Lesion segmentation is a core task for quantitative analysis of MRI scans of Multiple Sclerosis patients. The recent success of deep learning techniques in a variety of medical image analysis applications has renewed community interest in this challenging problem and led to a burst of activity for new algorithm development. In this survey, we investigate the supervised CNN-based methods for MS lesion segmentation. We decouple these reviewed works into their algorithmic components and discuss each separately. For methods that provide evaluations on public benchmark datasets, we report comparisons between their results.
Technical University of Denmark（丹麦技术大学）；Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A·马丁诺斯生物医学成像中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Technical University of Munich（慕尼黑工业大学）
In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis. The method builds upon an existing cross-sectional method for simultaneous whole-brain and lesion segmentation, introducing subject-specific latent variables to encourage temporal consistency between longitudinal scans. It is very generally applicable, as it does not make any prior assumptions on the scanner, the MRI protocol, or the number and timing of longitudinal follow-up scans. Preliminary experiments on three longitudinal datasets indicate that the …
SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints
Imperial College London（伦敦帝国学院）；Data Science Institute, Imperial College London（伦敦帝国学院数据科学研究所）；Beijing Institute of Technology（北京理工大学）；UK Dementia Research Institute（英国痴呆症研究所）；Rosalind Franklin Institute（罗莎琳德·富兰克林研究所）
Polytechnique Montreal（蒙特利尔理工学院）；University of Montreal（蒙特利尔大学）；CRIUGM（蒙特利尔大学神经科学研究所）
Despite recent improvements in medical image segmentation, the ability to generalize across imaging contrasts remains an open issue. To tackle this challenge, we implement Feature-wise Linear Modulation (FiLM) to leverage physics knowledge within the segmentation model and learn the characteristics of each contrast. Interestingly, a well-optimised U-Net reached the same performance as our FiLMed-Unet on a multi-contrast dataset (0.72 of Dice score), which suggests that there is a bottleneck in spinal MS lesion segmentation different from the generalization across varying contrasts. This bottle…
McGill University（麦吉尔大学）；MILA Quebec AI Institute（魁北克人工智能研究所（MILA））；Montreal Neurological Institute（蒙特利尔神经学研究所）
Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side effects/preferences for individual patients. Making this choice as early as possible is important, as delays in finding an effective therapy can lead to irreversible disability accrual. To this end, we present the first deep neural network model for individualized treatment decisions from baseline magnetic resonance imaging (MRI) (with clinical information if available) for MS patients. Our model (a) predicts future new and enlarging T2 weighted (NE-T2) le…
Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of Lausanne（洛桑大学）；Lausanne University Hospital（洛桑大学医院）；National Institute of Neurological Disorders and Stroke（美国国家神经疾病与卒中研究所）；National Institutes of Health（美国国立卫生研究院）；University of Bern（伯尔尼大学）；University of Pennsylvania（宾夕法尼亚大学）；Cedars-Sinai Medical Center（西达赛奈医疗中心）
The automated detection of cortical lesions (CLs) in patients with multiple sclerosis (MS) is a challenging task that, despite its clinical relevance, has received very little attention. Accurate detection of the small and scarce lesions requires specialized sequences and high or ultra-high field MRI. For supervised training based on multimodal structural MRI at 7T, two experts generated ground truth segmentation masks of 60 patients with 2014 CLs. We implemented a simplified 3D U-Net with three resolution levels (3D U-Net-). By increasing the complexity of the task (adding brain tissue segmen…
Istituto Italiano di Tecnologia（意大利理工学院）；Simon Fraser University（西蒙弗雷泽大学）；University of Verona（维罗纳大学）；Fondation Campus Biotech（生物科技园区基金会）；University of British Columbia（不列颠哥伦比亚大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
This paper presents a simple and effective generalization method for magnetic resonance imaging (MRI) segmentation when data is collected from multiple MRI scanning sites and as a consequence is affected by (site-)domain shifts. We propose to integrate a traditional encoder-decoder network with a regularization network. This added network includes an auxiliary loss term which is responsible for the reduction of the domain shift problem and for the resulting improved generalization. The proposed method was evaluated on multiple sclerosis lesion segmentation from MRI data. We tested the proposed…
University of Oulu（奥卢大学）；KFUPM（法赫德国王石油与矿业大学）；King Fahd Specialist Hospital Dammam（达曼法赫德国王专科医院）
Introduction: Multiple Sclerosis (MS) is a chronic disease that affects millions of people across the globe. MS can critically affect different organs of the central nervous system such as the eyes, the spinal cord, and the brain. Background: To help physicians in diagnosing MS lesions, computer-aided methods are widely used. In this regard, a considerable research has been carried out in the area of automatic detection and segmentation of MS lesions in magnetic resonance images (MRIs). Methodology: In this study, we review the different approaches that have been used in computer-aided detecti…
Deep learning (DL) networks have recently been shown to outperform other segmentation methods on various public, medical-image challenge datasets [3,11,16], especially for large pathologies. However, in the context of diseases such as Multiple Sclerosis (MS), monitoring all the focal lesions visible on MRI sequences, even very small ones, is essential for disease staging, prognosis, and evaluating treatment efficacy. Moreover, producing deterministic outputs hinders DL adoption into clinical routines. Uncertainty estimates for the predictions would permit subsequent revision by clinicians. We …
Automated Detection of Multiple Sclerosis Lesions on 7-tesla MRI Using U-net and Transformer-based Segmentation
Department of Neurology, University of Maryland School of Medicine（马里兰大学医学院神经内科）；Baltimore VA Medical Center, VA Maryland Healthcare System（巴尔的摩退伍军人事务医疗中心，马里兰州退伍军人医疗系统）
Henry M. Jackson Foundation（亨利·M·杰克逊基金会）；National Institute of Health（美国国立卫生研究院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Multiple Sclerosis (MS) is an autoimmune disease that leads to lesions in the central nervous system. Magnetic resonance (MR) images provide sufficient imaging contrast to visualize and detect lesions, particularly those in the white matter. Quantitative measures based on various features of lesions have been shown to be useful in clinical trials for evaluating therapies. Therefore robust and accurate segmentation of white matter lesions from MR images can provide important information about the disease status and progression. In this paper, we propose a fully convolutional neural network (CNN…
Springbok, Inc.（斯普林博克公司）；University of Virginia（弗吉尼亚大学）
Deep neural networks (DNN) have shown promises in the lesion segmentation of multiple sclerosis (MS) from multicontrast MRI including T1, T2, proton density (PD) and FLAIR sequences. However, one challenge in deploying such networks into clinical practice is the variability of imaging protocols, which often differ from the training dataset as certain MRI sequences may be unavailable or unusable. Therefore, trained networks need to adapt to practical situations when imaging protocols are different in deployment. In this paper, we propose a DNN-based MS lesion segmentation framework with a novel…
Research Institute of Computer Vision and Robotics, University of Girona（赫罗纳大学计算机视觉与机器人研究所）；Assiut University（艾斯尤特大学）；Vall d’Hebron University Hospital（瓦尔德希伯伦大学医院）
In this paper, we propose generating synthetic multiple sclerosis (MS) lesions on MRI images with the final aim to improve the performance of supervised machine learning algorithms, therefore avoiding the problem of the lack of available ground truth. We propose a two-input two-output fully convolutional neural network model for MS lesion synthesis in MRI images. The lesion information is encoded as discrete binary intensity level masks passed to the model and stacked with the input images. The model is trained end-to-end without the need for manually annotating the lesions in the training set…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Automated segmentation of multiple sclerosis (MS) lesions from MRI scans is important to quantify disease progression. In recent years, convolutional neural networks (CNNs) have shown top performance for this task when a large amount of labeled data is available. However, the accuracy of CNNs suffers when dealing with few and/or sparsely labeled datasets. A potential solution is to leverage the information available in large public datasets in conjunction with a target dataset which only has limited labeled data. In this paper, we propose a training framework, SSL2 (self-supervised-semi-superv…
Benchmarking and Explaining Deep Learning Cortical Lesion MRI Segmentation in Multiple Sclerosis
Faculty of Biology and Medicine, University of Lausanne (UNIL)（日内瓦大学生物与医学学院）；Radiology Department, Lausanne University Hospital (CHUV)（洛桑大学医院放射科）；MedGIFT, Institute of Informatics, School of Management, HES--SO Valais--Wallis University of Applied Sciences（应用科学与艺术西瓦利斯-瓦利斯大学信息学院）；CIBM Center for Biomedical Imaging（生物医学成像中心）；Department of Radiology and Medical Informatics, University of Geneva（日内瓦大学放射科与医学信息学系）；Translational Imaging in Neurology (ThINK) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel（神经学转化成像（ThINK）巴塞尔，巴塞尔大学医院和巴塞尔大学医学与生物医学工程系）；Multiple Sclerosis Center, Department of Neurology, University Hospital Basel（多发性硬化中心，巴塞尔大学医院神经科）；Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel（临床神经免疫学与神经科学巴塞尔研究中心（RC2NB），巴塞尔大学医院和巴塞尔大学）；Division of Radiological Physics, Department of Radiology, University Hospital Basel（放射物理学系，放射科，巴塞尔大学医院）；Department of Neurology, Icahn School of Medicine at Mount Sinai（伊坎医学院神经科）；Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health（神经疾病国家研究所转化神经放射学组，国家卫生研究院）；Neuroinflammation Imaging Lab (NIL), Université catholique de Louvain（神经炎症成像实验室（NIL），卢旺达大学）
Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Berlin Institute of Health（柏林健康研究所）
Machine learning-based imaging diagnostics has recently reached or even superseded the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is commonly diagnosed utilizing a combination of clinical presentation and …
University of Georgia（佐治亚大学）；Florida International University（佛罗里达国际大学）
Medical Imaging is one of the growing fields in the world of computer vision. In this study, we aim to address the Diabetic Retinopathy (DR) problem as one of the open challenges in medical imaging. In this research, we propose a new lesion detection architecture, comprising of two sub-modules, which is an optimal solution to detect and find not only the type of lesions caused by DR, their corresponding bounding boxes, and their masks; but also the severity level of the overall case. Aside from traditional accuracy, we also use two popular evaluation criteria to evaluate the outputs of our mod…
Deep neural networks have become the gold-standard approach for the automated segmentation of 3D medical images. Their full acceptance by clinicians remains however hampered by the lack of intelligible uncertainty assessment of the provided results. Most approaches to quantify their uncertainty, such as the popular Monte Carlo dropout, restrict to some measure of uncertainty in prediction at the voxel level. In addition not to be clearly related to genuine medical uncertainty, this is not clinically satisfying as most objects of interest (e.g. brain lesions) are made of groups of voxels whose …
McGill University（麦吉尔大学）；MILA (Quebec Artificial Intelligence Institute)（魁北克人工智能研究所（MILA））；University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（阿兰·图灵研究所）；NeuroRx Research（NeuroRx研究公司）
Many automatic machine learning models developed for focal pathology (e.g. lesions, tumours) detection and segmentation perform well, but do not generalize as well to new patient cohorts, impeding their widespread adoption into real clinical contexts. One strategy to create a more diverse, generalizable training set is to naively pool datasets from different cohorts. Surprisingly, training on this \it{big data} does not necessarily increase, and may even reduce, overall performance and model generalizability, due to the existence of cohort biases that affect label distributions. In this paper,…
Saratoga High School（萨拉托加高中）；Stanford University（斯坦福大学）
Chest X-rays of coronavirus disease 2019 (COVID-19) patients are frequently obtained to determine the extent of lung disease and are a valuable source of data for creating artificial intelligence models. Most work to date assessing disease severity on chest imaging has focused on segmenting computed tomography (CT) images; however, given that CTs are performed much less frequently than chest X-rays for COVID-19 patients, automated lung lesion segmentation on chest X-rays could be clinically valuable. There currently exists a universal shortage of chest X-rays with ground truth COVID-19 lung le…
Imperial College London（帝国理工学院）；UK Dementia Research Institute（英国痴呆症研究所）
Understanding the intensity characteristics of brain lesions is key for defining image-based biomarkers in neurological studies and for predicting disease burden and outcome. In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthetic lesions on healthy images and synthesize subject-specific pseudo-healthy images from pathological images. Furthermore, the proposed method can be used as a data augmentation module to generate synthetic images for training brain image segmentation networks. Experiments on mult…
Otto-von-Guericke University（奥托·冯·格里克大学）；University Hospital of Magdeburg（马格德堡大学医院）
Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical practice due to the increasing incidence of liver cancer each year. Whereas various network variants with overall promising results in the field of medical image segmentation have been successfully developed over the last years, almost all of them struggle with the challenge of accurately segmenting hepatic lesions in magnetic resonance imaging (MRI). This led to the idea of combining elements of convolutional and transformer-based architectures to overcome the existing limitations. T…
The University of Sydney（悉尼大学）；Sydney Neuroimaging Analysis Centre（悉尼神经影像分析中心）；NVIDIA Corporation（英伟达公司）
Federated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative learning without sharing raw data. Despite great success, FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks, due to variance in lesion characteristics imparted by different scanners and acquisition parameters. In this work, we propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms. Specifically, a learnable weight is assigned to each local node during the aggregation process, based on its segmentation perfo…
Brain tumor imaging has been part of the clinical routine for many years to perform non-invasive detection and grading of tumors. Tumor segmentation is a crucial step for managing primary brain tumors because it allows a volumetric analysis to have a longitudinal follow-up of tumor growth or shrinkage to monitor disease progression and therapy response. In addition, it facilitates further quantitative analysis such as radiomics. Deep learning models, in particular CNNs, have been a methodology of choice in many applications of medical image analysis including brain tumor segmentation. In this …
Tel Aviv University（特拉维夫大学）；The Chaim Sheba Medical Center（夏姆谢巴医疗中心）；Bar-Ilan University（巴伊兰大学）
Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets. However, obtaining such datasets in the medical domain remains a challenge. In this paper, we present methods for generating synthetic medical images using recently presented deep learning Generative Adversarial Networks (GANs). Furthermore, we show that generated medical images can be used for synthetic data augmentation, and improve the performance of CNN for medical image classificatio…
ALTA Institute, University of Cambridge（剑桥大学阿尔塔研究所）；University of Applied Sciences of Western Switzerland (HES-SO)（瑞士西部应用科学与艺术大学（HES-SO））；University of Lausanne（洛桑大学）；Lausanne University Hospital（洛桑大学医院）
The development of automatic segmentation techniques for medical imaging tasks requires assessment metrics to fairly judge and rank such approaches on benchmarks. The Dice Similarity Coefficient (DSC) is a popular choice for comparing the agreement between the predicted segmentation against a ground-truth mask. However, the DSC metric has been shown to be biased to the occurrence rate of the positive class in the ground-truth, and hence should be considered in combination with other metrics. This work describes a detailed analysis of the recently proposed normalised Dice Similarity Coefficient…
P-Count: Persistence-based Counting of White Matter Hyperintensities in Brain MRI
Martinos Center for Biomedical Imaging, MGH and Harvard Medical School（马蒂诺斯生物医学成像中心，麻省总医院与哈佛医学院）；Center for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital（哥本哈根大学医院丹麦磁共振研究中心）；Computer Science and Artificial Intelligence Laboratory, MIT（麻省理工学院计算机科学与人工智能实验室）
Multiple Sclerosis (MS) is a chronic neurological condition characterized by the development of lesions in the white matter of the brain. T2-fluid attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) provides superior visualization and characterization of MS lesions, relative to other MRI modalities. Follow-up brain FLAIR MRI in MS provides helpful information for clinicians towards monitoring disease progression. In this study, we propose a novel modification to generative adversarial networks (GANs) to predict future lesion-specific FLAIR MRI for MS at fixed time inte…
Feature reassembly is an essential component in modern CNN-based segmentation approaches, which includes feature downsampling and upsampling operators. Existing operators reassemble multiple features from a small predefined region into one for each target location independently. This may result in loss of spatial information, which could vanish activations caused by tiny lesions particularly when they cluster together. In this paper, we propose a many-to-many reassembly of features (M2MRF). It reassembles features in a dimension-reduced feature space and simultaneously aggregates multiple feat…
Technical University of Munich（慕尼黑工业大学）；University of Ljubljana（卢布尔雅那大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Segmentation of Multiple Sclerosis (MS) lesions in longitudinal brain MR scans is performed for monitoring the progression of MS lesions. We hypothesize that the spatio-temporal cues in longitudinal data can aid the segmentation algorithm. Therefore, we propose a multi-task learning approach by defining an auxiliary self-supervised task of deformable registration between two time-points to guide the neural network toward learning from spatio-temporal changes. We show the efficacy of our method on a clinical dataset comprised of 70 patients with one follow-up study for each patient. Our results…
National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）；Ping An Insurance Company of China（中国平安保险（集团）股份有限公司）
Automatic lesion detection from computed tomography (CT) scans is an important task in medical imaging analysis. It is still very challenging due to similar appearances (e.g. intensity and texture) between lesions and other tissues, making it especially difficult to develop a universal lesion detector. Instead of developing a specific-type lesion detector, this work builds a Universal Lesion Detector (ULDor) based on Mask R-CNN, which is able to detect all different kinds of lesions from whole body parts. As a state-of-the-art object detector, Mask R-CNN adds a branch for predicting segmentati…
Flinders University（弗林德斯大学）；Flinders Medical Centre（弗林德斯医疗中心）；Dr Jones & Partners Medical Imaging（琼斯博士及合伙人医疗影像中心）；University of Oxford（牛津大学）；University of Adelaide（阿德莱德大学）
Longitudinal imaging forms an essential component in the management and follow-up of many medical conditions. The presence of lesion changes on serial imaging can have significant impact on clinical decision making, highlighting the important role for automated change detection. Lesion changes can represent anomalies in serial imaging, which implies a limited availability of annotations and a wide variety of possible changes that need to be considered. Hence, we introduce a new unsupervised anomaly detection and localisation method trained exclusively with serial images that do not contain any…
Technical University of Denmark（丹麦技术大学）；Copenhagen University Hospital Hvidovre（哥本哈根大学医院维兹奥勒医院）；Medical Image Analysis Center (MIAC AG)（医学图像分析中心（MIAC AG））；Technical University of Munich（慕尼黑工业大学）；Copenhagen University Hospital Bispebjerg（哥本哈根大学医院比斯佩比约医院）；University of Copenhagen（哥本哈根大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients. The method integrates a novel model for white matter lesions into a previously validated generative model for whole-brain segmentation. By using separate models for the shape of anatomical structures and their appearance in MRI, the algorithm can adapt to data acquired with different scanners and imaging protocols without retraining. We validate the method using four disparate datasets, showing rob…
Universidade da Coruña（拉科鲁尼亚大学）；Instituto de Investigación Biomédica de A Coruña (INIBIC)（拉科鲁尼亚生物医学研究所）；Hospital Clínico San Carlos（圣卡洛斯临床医院）；Instituto de Investigación Sanitaria (IdISSC)（圣卡洛斯健康研究所）；Centro Internacional de Oftalmología Avanzada（国际高级眼科中心）
Age-related macular degeneration (AMD) is a degenerative disorder affecting the macula, a key area of the retina for visual acuity. Nowadays, it is the most frequent cause of blindness in developed countries. Although some promising treatments have been developed, their effectiveness is low in advanced stages. This emphasizes the importance of large-scale screening programs. Nevertheless, implementing such programs for AMD is usually unfeasible, since the population at risk is large and the diagnosis is challenging. All this motivates the development of automatic methods. In this sense, severa…
Institute of Medical Technology, Hamburg University of Technology（汉堡工业大学医学技术研究所）；jung diagnostics GmbH（荣格诊断有限公司）；Department of Neurology, University Hospital Zurich and University of Zurich（苏黎世大学医院与苏黎世大学神经病学系）
Multiple sclerosis lesion activity segmentation is the task of detecting new and enlarging lesions that appeared between a baseline and a follow-up brain MRI scan. While deep learning methods for single-scan lesion segmentation are common, deep learning approaches for lesion activity have only been proposed recently. Here, a two-path architecture processes two 3D MRI volumes from two time points. In this work, we investigate whether extending this problem to full 4D deep learning using a history of MRI volumes and thus an extended baseline can improve performance. For this purpose, we design a…
Hamburg University of Technology（汉堡工业大学）；jung diagnostics GmbH（jung diagnostics 有限公司）；University Hospital Zurich（苏黎世大学医院）；University of Zurich（苏黎世大学）；Technische Universität Dresden（德累斯顿工业大学）
Multiple sclerosis is an inflammatory autoimmune demyelinating disease that is characterized by lesions in the central nervous system. Typically, magnetic resonance imaging (MRI) is used for tracking disease progression. Automatic image processing methods can be used to segment lesions and derive quantitative lesion parameters. So far, methods have focused on lesion segmentation for individual MRI scans. However, for monitoring disease progression, \textit{lesion activity} in terms of new and enlarging lesions between two time points is a crucial biomarker. For this problem, several classic me…
RimSet: Quantitatively Identifying and Characterizing Chronic Active Multiple Sclerosis Lesion on Quantitative Susceptibility Maps
Department of Electrical and Computer Engineering, Johns Hopkins University（约翰霍普金斯大学电气与计算机工程系）；Department of Radiology, Weill Medical College of Cornell University（康奈尔大学韦尔医学院放射科）；Sibley School of Mechanical and Aerospace Engineering, Cornell University（康奈尔大学西尔伯学校机械与航空航天工程系）；Department of Electrical and Computer Engineering, Cornell University（康奈尔大学电气与计算机工程系）
University of Sydney（悉尼大学）；University of New South Wales（新南威尔士大学）；Sydney Neuroimaging Analysis Centre（悉尼神经影像分析中心）
Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system, characterized by the appearance of focal lesions in the white and gray matter that topographically correlate with an individual patient's neurological symptoms and signs. Magnetic resonance imaging (MRI) provides detailed in-vivo structural information, permitting the quantification and categorization of MS lesions that critically inform disease management. Traditionally, MS lesions have been manually annotated on 2D MRI slices, a process that is inefficient and prone to inter-/intra-obser…
Sun Yat-sen University（中山大学）；The First Affiliated Hospital, Sun Yat-sen University（中山大学附属第一医院）；Infervision Inc.（推想医疗科技股份有限公司）
Hepatocellular carcinoma (HCC) is the second most frequent cause of malignancy-related death and is one of the diseases with the highest incidence in the world. Because the liver is the only organ in the human body that is supplied by two major vessels: the hepatic artery and the portal vein, various types of malignant tumors can spread from other organs to the liver. And due to the liver masses' heterogeneous and diffusive shape, the tumor lesions are very difficult to be recognized, thus automatic lesion detection is necessary for the doctors with huge workloads. To assist doctors, this work…
Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis
Lausanne University Hospital（洛桑大学医院）；University of Lausanne（洛桑大学）；University of Applied Sciences of Western Switzerland（瑞士西部应用科学大学）；University of Cambridge（剑桥大学）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；University Hospital Basel（巴塞尔大学医院）；IBM Research Europe（IBM欧洲研究院）
Robert Bosch GmbH（罗伯特·博世有限公司）；TU Dortmund University（多特蒙德工业大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly sho…
Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis
University of Lausanne（洛桑大学）；Lausanne University Hospital（洛桑大学医院）；University of Applied Sciences Western Switzerland (HES-SO)（瑞士西部应用科学大学（HES-SO））；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；University Hospital and University Basel（巴塞尔大学医院与巴塞尔大学）；University of Genova（热那亚大学）；University of Geneva（日内瓦大学）
Multiple Sclerosis (MS) is a chronic neuroinflammatory disease and multi-modality MRIs are routinely used to monitor MS lesions. Many automatic MS lesion segmentation models have been developed and have reached human-level performance. However, most established methods assume the MRI modalities used during training are also available during testing, which is not guaranteed in clinical practice. Previously, a training strategy termed Modality Dropout (ModDrop) has been applied to MS lesion segmentation to achieve the state-of-the-art performance with missing modality. In this paper, we present …
Amirkabir University of Technology (Tehran Polytechnic)（阿米尔卡比尔理工大学（德黑兰理工学院））；South Tehran Branch Islamic Azad University (IAU)（伊斯兰阿扎德大学南德黑兰分校）；Arak University of Medical Sciences（阿拉克医科大学）
Magnetic resonance images (MRI) play an important role in supporting and substituting clinical information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit the modification of properties of fuzzy -c means algorithms and the canny edge detection. By changing and reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship between MS lesions and edge detection is established. For the special case of FCM, w…
University of LAquila（拉奎拉大学）；Sapienza University of Rome（罗马第一大学）
To date, several automated strategies for identification/segmentation of Multiple Sclerosis (MS) lesions with the use of Magnetic Resonance Imaging (MRI) have been presented, but they are outperformed by human experts, from whom they act very differently. This is mainly due to: the ambiguity originated by MRI instabilities; peculiar variability of MS; non specificity of MRI regarding MS. Physicians partially manage the uncertainty generated by ambiguity relying on radiological/clinical/anatomical background and experience. To emulate human diagnosis, we present an automated framework for ident…
Universidad de Buenos Aires（布宜诺斯艾利斯大学）；CONICET（国家科学技术研究委员会）；Universidad Nacional del Litoral（滨海国立大学）
Brain lesion and anatomy segmentation in magnetic resonance images are fundamental tasks in neuroimaging research and clinical practice. Given enough training data, convolutional neuronal networks (CNN) proved to outperform all existent techniques in both tasks independently. However, to date, little work has been done regarding simultaneous learning of brain lesion and anatomy segmentation from disjoint datasets. In this work we focus on training a single CNN model to predict brain tissue and lesion segmentations using heterogeneous datasets labeled independently, according to only one of the…
Instance-level quantitative saliency in multiple sclerosis lesion segmentation
Translational Imaging in Neurology (ThINk)（神经学转化成像（ThINk））；Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel（医学与生物医学工程系，巴塞尔大学医院和巴塞尔大学）；Department of Neurology, University Hospital Basel（神经病学系，巴塞尔大学医院）；Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB)（巴塞尔临床神经免疫学和神经科学研究中心（RC2NB））；MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland（MedGIFT，信息学院，管理学院，瓦莱-迪-瓦尔省应用科学与艺术西方瑞士大学）；CIBM Center for Biomedical Imaging, Lausanne, Switzerland（洛桑瑞士生物医学成像中心）；Radiology Department, Lausanne University Hospital (CHUV) and University of Lausanne（放射科，洛桑大学医院（CHUV）和洛桑大学）
Ecole Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；CIBM Center for Biomedical Imaging（CIBM生物医学影像中心）；UCLouvain（鲁汶大学）；Lausanne University and University Hospital（洛桑大学及大学医院）；National Institute of Neurological Disorders and Stroke, National Institutes of Health（美国国立卫生研究院国家神经疾病与卒中研究所）；Cedars-Sinai Medical Center（西达赛奈医疗中心）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Siemens Healthcare AG（西门子医疗股份公司）；Cliniques universitaires Saint-Luc, Université catholique de Louvain（圣吕克大学医院，天主教鲁汶大学）；CHUV（洛桑大学医院）；University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；University Hospital Basel and University of Basel（巴塞尔大学医院及巴塞尔大学）；IRCCS San Raffaele Hospital and Vita-Salute San Raffaele University（圣拉斐尔医院及圣拉斐尔生命健康大学）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）
The current multiple sclerosis (MS) diagnostic criteria lack specificity, and this may lead to misdiagnosis, which remains an issue in present-day clinical practice. In addition, conventional biomarkers only moderately correlate with MS disease progression. Recently, advanced MS lesional imaging biomarkers such as cortical lesions (CL), the central vein sign (CVS), and paramagnetic rim lesions (PRL), visible in specialized magnetic resonance imaging (MRI) sequences, have shown higher specificity in differential diagnosis. Moreover, studies have shown that CL and PRL are potential prognostic bi…
