Circular Non-uniform Sampling Patch Inputs for CNN Applied to Multiple Sclerosis Lesion Segmentation

2019 ◽  
pp. 673-680
Author(s):  
Gustavo Ulloa ◽  
Rodrigo Naranjo ◽  
Héctor Allende-Cid ◽  
Steren Chabert ◽  
Héctor Allende
Keyword(s):  
Multiple Sclerosis ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation ◽  
Uniform Sampling ◽  
Non Uniform Sampling

An Efficient Algorithm for Multiple Sclerosis Lesion Segmentation from Brain MRI

2003 ◽  
pp. 542-551 ◽  
Author(s):  
Rubén Cárdenes ◽  
Simon K. Warfield ◽  
Elsa M. Macías ◽  
Jose Aurelio Santana ◽  
Juan Ruiz-Alzola
Keyword(s):  
Multiple Sclerosis ◽  
Efficient Algorithm ◽  
Brain Mri ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation

Joint MRI T1 Unenhancing and Contrast-enhancing Multiple Sclerosis Lesion Segmentation with Deep Learning in OPERA Trials

Radiology ◽  
2021 ◽  
Author(s):  
Anitha Priya Krishnan ◽  
Zhuang Song ◽  
David Clayton ◽  
Laura Gaetano ◽  
Xiaoming Jia ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation

Level set evolution with intensity prior knowledge for multiple sclerosis lesion segmentation

2019 ◽  
Vol 5 (1) ◽  
pp. 37
Author(s):  
Guodong Zhang ◽  
Zhaoxuan Gong ◽  
Wei Guo ◽  
Zhenyu Zhu ◽  
Jia Guo ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Prior Knowledge ◽  
Level Set ◽  
Multiple Sclerosis Lesion ◽  
Lesion Segmentation ◽  
Level Set Evolution

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

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

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.

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