Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular metho...

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Autores principales: Hesamian, Mohammad Hesam, Jia, Wenjing, He, Xiangjian, Kennedy, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6646484/
https://www.ncbi.nlm.nih.gov/pubmed/31144149
http://dx.doi.org/10.1007/s10278-019-00227-x
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author Hesamian, Mohammad Hesam
Jia, Wenjing
He, Xiangjian
Kennedy, Paul
author_facet Hesamian, Mohammad Hesam
Jia, Wenjing
He, Xiangjian
Kennedy, Paul
author_sort Hesamian, Mohammad Hesam
collection PubMed
description Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.
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spelling pubmed-66464842019-08-06 Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges Hesamian, Mohammad Hesam Jia, Wenjing He, Xiangjian Kennedy, Paul J Digit Imaging Article Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions. Springer International Publishing 2019-05-29 2019-08 /pmc/articles/PMC6646484/ /pubmed/31144149 http://dx.doi.org/10.1007/s10278-019-00227-x Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Article
Hesamian, Mohammad Hesam
Jia, Wenjing
He, Xiangjian
Kennedy, Paul
Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
title Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
title_full Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
title_fullStr Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
title_full_unstemmed Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
title_short Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
title_sort deep learning techniques for medical image segmentation: achievements and challenges
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6646484/
https://www.ncbi.nlm.nih.gov/pubmed/31144149
http://dx.doi.org/10.1007/s10278-019-00227-x
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