomeruli from high-resolution whole slide imaging (WSI) play essential roles in the computer-assisted diagnosis and scientific research in digital renal pathology. Historically, such comprehensive quantification requires extensive programming skills in order to be able to handle heterogeneous and customized computational tools. To bridge the gap of performing glomerular quantification for non-technical users, we develop the Glo-In-One toolkit to achieve holistic glomerular detection, segmentation, and characterization via a single line of command. Additionally, we release a large-scale collection of 30,000 unlabeled glomerular images to further facilitate the algorithmic development of self-supervised deep learning. The inputs of the Glo-In-One toolkit are WSIs, while the outputs are (1) WSI-level multi-class circle glomerular detection results (which can be directly manipulated with ImageScope), (2) glomerular image patches with segmentation masks, and (3) different lesion types. To leverage the performance of the Glo-In-One toolkit, we introduce self-supervised deep learning to glomerular quantification via large-scale web image mining. The GGS fine-grained classification model achieved a decent performance compared with baseline supervised methods while only using 10% of the annotated data. The glomerular detection achieved an average precision of 0.627 with circle representations, while the glomerular segmentation achieved a 0.955 patch-wise Dice Similarity Coefficient (DSC).
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Digital imaging and advanced microscopy play a pivotal role in the diagnosis of kidney diseases. In recent years, great achievements have been made in digital imaging, providing novel approaches for precise quantitative assessments of nephropathology and relieving burdens of renal pathologists. Developing novel methods of artificial intelligence (AI)-assisted technology through multidisciplinary interaction among computer engineers, renal specialists, and nephropathologists could prove beneficial for renal pathology diagnoses. An increasing number of publications has demonstrated the rapid growth of AI-based technology in nephrology. In this review, we offer an overview of AI-assisted renal pathology, including AI concepts and the workflow of processing digital image data, focusing on the impressive advances of AI application in disease-specific backgrounds. In particular, this review describes the applied computer vision algorithms for the segmentation of kidney structures, diagnosis of specific pathological changes, and prognosis prediction based on images. Lastly, we discuss challenges and prospects to provide an objective view of this topic.
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Background: Image-based machine learning tools hold great promise for clinical applications in pathology research. However, the ideal end-users of these computational tools (e.g., pathologists and biological scientists) often lack the programming experience required for the setup and use of these tools which often rely on the use of command line interfaces. Methods: We have developed Histo-Cloud, a tool for segmentation of whole slide images (WSIs) that has an easy-to-use graphical user interface. This tool runs a state-of-the-art convolutional neural network (CNN) for segmentation of WSIs in the cloud and allows the extraction of features from segmented regions for further analysis. Results: By segmenting glomeruli, interstitial fibrosis and tubular atrophy, and vascular structures from renal and non-renal WSIs, we demonstrate the scalability, best practices for transfer learning, and effects of dataset variability. Finally, we demonstrate an application for animal model research, analyzing glomerular features in three murine models. Conclusions: Histo-Cloud is open source, accessible over the internet, and adaptable for segmentation of any histological structure regardless of stain.
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Machine learning techniques have great potential to improve medical diagnostics, offering ways to improve accuracy, reproducibility and speed, and to ease workloads for clinicians. In the field of histopathology, deep learning algorithms have been developed that perform similarly to trained pathologists for tasks such as tumor detection and grading. However, despite these promising results, very few algorithms have reached clinical implementation, challenging the balance between hope and hype for these new techniques. This Review provides an overview of the current state of the field, as well as describing the challenges that still need to be addressed before artificial intelligence in histopathology can achieve clinical value. Recent advances in machine learning techniques have created opportunities to improve medical diagnostics, but implementing these advances in the clinic will not be without challenge.
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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.
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In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor-intensive. The goal of this study was to investigate the potential of convolutional neural networks (CNNs) to quantify inflammation and chronic features in kidney transplant biopsies. A structure segmentation CNN and a lymphocyte detection CNN were applied on 125 whole-slide image pairs of PAS-, and CD3-stained slides. The CNN results were used to quantify healthy and sclerotic glomeruli, interstitial fibrosis, tubular atrophy, and inflammation both within non-atrophic and atrophic tubuli, and in areas of interstitial fibrosis. The computed tissue features showed high correlations with Banff lesion scores of five pathologists. Analyses on a small subset showed a moderate correlation towards higher CD3⁺ cell density within scarred regions and higher CD3⁺ cell count inside atrophic tubuli correlated with long-term change of estimated glomerular filtration rate. The presented CNNs are valid tools to yield objective quantitative information on glomeruli number, fibrotic tissue, and inflammation within scarred and non-scarred kidney parenchyma in a reproducible fashion. CNNs have the potential to improve kidney transplant diagnostics and will benefit the community as a novel method to generate surrogate endpoints for large-scale clinical studies.
Article
Purpose: The quantitative detection, segmentation, and characterization of glomeruli from high-resolution whole slide imaging (WSI) play essential roles in the computer-assisted diagnosis and scientific research in digital renal pathology. Historically, such comprehensive quantification requires extensive programming skills to be able to handle heterogeneous and customized computational tools. To bridge the gap of performing glomerular quantification for non-technical users, we develop the Glo-In-One toolkit to achieve holistic glomerular detection, segmentation, and characterization via a single line of command. Additionally, we release a large-scale collection of 30,000 unlabeled glomerular images to further facilitate the algorithmic development of self-supervised deep learning. Approach: The inputs of the Glo-In-One toolkit are WSIs, while the outputs are (1) WSI-level multi-class circle glomerular detection results (which can be directly manipulated with ImageScope), (2) glomerular image patches with segmentation masks, and (3) different lesion types. In the current version, the fine-grained global glomerulosclerosis (GGS) characterization is provided, including assessed-solidified-GSS (associated with hypertension-related injury), disappearing-GSS (a further end result of the SGGS becoming contiguous with fibrotic interstitium), and obsolescent-GSS (nonspecific GGS increasing with aging) glomeruli. To leverage the performance of the Glo-In-One toolkit, we introduce self-supervised deep learning to glomerular quantification via large-scale web image mining. Results: The GGS fine-grained classification model achieved a decent performance compared with baseline supervised methods while only using 10% of the annotated data. The glomerular detection achieved an average precision of 0.627 with circle representations, while the glomerular segmentation achieved a 0.955 patch-wise Dice dimilarity coefficient. Conclusion: We develop and release an open-source Glo-In-One toolkit, a software with holistic glomerular detection, segmentation, and lesion characterization. This toolkit is user-friendly to non-technical users via a single line of command. The toolbox and the 30,000 web mined glomerular images have been made publicly available at https://github.com/hrlblab/Glo-In-One.
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Purpose of review: We seek to determine recent advances in kidney pathophysiology that have been enabled or enhanced by artificial intelligence. We describe some of the challenges in the field as well as future directions. Recent findings: We first provide an overview of artificial intelligence terminologies and methodologies. We then describe the use of artificial intelligence in kidney diseases to discover risk factors from clinical data for disease progression, annotate whole slide imaging and decipher multiomics data. We delineate key examples of risk stratification and prognostication in acute kidney injury (AKI) and chronic kidney disease (CKD). We contextualize these applications in kidney disease oncology, one of the subfields to benefit demonstrably from artificial intelligence using all if these approaches. We conclude by elucidating technical challenges and ethical considerations and briefly considering future directions. Summary: The integration of clinical data, patient derived data, histology and proteomics and genomics can enhance the work of clinicians in providing more accurate diagnoses and elevating understanding of disease progression. Implementation research needs to be performed to translate these algorithms to the clinical setting.
Chapter
Unsupervised learning algorithms (e.g., self-supervised learning, auto-encoder, contrastive learning) allow deep learning models to learn effective image representations from large-scale unlabeled data. In medical image analysis, even unannotated data can be difficult to obtain for individual labs. Fortunately, national-level efforts have been made to provide efficient access to obtain biomedical image data from previous scientific publications. For instance, NIH has launched the Open-i\(^\circledR \) search engine that provides a large-scale image database with free access. However, the images in scientific publications consist of a considerable amount of compound figures with subplots. To extract and curate individual subplots, many different compound figure separation approaches have been developed, especially with the recent advances in deep learning. However, previous approaches typically required resource extensive bounding box annotation to train detection models. In this paper, we propose a simple compound figure separation (SimCFS) framework that uses weak classification annotations from individual images. Our technical contribution is three-fold: (1) we introduce a new side loss that is designed for compound figure separation; (2) we introduce an intra-class image augmentation method to simulate hard cases; (3) the proposed framework enables an efficient deployment to new classes of images, without requiring resource extensive bounding box annotations. From the results, the SimCFS achieved a new state-of-the-art performance on the ImageCLEF 2016 Compound Figure Separation Database. The source code of SimCFS is made publicly available at https://github.com/hrlblab/ImageSeperation.
Article
Background Classification of glomerular diseases and identification of glomerular lesions require careful morphological examination by experienced nephropathologists, which is labor-intensive, time-consuming, and prone to interobserver variability. In this regard, recent advance in machine learning-based image analysis is promising. Methods We combined Mask Region-based Convolutional Neural Networks (Mask R-CNN) with an additional classification step to build a glomerulus detection model using human kidney biopsy samples. A Long Short-Term Memory (LSTM) recurrent neural network was applied for glomerular disease classification, and another two-stage model using ResNeXt-101 was constructed for glomerular lesion identification in cases of lupus nephritis. Results The detection model showed state-of-the-art performance on variedly stained slides with F1 scores up to 0.944. The disease classification model showed good accuracies up to 0.940 on recognizing different glomerular diseases based on H&E whole slide images. The lesion identification model demonstrated high discriminating power with area under the receiver operating characteristic curve up to 0.947 for various glomerular lesions. Models showed good generalization on external testing datasets. Conclusion This study is the first-of-its-kind showing how each step of kidney biopsy interpretation carried out by nephropathologists can be captured and simulated by machine learning models. The models were integrated into a whole slide image viewing and annotating platform to enable nephropathologists to review, correct, and confirm the inference results. Further improvement on model performances and incorporating inputs from immunofluorescence, electron microscopy, and clinical data might realize actual clinical use.
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Quantification and classification of tissue features such as glomerulus are important elements of the histopathologic assessment of renal tissue. To fulfill this task, glomerulus segmentation is required. In this paper, we propose a multi-stream glomerulus segmentation framework based on three signature models: FCN, Deeplabv3 and Unet. Resnet is combined with FCN and Deeplabv3 model to enhance the encoding process. Meanwhile, Unet is upgraded to use EfficientNet as the backbone for feature extraction. Each individual model will output a local decision. On top of these base learners, ensemble approaches are proposed for robust performance through prediction aggregation. Among all the ensemble methods, the Bayesian voting method performs best and achieves F-score of 91.5%
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Background and objective: The effective application of deep learning to digital histopathology is hampered by the shortage of high-quality annotated images. In this paper we focus on the supervised segmentation of glomerular structures in patches of whole slide images of renal histopathological slides. Considering a U-Net model employed for segmentation, our goal is to evaluate the impact of augmenting training data with random spatial deformations. Methods: The effective application of deep learning to digital histopathology is hampered by the shortage of high-quality annotated images. In this paper we focus on the supervised segmentation of glomerular structures in patches of whole slide images of renal histopathological slides. Considering a U-Net model employed for segmentation, our goal is to evaluate the impact of augmenting training data with random spatial deformations. Results: We show that augmenting training data with spatially deformed images yields an improvement of up to 0.23 in average Dice score, with respect to training with no augmentation. We demonstrate that deformations with relatively strong distortions yield the best performance increase, while previous work only report the use of deformations with low distortions. The selected deformation models yield similar performance increase, provided that their parameters are properly adjusted. We provide bounds on the optimal parameter values, obtained through parameter sampling, which is achieved in a lower computational complexity with our single-parameter method. The paper is accompanied by a framework for evaluating the impact of random spatial deformations on the performance of any U-Net segmentation model. Conclusion: To our knowledge, this study is the first to evaluate the impact of random spatial deformations on the segmentation of histopathological images. Our study and framework provide tools to help practitioners and researchers to make a better usage of random spatial deformations when training deep models for segmentation.
Preprint
Box representation has been extensively used for object detection in computer vision. Such representation is efficacious but not necessarily optimized for biomedical objects (e.g., glomeruli), which play an essential role in renal pathology. In this paper, we propose a simple circle representation for medical object detection and introduce CircleNet, an anchor-free detection framework. Compared with the conventional bounding box representation, the proposed bounding circle representation innovates in three-fold: (1) it is optimized for ball-shaped biomedical objects; (2) The circle representation reduced the degree of freedom compared with box representation; (3) It is naturally more rotation invariant. When detecting glomeruli and nuclei on pathological images, the proposed circle representation achieved superior detection performance and be more rotation-invariant, compared with the bounding box. The code has been made publicly available: https://github.com/hrlblab/CircleNet
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The largest bottleneck to the development of convolutional neural network (CNN) models in the computational pathology domain is the collection and curation of diverse training datasets. Training CNNs requires large cohorts of image data, and model generalizability is dependent on training data heterogeneity. Including data from multiple centers enhances the generalizability of CNN based models, but this is hindered by the logistical challenges of sharing medical data. In this paper we explore the feasibility of training our recently developed cloud-based segmentation tool (Histo-Cloud) using federated learning. Using a dataset of renal tissue biopsies we show that federated training to segment interstitial fibrosis and tubular atrophy (IFTA) using datasets from three institutions is not found to be different from a training by pooling the data on one server when tested on a fourth (holdout) institution’s data. Further, training a model to segment glomeruli for a federated dataset (split by staining) demonstrates similar performance.
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Box representation has been extensively used for object detection in computer vision. Such representation is efficacious but not necessarily optimized for biomedical objects (e.g., glomeruli), which play an essential role in renal pathology. In this paper, we propose a simple circle representation for medical object detection and introduce CircleNet, an anchor-free detection framework. Compared with the conventional bounding box representation, the proposed bounding circle representation innovates in three-fold: (1) it is optimized for ball-shaped biomedical objects; (2) The circle representation reduced the degree of freedom compared with box representation; (3) It is naturally more rotation invariant. When detecting glomeruli and nuclei on pathological images, the proposed circle representation achieved superior detection performance and be more rotation-invariant, compared with the bounding box. The code has been made publicly available: https://github.com/hrlblab/CircleNet.
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Rationale & Objective The current classification system for focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) does not fully capture the complex structural changes in kidney biopsies, nor the clinical and molecular heterogeneity of these diseases. Study Design Prospective observational cohort study. Setting & Participants N=221 MCD and FSGS patients enrolled in the Nephrotic Syndrome Study Network (NEPTUNE). Exposures The NEPTUNE Digital Pathology Scoring System (NDPSS) was applied to generate scores for 37 glomerular descriptors. Outcomes Time from biopsy to complete proteinuria remission, time from biopsy to kidney disease progression (40% eGFR decline or kidney failure), and eGFR over time. Analytical Approach Cluster analysis was used to group patients with similar morphologic characteristics. Glomerular descriptors and patient clusters were assessed for associations with outcomes using adjusted Cox models and linear mixed models. Messenger RNA from glomerular tissue was used to assess differentially expressed genes between clusters and identify genes associated with individual descriptors driving cluster membership. Results Three clusters were identified: X (N=56), Y (N=68), and Z (N=97). Clusters Y and Z had higher probabilities of proteinuria remission (HR [95% CI]= 1.95 [0.99, 3.85] and 3.29 [1.52, 7.13], respectively), lower hazards of disease progression 0.22 [0.08, 0.57] and 0.11 [0.03, 0.45], respectively), and lower loss of eGFR over time compared with X. Cluster X had 1920 differentially expressed genes compared to Y+Z, which reflected activation of pathways of immune response and inflammation. Six descriptors driving the clusters individually correlated with clinical outcomes and gene expression. Limitations Low prevalence of some descriptors and biopsy at a single time point. Conclusions The NDPSS allows for categorization of FSGS/MCD patients into clinically and biologically relevant subgroups, and uncovers histologic parameters associated with clinical outcomes and molecular signatures not included in current classification systems.
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Background and objective: Training a deep convolutional neural network (CNN) for automatic image classification requires a large database with images of labeled samples. However, in some applications such as biology and medicine only a few experts can correctly categorize each sample. Experts are able to identify small changes in shape and texture which go unnoticed by untrained people, as well as distinguish between objects in the same class that present drastically different shapes and textures. This means that currently available databases are too small and not suitable to train deep learning models from scratch. To deal with this problem, data augmentation techniques are commonly used to increase the dataset size. However, typical data augmentation methods introduce artifacts or apply distortions to the original image, which instead of creating new realistic samples, obtain basic spatial variations of the original ones. Methods: We propose a novel data augmentation procedure which generates new realistic samples, by combining two samples that belong to the same class. Although the idea behind the method described in this paper is to mimic the variations that diatoms experience in different stages of their life cycle, it has also been demonstrated in glomeruli and pollen identification problems. This new data augmentation procedure is based on morphing and image registration methods that perform diffeomorphic transformations. Results: The proposed technique achieves an increase in accuracy over existing techniques of 0.47%, 1.47%, and 0.23% for diatom, glomeruli and pollen problems respectively. Conclusions: For the Diatom dataset, the method is able to simulate the shape changes in different diatom life cycle stages, and thus, images generated resemble newly acquired samples with intermediate shapes. In fact, the other methods compared obtained worse results than those which were not using data augmentation. For the Glomeruli dataset, the method is able to add new samples with different shapes and degrees of sclerosis (through different textures). This is the case where our proposed DA method is more beneficial, when objects highly differ in both shape and texture. Finally, for the Pollen dataset, since there are only small variations between samples in a few classes and this dataset has other features such as noise which are likely to benefit other existing DA techniques, the method still shows an improvement of the results.
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In this narrative review, we focus on the application of artificial intelligence in the clinical history of patients with glomerular disease, digital pathology in kidney biopsy, renal ultrasonography imaging, and prediction of chronic kidney disease (CKD). With the development of natural language processing, the clinical history of a patient can be used to identify a computable phenotype. In kidney pathology, digital imaging has adopted innovative deep learning algorithms (DLAs) that can improve the predictive capability of the examined lesions. However, at this time, these applications can only be used in research because there is no recognized validation to replace the conventional diagnostic applications. Kidney ultrasonography, used in the clinical examination of patients, provides information about the progression of kidney damage. Machine learning algorithms (MLAs) with promising results for the early detection of CKD have been proposed, but, still, they are not solid enough to be incorporated into the clinical practice. A few tools for glomerulonephritis, based on MLAs, are available in clinical practice. They can be downloaded on computers and cellular phones but can only be applied to uniracial cohorts of patients. To improve their performance, it is necessary to organize large consortia with multiracial cohorts. Finally, in many studies MLA development has been carried out using retrospective cohorts. The performance of the models might differ in retrospective cohorts compared to real-world data. Therefore, the models should be validated in prospective external large cohorts.
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Background: Traditionally, cases for cohort selection and quality assurance purposes are identified through structured query language (SQL) searches matching specific keywords. Recently, several neural network-based natural language processing (NLP) pipelines have emerged as an accurate alternative/complementary method for case retrieval. Methods: The diagnosis section of 1000 pathology reports with the terms "colon" and "carcinoma" were retrieved from our laboratory information system through a SQL query. Each of the reports were labeled as either positive or negative, where cases are considered positive if the case was a primary adenocarcinoma of the colon. Negative cases comprised adenocarcinoma from other sites, metastatic adenocarcinomas, benign conditions, rectal cancers, and other cases that do not fit in the primary colonic adenocarcinoma category. The 1000 cases were randomly separated into training, validation, and holdout sets. A convolutional neural network (CNN) model built using Keras (a neural network library) was trained to identify positive cases, and the model was applied to the holdout set to predict the category for each case. Results: The CNN model classified 141 out of 149 primary colonic adenocarcinoma cases, and 43 out of 51 negative cases correctly, achieving an accuracy of 92% and area under the ROC curve (AUC) of 0.957. Conclusion: Trained convolutional neural network models by itself, or as an adjunct to keyword and pattern-based text extraction methods may be used to search for pathology cases of interest with high accuracy.
Chapter
The human body is complex but very interesting to discover. The human body has many unsolved riddles which yet haven’t been answered and sometimes even astonishes scientists and doctors when something new is discovered. The whole review consists of the functionality of the kidney; it causes of dysfunction, methods of detection, replacement techniques, and kidney-on-chip. Since every organ in the human body malfunctions due to various complications in its working which leads to various diseases. About, 13% of the world’s population is affected by kidney diseases. Since replacement techniques like dialysis and kidney transplantation are costly and sometimes, not available also due to lack of donors (in case of kidney transplantation). Researchers and scientists are trying to develop the kidney’s function artificially which is termed kidney-on-chip which is a revolutionary idea.KeywordsProximal convoluted tubule (PCT)Distal convoluted tubule (DCT)Acute kidney injury (AKI)Chronic kidney disease (CKD)Blood urea nitrogen (BUN)Glomerular filtration rate (GFR)
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Rationale & Objective The number of glomeruli is often used to determine the adequacy of a kidney biopsy (eg, at least 10 glomeruli). It is often assumed that biopsy specimens with limited amounts of cortex are too imprecise for detection of focal pathology. Study Design Clinical-pathologic correlation (cross-sectional). Setting & Participants Living kidney donors who underwent a needle core biopsy of their kidney at the time of donation. Exposure The amount of cortex biopsied as determined by either the number of glomeruli or area of cortex on histology. Outcome The percentage of globally sclerotic glomeruli, density of interstitial fibrosis foci, and severity of arteriosclerosis were determined. Analytical approach A beta-binomial model assessed how the mean percentage of globally sclerotic glomeruli and patient variability in percentage of globally sclerotic glomeruli differed with the number of glomeruli on the biopsy specimen. Additional models assessed the association of interstitial fibrosis and arteriosclerosis with number of glomeruli. Results There were 2,915 kidney donors studied. Fewer glomeruli on the biopsy specimen associated with higher mean percentage of globally sclerotic glomeruli and higher patient variability in percentage of globally sclerotic glomeruli. Smaller cortical volume on imaging correlated with both less cortex on biopsy and higher percentage of globally sclerotic glomeruli. Based on a statistical simulation, the probability of patient percentage of globally sclerotic glomeruli ≥ 10% if the biopsy percentage of globally sclerotic glomeruli is ≥10% (positive predictive value) was 45% with 1 to 9 glomeruli versus 31% with 10 or more glomeruli; the negative predictive value was 91% versus 98%. Fewer glomeruli also associated with more interstitial fibrosis and arteriosclerosis. Limitations The study was limited to living kidney donors. Patient variability in percentage of globally sclerotic glomeruli was based on a statistical model because multiple biopsy specimens per patient were not available. Conclusions The amount of cortex on a needle core biopsy is not completely random. Chronic changes from loss of cortex contribute to low amounts of cortex on a kidney biopsy specimen.
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Advancements in computer vision and artificial intelligence (AI) carry the potential to make significant contributions to health care, particularly in diagnostic specialties such as radiology and pathology. The impact of these technologies on physician stakeholders is the subject of significant speculation. There is however a dearth of information regarding the opinions, enthusiasm, and concerns of the pathology community at large. Here, we report results from a survey of 487 pathologist-respondents practicing in 54 countries, conducted to examine perspectives on AI implementation in clinical practice. Despite limitations, including difficulty with quantifying response bias and verifying identity of respondents to this anonymous and voluntary survey, several interesting findings were uncovered. Overall, respondents carried generally positive attitudes towards AI, with nearly 75% reporting interest or excitement in AI as a diagnostic tool to facilitate improvements in workflow efficiency and quality assurance in pathology. Importantly, even within the more optimistic cohort, a significant number of respondents endorsed concerns about AI, including the potential for job displacement and replacement. Overall, around 80% of respondents predicted the introduction of AI technology in the pathology laboratory within the coming decade. Attempts to identify statistically significant demographic characteristics (e.g., age, sex, type/place of practice) predictive of attitudes towards AI using Kolmogorov–Smirnov (KS) testing revealed several associations. Important themes which were commented on by respondents included the need for increasing efforts towards physician training and resolving medical-legal implications prior to the generalized implementation of AI in pathology.
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Introduction: The number of glomeruli and glomerulosclerosis evaluated on kidney biopsy slides constitute standard components of a renal pathology report. Prevailing methods for glomerular assessment remain manual, labor intensive, and nonstandardized. We developed a deep learning framework to accurately identify and segment glomeruli from digitized images of human kidney biopsies. Methods: Trichrome-stained images (n = 275) from renal biopsies of 171 patients with chronic kidney disease treated at the Boston Medical Center from 2009 to 2012 were analyzed. A sliding window operation was defined to crop each original image to smaller images. Each cropped image was then evaluated by at least 3 experts into 3 categories: (i) no glomerulus, (ii) normal or partially sclerosed (NPS) glomerulus, and (iii) globally sclerosed (GS) glomerulus. This led to identification of 751 unique images representing nonglomerular regions, 611 images with NPS glomeruli, and 134 images with GS glomeruli. A convolutional neural network (CNN) was trained with cropped images as inputs and corresponding labels as output. Using this model, an image processing routine was developed to scan the test images to segment the GS glomeruli. Results: The CNN model was able to accurately discriminate nonglomerular images from NPS and GS images (performance on test data: Accuracy: 92.67% ± 2.02% and Kappa: 0.8681 ± 0.0392). The segmentation model that was based on the CNN multilabel classifier accurately marked the GS glomeruli on the test data (Matthews correlation coefficient = 0.628). Conclusion: This work demonstrates the power of deep learning for assessing complex histologic structures from digitized human kidney biopsies.
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Glomerulus classification and detection in kidney tissue segments are key processes in nephropathology used for the correct diagnosis of the diseases. In this paper, we deal with the challenge of automating Glomerulus classification and detection from digitized kidney slide segments using a deep learning framework. The proposed method applies Convolutional Neural Networks (CNNs) between two classes: Glomerulus and Non-Glomerulus, to detect the image segments belonging to Glomerulus regions. We configure the CNN with the public pre-trained AlexNet model and adapt it to our system by learning from Glomerulus and Non-Glomerulus regions extracted from training slides. Once the model is trained, labeling is performed by applying the CNN classification to the image blocks under analysis. The results of the method indicate that this technique is suitable for correct Glomerulus detection in Whole Slide Images (WSI), showing robustness while reducing false positive and false negative detections.
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Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, there is a strong demand for the development of computer based image analysis systems. In this work, the focus is on the segmentation of the glomeruli constituting a highly relevant structure in renal histopathology, which has not been investigated before in combination with CNNs. We propose two different CNN cascades for segmentation applications with sparse objects. These approaches are applied to the problem of glomerulus segmentation and compared with conventional fully-convolutional networks. Overall, with the best performing cascade approach, single CNNs are outperformed and a pixel-level Dice similarity coefficient of 0.90 is obtained. Combined with qualitative and further object-level analyses the obtained results are assessed as excellent also compared to recent approaches. In conclusion, we can state that especially one of the proposed cascade networks proved to be a highly powerful tool for segmenting the renal glomeruli providing best segmentation accuracies and also keeping the computing time at a low level.
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Glomerulus classification in kidney tissue segments is a key process in nephropathology to obtain correct diseases diagnosis. In this paper, we deal with the challenge to automate the Glomerulus classification from digitized kidney slide segments using a deep learning framework. The proposed method applies Convolutional Neural Networks (CNNs) classification between two classes: Glomerulus and Non-Glomerulus, to detect the image segments belonging to Glomerulus regions. We configure the CNN with the public pre-trained AlexNet model, and adapt it to our system by learning from Glomerulus and Non-Glomerulus regions extracted from training slides. Once the model is trained, the labelling is performed applying the CNN classification to the image segments under analysis. The results obtained indicate that this technique is suitable for correct Glomerulus classification, showing robustness while reducing false positive and false negative detections.
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Transplantable kidneys are in very limited supply. Accurate viability assessment prior to transplantation could minimize organ discard. Rapid and accurate evaluation of intra-operative donor kidney biopsies is essential for determining which kidneys are eligible for transplantation. The criteria for accepting or rejecting donor kidneys relies heavily on pathologist determination of the percent of glomeruli (determined from a frozen section) that are normal and sclerotic. This percentage is a critical measurement that correlates with transplant outcome. Inter- and intra-observer variability in donor biopsy evaluation is, however, significant. An automated method for determination of percent global glomerulosclerosis could prove useful in decreasing evaluation variability, increasing throughput, and easing the burden on pathologists. Here, we describe the development of a deep learning model that identifies and classifies non-sclerosed and sclerosed glomeruli in whole-slide images of donor kidney frozen section biopsies. This model extends a convolutional neural network (CNN) pre-trained on a large database of digital images. The extended model, when trained on just 48 whole slide images, exhibits slide-level evaluation performance on par with expert renal pathologists. The model substantially outperforms a model trained on image patches of isolated glomeruli. Encouragingly, the model’s performance is robust to slide preparation artifacts associated with frozen section preparation. As the first model reported that identifies and classifies normal and sclerotic glomeruli in frozen kidney sections, and thus the first model reported in the literature relevant to kidney transplantation, it may become an essential part of donor kidney biopsy evaluation in the clinical setting.
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Transplantable kidneys are in very limited supply. Accurate viability assessment prior to transplantation could minimize organ discard. Rapid and accurate evaluation of intraoperative donor kidney biopsies is essential for determining which kidneys are eligible for transplantation. The criteria for accepting or rejecting donor kidneys relies heavily on pathologist determination of the percent of glomeruli (determined from a frozen section) that are normal and sclerotic. This percentage is a critical measurement that correlates with transplant outcome. Inter- and intra-observer variability in donor biopsy evaluation is, however, significant. An automated method for determination of percent global glomerulosclerosis could prove useful in decreasing evaluation variability, increasing throughput, and easing the burden on pathologists. Here, we describe the development of a deep learning model that identifies and classifies non-sclerosed and sclerosed glomeruli in whole-slide images of donor kidney frozen section biopsies. This model extends a convolutional neural network (CNN) pre-trained on a large database of digital images. The extended model, when trained on just 48 whole slide images, exhibits slide-level evaluation performance on par with expert renal pathologists. Encouragingly, the model’s performance is robust to slide preparation artifacts associated with frozen section preparation. The model substantially outperforms a model trained on image patches of isolated glomeruli, in terms of both accuracy and speed. The methodology overcomes the technical challenge of applying a pretrained CNN bottleneck model to whole-slide image classification. The traditional patch-based approach, while exhibiting deceptively good performance classifying isolated patches, does not translate successfully to whole-slide image segmentation in this setting. As the first model reported that identifies and classifies normal and sclerotic glomeruli in frozen kidney sections, and thus the first model reported in the literature relevant to kidney transplantation, it may become an essential part of donor kidney biopsy evaluation in the clinical setting.
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Background Histologic examination of fixed renal tissue is widely used to assess morphology and the progression of disease. Commonly reported metrics include glomerular number and injury. However, characterization of renal histology is a time-consuming and user-dependent process. To accelerate and improve the process, we have developed a glomerular localization pipeline for trichrome-stained kidney sections using a machine learning image classification algorithm. Methods We prepared 4- μ m slices of kidneys from rats of various genetic backgrounds that were subjected to different experimental protocols and mounted the slices on glass slides. All sections used in this analysis were trichrome stained and imaged in bright field at a minimum resolution of 0.92 μ m per pixel. The training and test datasets for the algorithm comprised 74 and 13 whole renal sections, respectively, totaling over 28,000 glomeruli manually localized. Additionally, because this localizer will be ultimately used for automated assessment of glomerular injury, we assessed bias of the localizer for preferentially identifying healthy or damaged glomeruli. Results Localizer performance achieved an average precision and recall of 96.94% and 96.79%, respectively, on whole kidney sections without evidence of bias for or against glomerular injury or the need for manual preprocessing. Conclusions This study presents a novel and robust application of convolutional neural nets for the localization of glomeruli in healthy and damaged trichrome-stained whole-renal section mounts and lays the groundwork for automated glomerular injury scoring.
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We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif-ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make train-ing faster, we used non-saturating neurons and a very efficient GPU implemen-tation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.