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Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI
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 i...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7714963/ https://www.ncbi.nlm.nih.gov/pubmed/33328949 http://dx.doi.org/10.3389/fninf.2020.610967 |
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author | Zeng, Chenyi Gu, Lin Liu, Zhenzhong Zhao, Shen |
author_facet | Zeng, Chenyi Gu, Lin Liu, Zhenzhong Zhao, Shen |
author_sort | Zeng, Chenyi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7714963 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77149632020-12-15 Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI Zeng, Chenyi Gu, Lin Liu, Zhenzhong Zhao, Shen Front Neuroinform Neuroscience 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. Frontiers Media S.A. 2020-11-20 /pmc/articles/PMC7714963/ /pubmed/33328949 http://dx.doi.org/10.3389/fninf.2020.610967 Text en Copyright © 2020 Zeng, Gu, Liu and Zhao. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Zeng, Chenyi Gu, Lin Liu, Zhenzhong Zhao, Shen Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI |
title | Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI |
title_full | Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI |
title_fullStr | Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI |
title_full_unstemmed | Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI |
title_short | Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI |
title_sort | review of deep learning approaches for the segmentation of multiple sclerosis lesions on brain mri |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7714963/ https://www.ncbi.nlm.nih.gov/pubmed/33328949 http://dx.doi.org/10.3389/fninf.2020.610967 |
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