An adaptive sparse Bayesian model combined with probabilistic label fusion for multiple sclerosis lesion segmentation in brain MRI

2020 ◽  
Vol 105 ◽  
pp. 695-704
Author(s):  
Jingjing Wang ◽  
Meiru Liu ◽  
Chunhui Zhang ◽  
Huaqiang Xu ◽  
Liren Zhang ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Bayesian Model ◽  
Brain Mri ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation ◽  
Label Fusion

A novel multi-atlas and multi-channel (MAMC) approach for multiple sclerosis lesion segmentation in brain MRI

2019 ◽  
Vol 13 (5) ◽  
pp. 1019-1027
Author(s):  
Jingjing Wang ◽  
Changjun Hu ◽  
Huaqiang Xu ◽  
Yan Leng ◽  
Liren Zhang ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Brain Mri ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation

scholarly journals Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI

2020 ◽  
Vol 14 ◽  
Author(s):  
Chenyi Zeng ◽  
Lin Gu ◽  
Zhenzhong Liu ◽  
Shen Zhao
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Brain Mri ◽  
Multiple Sclerosis Lesion ◽  
Dice Similarity Coefficient ◽  
Learning Approaches ◽  
Review Of The Literature ◽  
Lesion Segmentation ◽  
Segmentation Methods ◽  
Future Direction

In recent years, there have been multiple works of literature reviewing methods for automatically segmenting multiple sclerosis (MS) lesions. However, there is no literature systematically and individually review deep learning-based MS lesion segmentation methods. Although the previous review also included methods based on deep learning, there are some methods based on deep learning that they did not review. In addition, their review of deep learning methods did not go deep into the specific categories of Convolutional Neural Network (CNN). They only reviewed these methods in a generalized form, such as supervision strategy, input data handling strategy, etc. This paper presents a systematic review of the literature in automated multiple sclerosis lesion segmentation based on deep learning. Algorithms based on deep learning reviewed are classified into two categories through their CNN style, and their strengths and weaknesses will also be given through our investigation and analysis. We give a quantitative comparison of the methods reviewed through two metrics: Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). Finally, the future direction of the application of deep learning in MS lesion segmentation will be discussed.

scholarly journals Multiple Sclerosis Lesion Segmentation in Brain MRI Using Inception Modules Embedded in a Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shahab U. Ansari ◽  
Kamran Javed ◽  
Saeed Mian Qaisar ◽  
Rashad Jillani ◽  
Usman Haider
Keyword(s):  
Neural Network ◽  
Multiple Sclerosis ◽  
Convolutional Neural Network ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Brain Lesion ◽  
Similarity Index ◽  
Structural Similarity ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation

Multiple sclerosis (MS) is a chronic and autoimmune disease that forms lesions in the central nervous system. Quantitative analysis of these lesions has proved to be very useful in clinical trials for therapies and assessing disease prognosis. However, the efficacy of these quantitative analyses greatly depends on how accurately the MS lesions have been identified and segmented in brain MRI. This is usually carried out by radiologists who label 3D MR images slice by slice using commonly available segmentation tools. However, such manual practices are time consuming and error prone. To circumvent this problem, several automatic segmentation techniques have been investigated in recent years. In this paper, we propose a new framework for automatic brain lesion segmentation that employs a novel convolutional neural network (CNN) architecture. In order to segment lesions of different sizes, we have to pick a specific filter or size 3 × 3 or 5 × 5. Sometimes, it is hard to decide which filter will work better to get the best results. Google Net has solved this problem by introducing an inception module. An inception module uses 3 × 3 , 5 × 5 , 1 × 1 and max pooling filters in parallel fashion. Results show that incorporating inception modules in a CNN has improved the performance of the network in the segmentation of MS lesions. We compared the results of the proposed CNN architecture for two loss functions: binary cross entropy (BCE) and structural similarity index measure (SSIM) using the publicly available ISBI-2015 challenge dataset. A score of 93.81 which is higher than the human rater with BCE loss function is achieved.

scholarly journals Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI

2006 ◽  
Vol 34 (1) ◽  
pp. 142-151 ◽  
Author(s):  
Balasrinivasa Rao Sajja ◽  
Sushmita Datta ◽  
Renjie He ◽  
Meghana Mehta ◽  
Rakesh K. Gupta ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Brain Mri ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation ◽  
Unified Approach

scholarly journals Multiple Sclerosis Lesion Segmentation Using Joint Label Fusion

2017 ◽  
pp. 138-145 ◽  
Author(s):  
Mengjin Dong ◽  
Ipek Oguz ◽  
Nagesh Subbana ◽  
Peter Calabresi ◽  
Russell T. Shinohara ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation ◽  
Label Fusion
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