Tanya Nair, Doina Precup, Douglas L. Arnold, Tal Arbel
备注：
Updated references in Introduction; Accepted to the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018)
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 present the first exploration of multiple uncertainty estimates based on Monte Carlo (MC) dropout [4] in the context of deep networks for lesion detection and segmentation in medical images. Specifically, we develop a 3D MS lesion segmentation CNN, augmented to provide four different voxel-based uncertainty measures based on MC dropout. We train the network on a proprietary, large-scale, multi-site, multi-scanner, clinical MS dataset, and compute lesion-wise uncertainties by accumulating evidence from voxel-wise uncertainties within detected lesions. We analyze the performance of voxel-based segmentation and lesion-level detection by choosing operating points based on the uncertainty. Empirical evidence suggests that uncertainty measures consistently allow us to choose superior operating points compared only using the network's sigmoid output as a probability.
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…
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 compariso…
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…
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…
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…
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…
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 …
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）
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）
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…
University of Bern（伯尔尼大学）；Inselspital, Bern University Hospital（伯尔尼大学医院 Inselspital）；Universitätsklinik Balgrist（巴尔格里斯特大学医院）
The detection of new or enlarged white-matter lesions in multiple sclerosis is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-counting is highly subjective, with high degree of inter- and intra-rater variability. Automated methods for lesion quantification hold the potential to make the detection of new and enlarged lesions consistent and repeatable. However, the majority of lesion segmentation algorithms are not evaluated for their ability to separa…
Multiple sclerosis (MS) is a demyelinating disease that affects more than 2 million people worldwide. The most used imaging technique to help in its diagnosis and follow-up is magnetic resonance imaging (MRI). Fluid Attenuated Inversion Recovery (FLAIR) images are usually acquired in the context of MS because lesions often appear hyperintense in this particular image weight, making it easier for physicians to identify them. Though lesions have a bright intensity profile, it may overlap with white matter (WM) and gray matter (GM) tissues, posing difficulties to be accurately segmented. In this …
Longitudinal tracking of multiple sclerosis lesions in the spinal cord: A validation study
Polytechnique Montreal（蒙特利尔理工学院）；TUM University Hospital（慕尼黑工业大学附属医院）；University of British Columbia（不列颠哥伦比亚大学）；University of Calgary（卡尔加里大学）；University of Toronto（多伦多大学）；Université de Montréal（蒙特利尔大学）；University of Alberta（阿尔伯塔大学）
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…
Istituto Italiano di Tecnologia（意大利理工学院）；University of Genoa（热那亚大学）；Fondation Campus Biotech Geneva（日内瓦校园生物技术基金会）；San Raffaele Scientific Institute（圣拉斐尔科学研究所）；University of Verona（维罗纳大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our method is based on a deep end-to-end 2D convolutional neural network (CNN) for slice-based segmentation of 3D volumetric data. The proposed CNN includes a multi-branch downsampling path, which enables the network to encode information from multiple modalities separately. Multi-scale feature fusion blocks are proposed to combine feature maps from different modalities at different stages of the network. Then, multi-scale feature upsampling blocks ar…
Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation
University of Lausanne（洛桑大学）；Lausanne University Hospital（洛桑大学医院）；HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland（瑞士西部高等专业学院瓦莱州瓦利斯分校）；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；ALTA Institute, University of Cambridge（剑桥大学ALTA研究所）；Yandex Research（Yandex研究院）；Isomorphic Labs（同构实验室）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；University Hospital Basel and University of Basel（巴塞尔大学医院与巴塞尔大学）
University of Girona（赫罗纳大学）；Vall d’Hebron University Hospital（巴尔德希伯伦大学医院）；Girona Magnetic Resonance Center（赫罗纳磁共振中心）；Dr. Josep Trueta University Hospital（若泽普·特鲁埃塔博士大学医院）
In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from small sets of training data, which can be very interesting in practice, given the difficulty to obtain manual …
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…
Inselspital, Bern University Hospital（伯尔尼大学医院（Inselspital））；Spital Tiefenau（Tiefenau医院）；Medizinisch Radiologisches Institut（医学放射学研究所）；University of Bern（伯尔尼大学）
Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper we explore segmentation solutions based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of lesions and grey-matter structures in multi-modal MR imaging, and the performance of these methods when applied to out-of-centre data. We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters:…
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…
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 …
University of Girona（赫罗纳大学）；Assiut University（艾斯尤特大学）；Vall d’Hebron University Hospital（瓦尔德希伯伦大学医院）；Girona Magnetic Resonance Center（赫罗纳磁共振中心）；Dr. Josep Trueta University Hospital（何塞普·特鲁埃塔大学医院）
In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evaluated on different image domains compared with those used for training, which demonstrates the lack of adaptability of CNNs to unseen imaging data. In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based…
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…
SegHeD+: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints and Lesion-aware Augmentation
Imperial College London（帝国理工学院）；Data Science Institute, Imperial College London（帝国理工学院数据科学研究所）；UK Dementia Research Institute, Imperial College London（帝国理工学院英国痴呆症研究所）；Rosalind Franklin Institute（罗莎琳德·富兰克林研究所）
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…
Boston Children's Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）；Northeastern University（东北大学）
Fully convolutional deep neural networks have been asserted to be fast and precise frameworks with great potential in image segmentation. One of the major challenges in training such networks raises when data is unbalanced, which is common in many medical imaging applications such as lesion segmentation where lesion class voxels are often much lower in numbers than non-lesion voxels. A trained network with unbalanced data may make predictions with high precision and low recall, being severely biased towards the non-lesion class which is particularly undesired in most medical applications where…
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…
Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of Lausanne（洛桑大学）；Siemens Healthcare AG（西门子医疗股份公司）；Lausanne University Hospital（洛桑大学医院）；University Hospital Basel（巴塞尔大学医院）；University of Basel（巴塞尔大学）
In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients. In particular, we train and test both methods on early stage disease patients, to verify their performance in challenging conditions, more similar to a clinical setting than what is typically provided in multiple sclerosis segmentation challenges. Furthermore, we evaluate a prototype naive combination of the two methods, which refines the final segmentation. All methods were trained on 32 patients, and the evalua…
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…
Shanghai AI Lab（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；University of Surrey（萨里大学）；University of Electronic Science and Technology of China（电子科技大学）；Tongji University（同济大学）
Tumor lesion segmentation is one of the most important tasks in medical image analysis. In clinical practice, Fluorodeoxyglucose Positron-Emission Tomography~(FDG-PET) is a widely used technique to identify and quantify metabolically active tumors. However, since FDG-PET scans only provide metabolic information, healthy tissue or benign disease with irregular glucose consumption may be mistaken for cancer. To handle this challenge, PET is commonly combined with Computed Tomography~(CT), with the CT used to obtain the anatomic structure of the patient. The combination of PET-based metabolic and…
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）
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 …
Multiple sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system (CNS) that results in focal injury to the grey and white matter. The presence of white matter lesions biases morphometric analyses such as registration, individual longitudinal measurements and tissue segmentation for brain volume measurements. Lesion-inpainting with intensities derived from surrounding healthy tissue represents one approach to alleviate such problems. However, existing methods inpaint lesions based on texture information derived from local surrounding tissue, often leading to incons…
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…
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）
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）；MILA, Quebec AI Institute（魁北克人工智能研究所MILA）；CHU Rennes（雷恩大学中心医院）；CHU Montpellier（蒙彼利埃大学中心医院）；Karolinska Institutet（卡罗林斯卡学院）；University of California, San Francisco（加州大学旧金山分校）；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中风研究所）；IRCCS San Raffaele Scientific Institute（IRCCS圣拉斐尔科学研究所）；Vita-Salute San Raffaele University（圣拉斐尔生命健康大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；AP-HM, CHU Timone（马赛公共医院集团蒂莫内大学中心医院）；Aix-Marseille Univ（艾克斯-马赛大学）；CNRS（法国国家科学研究中心）
Segmentation of Multiple Sclerosis (MS) lesions is a challenging problem. Several deep-learning-based methods have been proposed in recent years. However, most methods tend to be static, that is, a single model trained on a large, specialized dataset, which does not generalize well. Instead, the model should learn across datasets arriving sequentially from different hospitals by building upon the characteristics of lesions in a continual manner. In this regard, we explore experience replay, a well-known continual learning method, in the context of MS lesion segmentation across multi-contrast d…
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 …
Polytechnique Montreal（蒙特利尔理工学院）；University of Montreal（蒙特利尔大学）；Zuckerberg San Francisco General Hospital（扎克伯格旧金山综合医院）；University of California（加利福尼亚大学）；Montreal Neurological Institute（蒙特利尔神经学研究所）；Capital Medical University（首都医科大学）；Xuanwu Hospital（宣武医院）；Beijing Tiantan Hospital（北京天坛医院）；Karolinska Institutet（卡罗林斯卡学院）；Massachusetts General Hospital（麻省总医院）；CHU Rennes（雷恩大学医学中心）；Univ Rennes（雷恩大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；IRISA（法国计算机与随机系统研究所）；University Hospital of Montpellier（蒙彼利埃大学医院）
The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. The goal of this study…
