Kensaku Mori

• Nagoya University

Purpose Depth estimation is a powerful tool for navigation in laparoscopic surgery. Previous methods utilize predicted depth maps and the relative poses of the camera to accomplish self-supervised depth estimation. However, the smooth surfaces of organs with textureless regions and the laparoscope’s complex rotations make depth and pose estimation...

Background Computer-aided diagnosis (CADx) enables the distinction between neoplastic and non-neoplastic polyps during colonoscopy. We aimed to estimate the patient-level benefit and harm of CADx. Methods We conducted a comparative analysis on data from the EndoBRAIN international clinical trial, evaluating the effect of optical diagnosis during co...

Purpose The paper introduces a novel two-step network based on semi-supervised learning for intestine segmentation from CT volumes. The intestine folds in the abdomen with complex spatial structures and contact with neighboring organs that bring difficulty for accurate segmentation and labeling at the pixel level. We propose a multi-dimensional con...

To determine whether convolutional neural networks (CNN) can classify the severity of central vision loss using fundus autofluorescence (FAF) images and color fundus images of retinitis pigmentosa (RP), and to evaluate the utility of those images for severity classification. Retrospective observational study. Medical charts of patients with RP who...

Purpose Deep-learning-based supervised CT segmentation relies on fully and densely labeled data, the labeling process of which is time-consuming. In this study, our proposed method aims to improve segmentation performance on CT volumes with limited annotated data by considering category-wise difficulties and distribution. Methods We propose a nove...

In medical imaging, developing generalized segmentation models that can handle multiple organs and lesions is crucial. However, the scarcity of fully annotated datasets and strict privacy regulations present significant barriers to data sharing. Federated Learning (FL) allows decentralized model training, but existing FL methods often struggle with...

In this paper, we study weakly-supervised laparoscopic image segmentation with sparse annotations. We introduce a novel Bayesian deep learning approach designed to enhance both the accuracy and interpretability of the model's segmentation, founded upon a comprehensive Bayesian framework, ensuring a robust and theoretically validated method. Our app...

Purpose Accurate segmentation of tubular structures is crucial for clinical diagnosis and treatment but is challenging due to their complex branching structures and volume imbalance. The purpose of this study is to propose a 3D deep learning network that incorporates skeleton information to enhance segmentation accuracy in these tubular structures....

Parkinson disease (PD) is a common progressive neurodegenerative disorder in our ageing society. Early-stage PD biomarkers are desired for timely clinical intervention and understanding of pathophysiology. Since one of the characteristics of PD is the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, we propose a featu...

Purpose This paper considers a new problem setting for multi-organ segmentation based on the following observations. In reality, (1) collecting a large-scale dataset from various institutes is usually impeded due to privacy issues; (2) many images are not labeled since the slice-by-slice annotation is costly; and (3) datasets may exhibit inconsiste...

We performed a conversation analysis of the speech conducted among the surgical team during three-dimensional (3D)-printed liver model navigation for thrice or more repeated hepatectomy (TMRH). Seventeen patients underwent 3D-printed liver navigation surgery for TMRH. After transcription of the utterances recorded during surgery, the transcribed ut...

Since even subtle mucosal changes may be depicted using virtual endoscopy created by the three-dimensional reconstruction of MDCT images, we developed a novel diagnostic imaging system that integrates and displays virtual enteroscopy, curved planar reconstruction, and a virtual unfolded view, the width of which changes with increases/decreases in t...

Purpose Pancreatic duct dilation is associated with an increased risk of pancreatic cancer, the most lethal malignancy with the lowest 5-year relative survival rate. Automatic segmentation of the dilated pancreatic duct from contrast-enhanced CT scans would facilitate early diagnosis. However, pancreatic duct segmentation poses challenges due to it...

Background Fundus fluorescein angiography (FFA) is an imaging method used to assess retinal vascular structures by injecting exogenous dye. FFA images provide complementary information to that provided by the widely used color fundus (CF) images. However, the injected dye can cause some adverse side effects, and the method is not suitable for all p...

This study investigated the influence of using three-dimensional (3D) computer and 3D-printed models on the spatial reasoning of experts and novices. The task of this study required general university students as novices in Experiment 1 and surgeons specializing in digestive surgery as experts in Experiment 2 to infer the cross sections of a liver,...

Background: We report a new real-time navigation system for laparoscopic hepatectomy (LH), which resembles a car navigation system. Material and methods: Virtual three-dimensional liver and body images were reconstructed using the "New-VES" system, which worked as roadmap during surgery. Several points of the patient's body were registered in vi...

This study focuses on enhancing the inference speed of laparoscopic tool detection on embedded devices. Laparoscopy, a minimally invasive surgery technique, markedly reduces patient recovery times and postoperative complications. Real‐time laparoscopic tool detection helps assisting laparoscopy by providing information for surgical navigation, and...

Aim To develop an artificial intelligence (AI) algorithm that diagnoses cataracts/corneal diseases from multiple conditions using smartphone images. Methods This study included 6442 images that were captured using a slit-lamp microscope (6106 images) and smartphone (336 images). An AI algorithm was developed based on slit-lamp images to differenti...

The task of segmentation is integral to computer‐aided surgery systems. Given the privacy concerns associated with medical data, collecting a large amount of annotated data for training is challenging. Unsupervised learning techniques, such as contrastive learning, have shown powerful capabilities in learning image‐level representations from unlabe...

This paper focuses on a new and challenging problem related to instrument segmentation. This paper aims to learn a generalizable model from distributed datasets with various imperfect annotations. Collecting a large‐scale dataset for centralized learning is usually impeded due to data silos and privacy issues. Besides, local clients, such as hospit...

We propose a large-factor super-resolution (SR) method for performing SR on registered medical image datasets. Conventional SR approaches use low-resolution (LR) and high-resolution (HR) image pairs to train a deep convolutional neural network (DCN). However, LR–HR images in medical imaging are commonly acquired from different imaging devices, and...

In the context of Minimally Invasive Surgery, surgeons mainly rely on visual feedback during medical operations. In common procedures such as tissue resection, the automation of endoscopic control is crucial yet challenging, particularly due to the interactive dynamics of multi-agent operations and the necessity for real-time adaptation. This paper...

Developing a generalized segmentation model capable of simultaneously delineating multiple organs and diseases is highly desirable. Federated learning (FL) is a key technology enabling the collaborative development of a model without exchanging training data. However, the limited access to fully annotated training data poses a major challenge to tr...

Ossification of the ligaments progresses slowly in the initial stages, and most patients are unaware of the disease until obvious myelopathy symptoms appear. Consequently, treatment and clinical outcomes are not satisfactory. This study is aimed at developing an automated system for the detection of the thoracic ossification of the posterior longit...

The intestine is an essential digestive organ that can cause serious health problems once diseased. This paper proposes a method for intestine segmentation to intestine obstruction diagnosis assistance called multi-dimensional U-Net (M U-Net). We employ two encoders to extract features from two-dimensional (2D) CT slices and three-dimensional (3D)...

This work proposes an innovative self-supervised approach to monocular depth estimation in laparoscopic scenarios. Previous methods independently predicted depth maps ignoring spatial coherence in local regions and temporal correlation between adjacent images. The proposed approach leverages spatio-temporal coherence to address the challenges of te...

Semantic segmentation of laparoscopic images is an important issue for intraoperative guidance in laparoscopic surgery. However, acquiring and annotating laparoscopic datasets is labor-intensive, which limits the research on this topic. In this paper, we tackle the Domain-Adaptive Semantic Segmentation (DASS) task, which aims to train a segmentatio...

COVID-19 is linked to endotheliopathy and coagulopathy, which can result in multi-organ failure. The mechanisms causing endothelial damage due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain elusive. Here, we developed an infection-competent human vascular organoid from pluripotent stem cells for modeling endotheliopathy. Lon...

Developing a generalized segmentation model capable of simultaneously delineating multiple organs and diseases is highly desirable. Federated learning (FL) is a key technology enabling the collaborative development of a model without exchanging training data. However, the limited access to fully annotated training data poses a major challenge to tr...

Objectives: In this study, we aimed to develop an artificial intelligence (AI)-based model for predicting post-ERCP pancreatitis (PEP). Methods: We retrospectively reviewed ERCP patients at Nagoya University Hospital (NUH) and Toyota Memorial Hospital (TMH). We constructed two prediction models, a random forest (RF), one of the machine-learning...

This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes. One major role of chest CT scanning in COVID-19 diagnoses is identification of an inflammation particular to the disease. This task is generally performed by radiologists through an interpretation of the...

Purpose: Endoscopic submucosal dissection (ESD) is a minimally invasive treatment for early gastric cancer. However, perforations may happen and cause peritonitis during ESD. Thus, there is a potential demand for a computer-aided diagnosis system to support physicians in ESD. This paper presents a method to detect and localize perforations from co...

Purpose: Minimally invasive surgery (MIS) using a thoraco- or laparoscope is becoming a more common surgical technique. In MIS, a magnified view from a thoracoscope helps surgeons conduct precise operations. However, there is a risk of the visible area becoming narrow. To confirm that the operation field is safe, the surgeon will draw the thoracos...

We present a general framework for medical image segmentation from limited supervision, reducing the reliance on fully and densely labeled data. Our method is simple, jointly trains triple diverse models, and adopts a mix augmentation scheme, and thus is called TriMix. TriMix imposes consistency under a more challenging perturbation, i.e., combinin...

Oesophageal achalasia is a primary oesophageal motility disorder disease. To diagnose oesophagus achalasia, physicians recommend endoscopic evaluation of the oesophagus. However, a low sensitivity still accompanies esophagoscopy on oesophagus achalasia diagnosis. Thus, a quantitative diagnosis system is needed to support physicians diagnose achalas...

Polyp segmentation from colonoscopy videos is an essential task in medical image processing for detecting early cancer. However, segmenting a precise boundary is still challenging, even with powerful deep neural networks. We consider the difficulty can be caused by: (1) the ambiguity boundary and (2) some complicated shape makes polyps hard to segm...

PurposeA surgical navigation system helps surgeons understand anatomical structures in the operative field during surgery. Patient-to-image registration, which aligns coordinate systems between the CT volume and a positional tracker, is vital for accurate surgical navigation. Although a point-based rigid registration method using fiducials on the b...

Objectives: Meticulous inspection of the mucosa during colonoscopy, represents a lengthier withdrawal time, but has been shown to increase adenoma detection rate (ADR). We investigated if artificial intelligence-aided speed monitoring can improve suboptimal withdrawal time. Methods: We evaluated the implementation of a computer-aided speed monit...

Physicians use an endoscopic navigation system during bronchoscopy to decrease the risk of getting lost in complex tree-structure like bronchus. Most existing navigation systems based on the camera pose estimated from bronchoscope tracking and/or deep learning. However, bronchoscope tracking-based method exists tracking error, and the pre-training...

This study presents a novel framework for classifying and visualizing pneumonia induced by COVID-19 from CT images. Although many image classification methods using deep learning have been proposed, in the case of medical image fields, standard classification methods are unable to be used in some cases because the medical images that belong to the...

Objectives: To develop a convolutional neural network to recognize the seminal vesicle and vas deferens (SV-VD) in the posterior approach of robot-assisted radical prostatectomy (RARP) and assess the performance of the convolutional neural network model under clinically relevant conditions. Methods: Intraoperative videos of robot-assisted radica...

Purpose Segmentation tasks are important for computer-assisted surgery systems as they provide the shapes of organs and the locations of instruments. What prevents the most powerful segmentation approaches from becoming practical applications is the requirement for annotated data. Active learning provides strategies to dynamically select the most i...

Purpose This paper aims to propose a deep learning-based method for abdominal artery segmentation. Blood vessel structure information is essential to diagnosis and treatment. Accurate blood vessel segmentation is critical to preoperative planning. Although deep learning-based methods perform well on large organs, segmenting small organs such as blo...

This work proposes a new feature extraction method to analyse patterns of the substantia nigra in Parkinson disease. Recent imaging techniques such that neuromelanin-sensitive MRI enable us to recognise the region of the substantia nigra and capture early Parkinson-disease-related changes. However, automated feature extraction of Parkinson-disease-...

Segmentation studies in medical image analysis are always associated with a particular task scenario. However, building datasets to train models to segment multiple types of organs and pathologies is challenging. For example, a dataset annotated for the pancreas and pancreatic tumors will result in a model that cannot segment other organs, like the...

Automatic segmentation of substantia nigra (SN), which is Parkinson’s disease-related tissue, is an important step toward accurate computer-aided diagnosis systems. Conventional methods for SN segmentation depend heavily on limited magnetic resonance imaging (MRI) modalities such as neuromelanin and quantitative susceptibility mapping, which requir...

Importance: Deep learning-based automatic surgical instrument recognition is an indispensable technology for surgical research and development. However, pixel-level recognition with high accuracy is required to make it suitable for surgical automation. Objective: To develop a deep learning model that can simultaneously recognize 8 types of surgi...

Purpose: Precise polyp detection and localisation are essential for colonoscopy diagnosis. Statistical machine learning with a large-scale data set can contribute to the construction of a computer-aided diagnosis system for the prevention of overlooking and miss-localisation of a polyp in colonoscopy. We propose new visual explaining methods for a...

Background Recognition of the inferior mesenteric artery (IMA) during colorectal cancer surgery is crucial to avoid intraoperative hemorrhage and define the appropriate lymph node dissection line. This retrospective feasibility study aimed to develop an IMA anatomical recognition model for laparoscopic colorectal resection using deep learning, and...

Clarifying the generalizability of deep-learning-based surgical-instrument segmentation networks in diverse surgical environments is important in recognizing the challenges of overfitting in surgical-device development. This study comprehensively evaluated deep neural network generalizability for surgical instrument segmentation using 5238 images r...

Background Current colonoscopy practice allows endoscopists to leave tiny hyperplastic polyps in-situ in the rectosigmoid colon if endoscopic diagnosis is done with a high level of accuracy and confidence. Artificial intelligence (AI) is expected to further facilitate this process, however, there are no studies that have evaluated the additional va...

Background: Artificial intelligence (AI) for polyp detection is being introduced to colonoscopy, but there is uncertainty how this affects endoscopists' ability to detect polyps and neoplasms. We performed a video-based study to address whether AI improved the endoscopists' performance to detect polyps. Methods: We established a dataset of 200 c...

Background Artificial intelligence (AI) has been largely investigated in the field of surgery, particularly in quality assurance. However, AI-guided navigation during surgery has not yet been put into practice because a sufficient level of performance has not been reached. We aimed to develop deep learning-based AI image processing software to iden...

Purpose We propose a super-resolution (SR) method, named SR-CycleGAN, for SR of clinical computed tomography (CT) images to the micro-focus x-ray CT CT (μCT) level. Due to the resolution limitations of clinical CT (about 500×500×500 μm3/voxel), it is challenging to obtain enough pathological information. On the other hand, μCT scanning allows the i...