Article

Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network

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Abstract

Background and objective: Cardiac perfusion magnetic resonance imaging (MRI) with first pass dynamic contrast enhancement (DCE) is a useful tool to identify perfusion defects in myocardial tissues. Automatic segmentation of the myocardium can lead to efficient quantification of perfusion defects. The purpose of this study was to investigate the usefulness of uncertainty estimation in deep convolutional neural networks for automatic myocardial segmentation. Methods: A U-Net segmentation model was trained on the cardiac cine data. Monte Carlo dropout sampling of the U-Net model was performed on the dynamic perfusion datasets frame-by-frame to estimate the standard deviation (SD) maps. The uncertainty estimate based on the sum of the SD values was used to select the optimal frames for endocardial and epicardial segmentations. DCE perfusion data from 35 subjects (14 subjects with coronary artery disease, 8 subjects with hypertrophic cardiomyopathy, and 13 healthy volunteers) were evaluated. The Dice similarity scores of the proposed method were compared with those of a semi-automatic U-Net segmentation method, which involved user selection of an image frame for segmentation in the cardiac perfusion dataset. Results: The proposed method was fully automatic and did not require manual labeling of the cardiac perfusion image data for model development. The mean Dice similarity score of the proposed automatic method was 0.806 (±0.096), which was comparable to the 0.808 (±0.084) Dice score of the semi-automatic U-Net segmentation method (intraclass correlation coefficient = 0.61, P < 0.001). Conclusions: Our study demonstrated the feasibility of applying an existing model trained on cardiac cine data to dynamic cardiac perfusion data to achieve robust and automatic segmentation of the myocardium. The uncertainty estimates can be used for screening purposes, which would facilitate the cases with high endocardial and epicardial uncertainty estimates to be sent for further evaluation and correction by human experts.

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... Results from uncertainty and error maps can be represented by a single score that can be used for failure detection in cases where it has a strong linear relationship with Dice values. This score can comprise measures like the intersection over union overlap [14], quality metric [12], [13], mean voxel-wise uncertainty [7], [14] and the pixel-wise sum [10], [11]. ...
... Similar to what is reported in [10], [11], we found a significant correlation between the VS values and Dice coefficients. We also saw an improved mean Dice value after we removed failed segmentation maps based on VS values. ...
... To evaluate the performance of QC approaches such as aggregation and Dice prediction, we used statistical metrics. Similar as in [10], [11], to aggregate uncertainty and error maps we used a voxelwise sum (VS) measure. As demonstrated in [4], [45], small isolated WMH clusters are more difficult to estimate correctly. ...
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Machine learning algorithms underpin modern diagnostic-aiding software, which has proved valuable in clinical practice, particularly in radiology. However, inaccuracies, mainly due to the limited availability of clinical samples for training these algorithms, hamper their wider applicability, acceptance, and recognition amongst clinicians. We present an analysis of state-of-the-art automatic quality control (QC) approaches that can be implemented within these algorithms to estimate the certainty of their outputs. We validated the most promising approaches on a brain image segmentation task identifying white matter hyperintensities (WMH) in magnetic resonance imaging data. WMH are a correlate of small vessel disease common in mid-to-late adulthood and are particularly challenging to segment due to their varied size, and distributional patterns. Our results show that the aggregation of uncertainty and Dice prediction were most effective in failure detection for this task. Both methods independently improved mean Dice from 0.82 to 0.84. Our work reveals how QC methods can help to detect failed segmentation cases and therefore make automatic segmentation more reliable and suitable for clinical practice.
... An additional useful property of CNNs is that they lend themselves well to be used with transfer learning where the majority of the network is kept with its high-level feature extraction ability and only the last output layer is exchanged with a new layer to fit with the purpose of the study [51]. As a result, the majority of deep learning studies on perfusion imaging in the last few years have used CNNs as the main architecture, as shown in Fig. 3. Furthermore, the power and flexibility of CNNs has opened the window for deep learning applications in more challenging image analysis domains such as stress perfusion CMR [23,27,30,31], resting CT perfusion (rCTP), and myocardial contrast echocardiography (MCE) [22]. ...
... There are some promising data on the effectiveness of using deep learning with CNNs to the pre-processing stage of perfusion quantification in CMR by automated identification of anatomical landmarks, such as the right ventricle (RV) insertion point into the septum and left ventricle (LV) centre on peak contrast enhancement [23,31,43]. Furthermore, CNN algorithms have been successfully applied to the segmentation of CMR perfusion images [27,30] with high performance. These applications in CMR still require further research. ...
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... Uncertainty modeling and estimation are being increasingly used in deep learning-based medical imaging applications [16,9,4,22,12,18]. These methods usually produce multiple output predictions for a single input and then measure uncertainty by aggregating information from these outputs. ...
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... In contrast, the accuracy is obtained 99.92% with the same method for the intra-patient scheme. Kim et al. [ 66 ] utilized the DCNN models such as U-Net, including Semi-automatic u-Net, Automatic U-Net (AU-Net), and automated encoder-decoder u-Net with Monte Carlo dropout sampling to estimate uncertainty in u-Net model fully automatic based on the cardiac perfusion image dataset for myocardial segmentation. Their results regarding average Dice similarity criterion using the proposed AU-Net method based on uncertainty estimation of 0.806 (average ± standard deviation: ± 0.096) performs better than the semi-automatic and automatic u-Net models in terms of the same criterion with values of 0.808 (average ± standard deviation: ± 0.084) and 0.729 (average ± standard deviation: 0.147). ...
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... Based on Lead II ECG signals, the Gabor-filter DCNN and DCNN models attained average accuracy rates of 99.55% and 98.74%, respectively, for the four-class classification task. Kim et al (Kim et al 2020) utilized U-Net architecture combined with the Monte Carlo dropout technique to estimate the uncertainty of the U-Net model using cardiac perfusion images for myocardial segmentation. Their approach obtained a better Dice similarity of 0.806±0.096 ...
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Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals worldwide. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG and some other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and some other biophysical signals.
... The MCD mechanism to measure NN uncertainty has primarily been used for image processing tasks with convolutional neural networks (CNNs), where the found estimates can be assessed visually [13][14][15]. Nevertheless, there are also entries for time series problems. ...
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The application of echo state networks to time series prediction has provided notable results, favored by their reduced computational cost, since the connection weights require no learning. However, there is a need for general methods that guide the choice of parameters (particularly the reservoir size and ridge regression coefficient), improve the prediction accuracy, and provide an assessment of the uncertainty of the estimates. In this paper we propose such a mechanism for uncertainty quantification based on Monte Carlo dropout, where the output of a subset of reservoir units is zeroed before the computation of the output. Dropout is only performed at the test stage, since the immediate goal is only the computation of a measure of the goodness of the prediction. Results show that the proposal is a promising method for uncertainty quantification, providing a value that is either strongly correlated with the prediction error or reflects the prediction of qualitative features of the time series. This mechanism could eventually be included into the learning algorithm in order to obtain performance enhancements and alleviate the burden of parameter choice.
... Model/Methods used Brain tumor [106], [107], [110]- [112], [114]- [118] Base U-net [18], [109], [120] 3D U-net [81] Adversarial net; GAN [59], [108] Residual block [113] Dense block [87] Cascaded U-net [92] Residual block; Parallel U-net [44] Inception block; Up skip connections [45] Dense block; Inception block [119] 3D U-net; Residual block [19] 3D U-net, Inception block, Residual block Brain tissue [103], [121]- [124] Base U-net [28], [160] 3D U-net [161] 2.5D U-net [54] Residual block [101] Parallel U-net [41] Attention gate; Residual block White matter tracts [126], [127] U-net with modified skip connections [125] Base U-net [89] Cascaded U-net Fetal brain [128]- [130] Base U-net [131] Base U-net; 3D U-net Stroke lesion/thrombus [133]- [136] Base U-net [132] 3D U-net [69] Dense block; Inception block Cardiovascular structures [138], [140]- [142], [144], [146] Base U-net [62], [139], [143], [145] Residual block [4], [9], [10], [28] 3D U-net [86], [90] Cascaded U-net [5], [8] Cascaded 3D U-net [11] Base U-net; 3D U-net [83] Adversarial net; GAN [58] Residual block [137] Dense block [74] U-net++ [47] Inception block; Residual block [6] Cascaded 3D U-net; Residual block Prostate cancer [147], [149]- [151] Base U-net [28] 3D U-net [64] Recurrent net [58] Residual block Liver cancer [152], [153] Base U-net [21] 3D U-net Nasopharyngeal cancer [25] 3D U-net; Residual block [98] Parallel U-net [154] Modified convolution block Femur [12]- [14] 3D U-net Breast cancer [155] Base U-net [99] Parallel U-net Spinal cord [156], [157] Base U-net Blood vessels [100] Base U-net Placenta [158] Base U-net Uterus [159] Base U-net Vertebral column [17] 3D U-net ...
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... Fig. 13 depicts the experiment's segmentation findings. Kim et al. [31] proposed a U-Netbased segmentation model. The method performs frame-by-frame Monte Carlo dropout sampling of the U-net model on a dynamic blood perfusion dataset and uncertainty estimates by the excellent frame's Standard Deviation (SD) sum. ...
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Background: Due to the advancement of medical imaging and computer technology, machine intelligence to analyze clinical image data increases the probability of disease prevention and successful treatment. When diagnosing and detecting heart disease, medical imaging can provide high-resolution scans of every organ or tissue in the heart. The diagnostic results obtained by the imaging method are less susceptible to human interference. They can process numerous patient information, assist doctors in early detection of heart disease, intervene and treat patients, and improve the understanding of heart disease symptoms and clinical diagnosis of great significance. In a computer-aided diagnosis system, accurate segmentation of cardiac scan images is the basis and premise of subsequent thoracic function analysis and 3D image reconstruction. Existing techniques: This paper systematically reviews automatic methods and some difficulties for cardiac segmentation in radiographic images. Combined with recent advanced deep learning techniques, the feasibility of using deep learning network models for image segmentation is discussed, and the commonly used deep learning frameworks are compared. Developed insights: There are many standard methods for medical image segmentation, such as traditional methods based on regions and edges and methods based on deep learning. Because of characteristics of non-uniform grayscale, individual differences, artifacts and noise of medical images, the above image segmentation methods have certain limitations. It is tough to obtain the needed results sensitivity and accuracy when performing heart segmentation. The deep learning model proposed has achieved good results in image segmentation. Accurate segmentation improves the accuracy of disease diagnosis and reduces subsequent irrelevant computations. Summary: There are two requirements for accurate segmentation of radiological images. One is to use image segmentation to improve the development of computer-aided diagnosis. The other is to achieve complete segmentation of the heart. When there are lesions or deformities in the heart, there will be some abnormalities in the radiographic images, and the segmentation algorithm needs to segment the heart altogether. The quantity of processing inside a certain range will no longer be a restriction for real-time detection with the advancement of deep learning and the enhancement of hardware device performance.
... The trained ANNs with dropout can make multiple predictions to estimate uncertainty. The MCD mechanism has mainly been applied for image processing tasks with convolutional neural networks (CNNs), where the uncertainty can be evaluated visually (Kim, Kim, and Choe 2020;Myojin et al. 2019;Stoean et al. 2020). Uber rides also used MCD combined with Bayesian NN for the large-scale time series anomaly detection (Zhu and Laptev 2017). ...
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... However, these methods simply enhance the ability to perceive global context information and do not implement the extraction of complex pixel features and the reuse of pixel features. The latest segmentation frameworks are mainly based on the encoder-decoder architecture and have been successfully used in many computer vision tasks, including human pose estimation [36], target detection [37,38], image style [39], high-resolution and super-resolution [40,41] of the image and so on. Most methods attempt to combine features from adjacent stages to enhance lowlevel features, regardless of their different representations and global context information. ...
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Medical image segmentation plays an important role in many clinical medicines, such as medical diagnosis and computer-assisted treatment. However, due to the large quality differences, variable lesion areas and their complex shapes, medical image segmentation is a very challenging task. However, most of the recent deep learning methods ignore the global context information as well as the receptive fields of pixels and do not consider the reuse of pixel features during the feature extraction stage. In this paper, we propose DGFAU-Net, an encoder–decoder structured 2D segmentation model, to overcome the shortcomings aforementioned. In the encoder, DenseNet and AtrousCNN networks are leveraged to extract image features. The DenseNet network is mainly used to achieve the reuse of pixel features, and AtrousCNN is utilized to enhance the receptive field of pixels. In the decoder, two modules, global feature attention upsample (GFAU) and pyramid pooling attention squeeze-excitation (PPASE), are proposed. GFAU combines low-level and high-level features to generate new features with richer information for improving the perceptions of global contextual information of pixels. PPASE employs a multi-scale pooling layer to enhance the pixel’s acceptance field. In addition, Focal loss is used to balance the difference between samples of the lesion and non-lesioned areas. Extensive experiments are conducted on one local dataset and two public datasets, which are the local dataset of MRI images of carotid plaque, DRIVE vessel segmentation dataset and CHASE_DB1 vessel segmentation dataset, and the experimental results demonstrate the effectiveness of our proposed model.
... Notable innovations were made in specific manuscripts. One recent paper incorporated a Monte-Carlo dropout in conjunction with a U-Net to provide uncertainty estimates for the segmentation [37]. This approach may help obviate unpredictable failure, and provide clinicians some degree of understanding of the expected quality of images. ...
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Machine learning (ML) and deep learning (DL) techniques have been increasingly applied to help diagnose coronary artery disease (CAD) as well as help with patient management decisions. Imaging has begun to play a larger role in these studies. Cardiovascular magnetic resonance (CMR) offers multiple techniques to diagnose CAD, and ML and DL have been used with these techniques in an effort to improve both the image quality and the speed of image interpretation. In particular, ML and DL have been applied to direct imaging of coronary vessel anatomy, imaging of coronary flow, and myocardial perfusion imaging. In applications aimed at imaging the coronary artery anatomy, ML and DL techniques have been used to improve image quality in reconstruction, improve the speed of reconstruction, allow for more sparse sampling of data, and enable automated evaluation of image quality. In applications of coronary flow imaging, ML and DL techniques have been used to reduce the uncertainty of phase-contrast measurements of blood velocity and flow, and physics-informed neural networks have been used to improve the modeling of flow based on both acquired image data and natural laws of motion. In myocardial perfusion imaging, ML and DL techniques have been used at multiple steps in the image analysis process to automate quantitative blood flow measurements, including motion correction, image registration, tracer kinetic modeling, and detection of perfusion defects. Future applications of ML and DL in evaluating CAD are expected to continue to develop with increasing impact in both diagnosis and patient management.
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Free access until Feb. 06, 2019 (https://authors.elsevier.com/a/1YFLEcV4K-KaG) This paper proposes a novel approach for automatic left ventricle (LV) quantification using convolutional neural networks (CNN). Methods: The general framework consists of one CNN for detecting the LV, and another for tissue classification. Also, three new deep learning architectures were proposed for LV quantification. These new CNNs introduce the ideas of sparsity and depthwise separable convolution into the U-net architecture, as well as, a residual learning strategy level-to-level. To this end, we extend the classical U-net architecture and use the generalized Jaccard distance as optimization objective function. Results: The CNNs were trained and evaluated with 140 patients from two public cardiovascular magnetic resonance datasets (Sunnybrook and Cardiac Atlas Project) by using a 5-fold cross-validation strategy. Our results demonstrate a suitable accuracy for myocardial segmentation ($\sim$0.9 Dice's coefficient), and a strong correlation with the most relevant physiological measures: 0.99 for end-diastolic and end-systolic volume, 0.97 for the left myocardial mass, 0.95 for the ejection fraction and 0.93 for the stroke volume and cardiac output. Conclusion: Our simulation and clinical evaluation results demonstrate the capability and merits of the proposed CNN to estimate different structural and functional features such as LV mass and EF which are commonly used for both prognosis and treatment of different pathologies. Significance: This paper suggests a new approach for automatic LV quantification based on deep learning where errors are comparable to the inter- and intra-operator ranges for manual contouring. Also, this approach may have important applications on motion quantification.
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Automated left ventricular (LV) segmentation is crucial for efficient quantification of cardiac function and morphology to aid subsequent management of cardiac pathologies. In this paper, we parameterize the complete (all short axis slices and phases) LV segmentation task in terms of the radial distances between the LV centerpoint and the endo- and epicardial contours in polar space. We then utilize convolutional neural network regression to infer these parameters. Utilizing parameter regression, as opposed to conventional pixel classification, allows the network to inherently reflect domain-specific physical constraints. We have benchmarked our approach primarily against the publicly-available left ventricle segmentation challenge (LVSC) dataset, which consists of 100 training and 100 validation cardiac MRI cases representing a heterogeneous mix of cardiac pathologies and imaging parameters across multiple centers. Our approach attained a .77 Jaccard index, which is the highest published overall result in comparison to other automated algorithms. To test general applicability, we also evaluated against the Kaggle Second Annual Data Science Bowl, where the evaluation metric was the indirect clinical measures of LV volume rather than direct myocardial contours. Our approach attained a Continuous Ranked Probability Score (CRPS) of .0124, which would have ranked tenth in the original challenge. With this we demonstrate the effectiveness of convolutional neural network regression paired with domain-specific features in clinical segmentation.
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We introduce a new methodology that combines deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance (MR) data. This combination is relevant for segmentation problems, where the visual object of interest presents large shape and appearance variations, but the annotated training set is small, which is the case for various medical image analysis applications, including the one considered in this paper. In particular, level set methods are based on shape and appearance terms that use small training sets, but present limitations for modelling the visual object variations. Deep learning methods can model such variations using relatively small amounts of annotated training, but they often need to be regularised to produce good generalisation. Therefore, the combination of these methods brings together the advantages of both approaches, producing a methodology that needs small training sets and produces accurate segmentation results. We test our methodology on the MICCAI 2009 left ventricle segmentation challenge database (containing 15 sequences for training, 15 for validation and 15 for testing), where our approach achieves the most accurate results in the semi-automated problem and state-of-the-art results for the fully automated challenge.
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Background: Recently there have been several clinical MR perfusion studies in patients with hypertrophic cardiomyopathy (HCM) who may suffer from myocardial ischemia due to coronary microvascular dysfunction. In these studies, data analysis relied on a manual procedure of tracing epicardial and endocardial borders. The goal of this work is to develop and validate a robust semi-automated analysis method for myocardial perfusion quantification in clinical HCM data. Method: Dynamic multi-slice stress perfusion MRI data were acquired from 18 HCM patients. The proposed semi-automated method required user input of two landmark selections: LV center point and RV insertion point. Automated segmentations of the endocardial and epicardial borders were performed in three short-axis slices using distance regularized level set evolution on RV, LV, and myocardial enhancement frames. Results: The proposed automated epicardial border detection method resulted in average radial distance errors of 7.5%, 9.5%, and 11.6% in basal, mid, and apical slices, respectively, when compared to manual tracing of the borders as a reference. In linear regression analysis, the highest correlation of myocardial upslope measurements was observed between the manual method and the proposed method in the anterolateral section (r=0.964), and the lowest correlation was observed in the inferoseptal section (r=0.866). Conclusion: The proposed semi-automated method for myocardial MR perfusion quantification is feasible in HCM patients who typically show (1) irregular myocardial shape and (2) low image contrast between the myocardium and its surrounding regions due to coronary microvascular disease.
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Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning algorithms combined with deformable models to develop and evaluate a fully automatic segmentation tool for the LV from short-axis cardiac MRI datasets. The method employs deep learning algorithms to learn the segmentation task from the ground true data. Convolutional networks are employed to automatically detect the LV chamber in MRI dataset. Stacked autoencoders are utilized to infer the shape of the LV. The inferred shape is incorporated into deformable models to improve the accuracy and robustness of the segmentation. We validated our method using 45 cardiac MR datasets taken from the MICCAI 2009 LV segmentation challenge and showed that it outperforms the state-of-the art methods. Excellent agreement with the ground truth was achieved. Validation metrics, percentage of good contours, Dice metric, average perpendicular distance and conformity, were computed as 96.69%, 0.94, 1.81mm and 0.86, versus those of 79.2%-95.62%, 0.87-0.9, 1.76-2.97mm and 0.67-0.78, obtained by other methods, respectively.
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Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different "thinned" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets. © 2014 Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov.
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Deep learning tools have recently gained much attention in applied machine learning. However such tools for regression and classification do not allow us to capture model uncertainty. Bayesian models offer us the ability to reason about model uncertainty, but usually come with a prohibitive computational cost. We show that dropout in multilayer perceptron models (MLPs) can be interpreted as a Bayesian approximation. Results are obtained for modelling uncertainty for dropout MLP models - extracting information that has been thrown away so far, from existing models. This mitigates the problem of representing uncertainty in deep learning without sacrificing computational performance or test accuracy. We perform an exploratory study of the dropout uncertainty properties. Various network architectures and non-linearities are assessed on tasks of extrapolation, interpolation, and classification. We show that model uncertainty is important for classification tasks using MNIST as an example, and use the model's uncertainty in a Bayesian pipeline, with deep reinforcement learning as a concrete example.
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Purpose: To develop an automated framework for accurate analysis of myocardial perfusion using first-pass magnetic resonance imaging. Methods: The proposed framework consists of four processing stages. First, in order to account for heart deformations due to respiratory motion and heart contraction, a two-step registration methodology is proposed, which has the ability to account for the global and local motions of the heart. The methodology involves an affine-based registration followed by a local B-splines alignment to maximize a new similarity function based on the first- and second-order normalized mutual information. Then the myocardium is segmented using a level-set function, its evolution being constrained by three features, namely, a weighted shape prior, a pixelwise mixed object/background image intensity distribution, and an energy of a second-order binary Markov-Gibbs random field spatial model. At the third stage, residual segmentation errors and imperfection of image alignment are reduced by employing a Laplace-based registration refinement step that provides accurate pixel-on-pixel matches on all segmented frames to generate accurate parametric perfusion maps. Finally, physiology is characterized by pixel-by-pixel mapping of empirical indexes (peak signal intensity, time-to-peak, initial upslope, and the average signal change of the slowly varying agent delivery phase), based on contrast agent dynamics. Results: The authors tested our framework on 24 perfusion data sets from 8 patients with ischemic damage who are undergoing a novel myoregeneration therapy. The performance of the processing steps of our framework is evaluated using both synthetic and in-vivo data. First, our registration methodology is evaluated using realistic synthetic phantoms and a distance-based error metric, and an improvement of registration is documented using the proposed similarity measure (P-value ≤10(-4)). Second, evaluation of our segmentation using the Dice similarity coefficient, documented an average of 0.910 ± 0.037 compared to two other segmentation methods that achieved average values of 0.862 ± 0.045 and 0.844 ± 0.047. Also, the receiver operating characteristic (ROC) analysis of our multifeature segmentation yielded an area under the ROC curve of 0.92, while segmentation based intensity alone showed low performance (an area of 0.69). Moreover, our framework indicated the ability, using empirical perfusion indexes, to reveal regional perfusion improvements with therapy and transmural perfusion differences across the myocardial wall. Conclusions: By quantitative and visual assessment, our framework documented the ability to characterize regional and transmural perfusion, thereby it augmenting the ability to assess follow-up treatment for patients undergoing myoregeneration therapy. This is afforded by our framework being able to handle both global and local deformations of the heart, segment accurately the myocardial wall, and provide accurate pixel-on-pixel matches of registered perfusion images.
Article
Contrast material-enhanced myocardial perfusion imaging by using cardiac magnetic resonance (MR) imaging has, during the past decade, evolved into an accurate technique for diagnosing coronary artery disease, with excellent prognostic value. Advantages such as high spatial resolution; absence of ionizing radiation; and the ease of routine integration with an assessment of viability, wall motion, and cardiac anatomy are readily recognized. The need for training and technical expertise and the regulatory hurdles, which might prevent vendors from marketing cardiac MR perfusion imaging, may have hampered its progress. The current review considers both the technical developments and the clinical experience with cardiac MR perfusion imaging, which hopefully demonstrates that it has long passed the stage of a research technique. In fact, cardiac MR perfusion imaging is moving beyond traditional indications such as diagnosis of coronary disease to novel applications such as in congenital heart disease, where the imperatives of avoidance of ionizing radiation and achievement of high spatial resolution are of high priority. More wide use of cardiac MR perfusion imaging, and novel applications thereof, are aided by the progress in parallel imaging, high-field-strength cardiac MR imaging, and other technical advances discussed in this review. © RSNA, 2013.
Article
AimsPerfusion-cardiac magnetic resonance (CMR) has emerged as a potential alternative to single-photon emission computed tomography (SPECT) to assess myocardial ischaemia non-invasively. The goal was to compare the diagnostic performance of perfusion-CMR and SPECT for the detection of coronary artery disease (CAD) using conventional X-ray coronary angiography (CXA) as the reference standard.Methods and resultsIn this multivendor trial, 533 patients, eligible for CXA or SPECT, were enrolled in 33 centres (USA and Europe) with 515 patients receiving MR contrast medium. Single-photon emission computed tomography and CXA were performed within 4 weeks before or after CMR in all patients. The prevalence of CAD in the sample was 49%. Drop-out rates for CMR and SPECT were 5.6 and 3.7%, respectively (P = 0.21). The primary endpoint was non-inferiority of CMR vs. SPECT for both sensitivity and specificity for the detection of CAD. Readers were blinded vs. clinical data, CXA, and imaging results. As a secondary endpoint, the safety profile of the CMR examination was evaluated. For CMR and SPECT, the sensitivity scores were 0.67 and 0.59, respectively, with the lower confidence level for the difference of +0.02, indicating superiority of CMR over SPECT. The specificity scores for CMR and SPECT were 0.61 and 0.72, respectively (lower confidence level for the difference: -0.17), indicating inferiority of CMR vs. SPECT. No severe adverse events occurred in the 515 patients.Conclusion In this large multicentre, multivendor study, the sensitivity of perfusion-CMR to detect CAD was superior to SPECT, while its specificity was inferior to SPECT. Cardiac magnetic resonance is a safe alternative to SPECT to detect perfusion deficits in CAD. All rights reserved.
Article
Guidelines for triaging patients for cardiac catheterization recommend a risk assessment and noninvasive testing. We determined patterns of noninvasive testing and the diagnostic yield of catheterization among patients with suspected coronary artery disease in a contemporary national sample. From January 2004 through April 2008, at 663 hospitals in the American College of Cardiology National Cardiovascular Data Registry, we identified patients without known coronary artery disease who were undergoing elective catheterization. The patients' demographic characteristics, risk factors, and symptoms and the results of noninvasive testing were correlated with the presence of obstructive coronary artery disease, which was defined as stenosis of 50% or more of the diameter of the left main coronary artery or stenosis of 70% or more of the diameter of a major epicardial vessel. A total of 398,978 patients were included in the study. The median age was 61 years; 52.7% of the patients were men, 26.0% had diabetes, and 69.6% had hypertension. Noninvasive testing was performed in 83.9% of the patients. At catheterization, 149,739 patients (37.6%) had obstructive coronary artery disease. No coronary artery disease (defined as <20% stenosis in all vessels) was reported in 39.2% of the patients. Independent predictors of obstructive coronary artery disease included male sex (odds ratio, 2.70; 95% confidence interval [CI], 2.64 to 2.76), older age (odds ratio per 5-year increment, 1.29; 95% CI, 1.28 to 1.30), presence of insulin-dependent diabetes (odds ratio, 2.14; 95% CI, 2.07 to 2.21), and presence of dyslipidemia (odds ratio, 1.62; 95% CI, 1.57 to 1.67). Patients with a positive result on a noninvasive test were moderately more likely to have obstructive coronary artery disease than those who did not undergo any testing (41.0% vs. 35.0%; P<0.001; adjusted odds ratio, 1.28; 95% CI, 1.19 to 1.37). In this study, slightly more than one third of patients without known disease who underwent elective cardiac catheterization had obstructive coronary artery disease. Better strategies for risk stratification are needed to inform decisions and to increase the diagnostic yield of cardiac catheterization in routine clinical practice.
Article
Quantitative determination of myocardial perfusion currently involves time-consuming postprocessing. This retrospective study presents automatic postprocessing consisting of image registration and image segmentation to obtain regional signal intensity time courses and quantitative perfusion values. The automatic postprocessing was tested in 75 examinations in volunteers and patients, 57 at rest and 18 under adenosine-induced stress, and compared with a manual evaluation. In a substudy consisting of 10 examinations, the interobserver variability of the manual evaluation was investigated. Manual evaluation resulted in perfusion values with a median of 0.70 ml/g/min ranging from 0.03 to 3.68 ml/g/min. For all 75 examinations, the variability (standard deviation of the differences) between automatic and manual evaluation was 0.34 ml/g/min. Interobserver variability was of a similar order, 0.35 ml/g/min for all measurements. Automatic evaluation was successfully applied to all datasets giving results equivalent to manual evaluation. The time of user interaction for one single slice could be reduced from 25 min for manual evaluation to less than 1 min using the automatic algorithm. This reduction may allow quantitative magnetic resonance perfusion imaging to become a routine clinical procedure.
Leveraging uncertainty estimates for predicting segmentation quality
  • T Devries
  • G W Taylor