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 sense, we propose a lesion enhancement technique to dim down WM and GM regions and highlight hyperintensities, making them much more distinguishable than other tissues. We applied our technique to the ISBI 2015 MS Lesion Segmentation Challenge and took the average gray level intensity of MS lesions, WM and GM on FLAIR and enhanced images. The lesion intensity profile in FLAIR was on average 25% and 19% brighter than white matter and gray matter, respectively; comparatively, the same profile in our enhanced images was on average 444% and 264% brighter. Such results mean a significant improvement on the intensity distinction among these three clusters, which may come as aid both for experts and automated techniques. Moreover, a byproduct of our proposal is that the enhancement can be used to automatically estimate a mask encompassing WM and MS lesions, which may be useful for brain tissue volume assessment and improve MS lesion segmentation accuracy in future works.
Comparative evaluation of training strategies using partially labelled datasets for segmentation of white matter hyperintensities and stroke lesions in FLAIR MRI
Using ResNet to Utilize 4-class T2-FLAIR Slice Classification Based on the Cholinergic Pathways Hyperintensities Scale for Pathological Aging
Yuan Ze University（元智大学）；Cardinal Tien Hospital（Cardinal Tien医院）；Far Eastern Memorial Hospital（远东纪念医院）；National Yang Ming Chiao Tung University（国立阳明交通大学）
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（伊萨尔河畔临床医院）；University of Dundee（邓迪大学）
Segmentation of both large and small white matter hyperintensities/lesions in brain MR images is a challenging task which has drawn much attention in recent years. We propose a multi-scale aggregation model framework to deal with volume-varied lesions. Firstly, we present a specifically-designed network for small lesion segmentation called Stack-Net, in which multiple convolutional layers are connected, aiming to preserve rich local spatial information of small lesions before the sub-sampling layer. Secondly, we aggregate multi-scale Stack-Nets with different receptive fields to learn multi-sc…
Segmenting white matter hyperintensities on isotropic three-dimensional Fluid Attenuated Inversion Recovery magnetic resonance images: Assessing deep learning tools on norwegian imaging database
University of Oslo（奥斯陆大学）；Akershus University Hospital（阿克什胡斯大学医院）；University of Toronto（多伦多大学）；Sunnybrook Research Institute（桑尼布鲁克研究所）；Vestfold Hospital Trust（西福尔医院信托）
Objective: Multiple Sclerosis (MS) is an autoimmune, and demyelinating disease that leads to lesions in the central nervous system. This disease can be tracked and diagnosed using Magnetic Resonance Imaging (MRI). Up to now a multitude of multimodality automatic biomedical approaches is used to segment lesions which are not beneficial for patients in terms of cost, time, and usability. The authors of the present paper propose a method employing just one modality (FLAIR image) to segment MS lesions accurately. Methods: A patch-based Convolutional Neural Network (CNN) is designed, inspired by 3D…
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…
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…
Sun Yat-sen University（中山大学）；University of Dundee（邓迪大学）；Technical University of Munich（慕尼黑工业大学）
White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. In this paper, we present a study using deep fully convolutional network and ensemble models to automatically detect such WMH using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) scans. The algorithm was evaluated and ranked 1 st in the WMH Segmentation Challenge at MICCAI 2017. In the evaluation stage, the implementation of the algorithm was submitted to the challenge organizers, who then in…
National Institute of Allergy and Infectious Diseases（美国国家过敏和传染病研究所）
Segmentation and quantification of white matter hyperintensities (WMHs) are of great importance in studying and understanding various neurological and geriatric disorders. Although automatic methods have been proposed for WMH segmentation on magnetic resonance imaging (MRI), manual corrections are often necessary to achieve clinically practical results. Major challenges for WMH segmentation stem from their inhomogeneous MRI intensities, random location and size distributions, and MRI noise. The presence of other brain anatomies or diseases with enhanced intensities adds further difficulties. T…
University of Arizona（亚利桑那大学）；Mallinckrodt Institute of Radiology, Washington University（华盛顿大学马林克罗放射学研究所）；University of Utah（犹他大学）
The relationship between cognition and white matter hyperintensities (WMH) volumes often depends on the accuracy of the lesion segmentation algorithm used. As such, accurate detection and quantification of WMH is of great interest. Here, we use a deep learning-based WMH segmentation algorithm, StackGen-Net, to detect and quantify WMH on 3D FLAIR volumes from ADNI. We used a subset of subjects (n=20) and obtained manual WMH segmentations by an experienced neuro-radiologist to demonstrate the accuracy of our algorithm. On a larger cohort of subjects (n=290), we observed that larger WMH volumes c…
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 …
Multiple Sclerosis (MS) is a chronic progressive neurological disease 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. Longitudinal brain FLAIR MRI in MS, involving repetitively imaging a patient over time, provides helpful information for clinicians towards monitoring disease progression. Predicting future whole brain MRI examinations with variable time lag has only been attempte…
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）
We present an edge preserving and denoising filter for enhancing the features in images, which contain an ROI having a narrow spatial extent. Typical examples include angiograms, or ROI spatially distributed in multiple locations and contained within an outlying region, such as in multiple-sclerosis. The filtering involves determination of multiplicative weights in the spatial domain using an extended set of neighborhood directions. Equivalently, the filtering operation may be interpreted as a combination of directional filters in the frequency domain, with selective weighting for spatial freq…
Image Sciences Institute, UMC Utrecht, Utrecht University（乌得勒支大学医学中心（UMC Utrecht）影像科学研究所）；Brain Center Rudolf Magnus, UMC Utrecht, Utrecht University（乌得勒支大学医学中心（UMC Utrecht）鲁道夫·马格努斯脑中心）；Department of Radiology, UMC Utrecht, the Netherlands（乌得勒支大学医学中心放射科（荷兰））；Department of Radiology, LUMC, Leiden, the Netherlands（莱顿大学医学中心（LUMC）放射科（荷兰））；Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland（巴塞尔大学生物医学工程系（瑞士奥尔施维尔））；Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, AB, Canada（卡尔加里大学（加拿大阿尔伯塔省）霍奇基斯脑研究所放射学与临床神经科学部）；NLP Logix；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）；School of Biomedical Engineering and Im（生物医学工程与...学院（原文未完整显示））
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge …
Multiple Sclerosis (MS) and microvascular leukoencephalopathy are two distinct neurological conditions, the first caused by focal autoimmune inflammation in the central nervous system, the second caused by chronic white matter damage from atherosclerotic microvascular disease. Both conditions lead to signal anomalies on Fluid Attenuated Inversion Recovery (FLAIR) magnetic resonance (MR) images, which can be distinguished by an expert neuroradiologist, but which can look very similar to the untrained eye as well as in the early stage of both diseases. In this paper, we attempt to train a 3-dime…
White Matter Hyperintensities (WMH) are the most common manifestation of cerebral small vessel disease (cSVD) on the brain MRI. Accurate WMH segmentation algorithms are important to determine cSVD burden and its clinical consequences. Most of existing WMH segmentation algorithms require both fluid attenuated inversion recovery (FLAIR) images and T1-weighted images as inputs. However, T1-weighted images are typically not part of standard clinicalscans which are acquired for patients with acute stroke. In this paper, we propose a novel brain atlas guided attention U-Net (BAGAU-Net) that leverage…
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）
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…
Efficient Transformer-Based Localized Patch Sampling for Choroid Plexus Segmentation in Multiple Sclerosis
University Hospital Basel（巴塞尔大学医院）；University of Basel（巴塞尔大学）；Università degli Studi di Genova（热那亚大学）；University of Verona（维罗纳大学）；Imperial College London（帝国理工学院）
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）
QSM-RimDS: A detection and segmentation tool for paramagnetic rim lesions in multiple sclerosis
Weill Cornell Medicine（威尔康奈尔医学院）；Cornell University（康奈尔大学）；Yale University（耶鲁大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；University of Engineering and Technology, VNU（越南国家大学工程技术大学）
Lesion-DDPM: Lesion-Enhanced 3D Diffusion for MS MRI Synthesis
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；National Taiwan University Hospital（国立台湾大学医院）；National University of Mongolia（蒙古国立大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Modality-Agnostic Input Channels Enable Segmentation of Brain lesions in Multimodal MRI with Sequences Unavailable During Training
Department of Engineering Science, University of Oxford, Oxford, UK（工程科学系，牛津大学，牛津，英国）；Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK（牛津大学临床神经科学系，牛津，英国）
