Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmenta...

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Autores principales:	Reza Kalantar, Gigin Lin, Jessica M. Winfield, Christina Messiou, Susan Lalondrelle, Matthew D. Blackledge, Dow-Mu Koh
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning pelvic cancer segmentation radiology radiation oncology radiotherapy planning Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/7aff8e47b4f149db9b8f67a8f0fb6c4f
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spelling	oai:doaj.org-article:7aff8e47b4f149db9b8f67a8f0fb6c4f2021-11-25T17:20:17ZAutomatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges10.3390/diagnostics111119642075-4418https://doaj.org/article/7aff8e47b4f149db9b8f67a8f0fb6c4f2021-10-01T00:00:00Zhttps://www.mdpi.com/2075-4418/11/11/1964https://doaj.org/toc/2075-4418The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.Reza KalantarGigin LinJessica M. WinfieldChristina MessiouSusan LalondrelleMatthew D. BlackledgeDow-Mu KohMDPI AGarticledeep learningpelvic cancer segmentationradiologyradiation oncologyradiotherapy planningMedicine (General)R5-920ENDiagnostics, Vol 11, Iss 1964, p 1964 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning pelvic cancer segmentation radiology radiation oncology radiotherapy planning Medicine (General) R5-920
spellingShingle	deep learning pelvic cancer segmentation radiology radiation oncology radiotherapy planning Medicine (General) R5-920 Reza Kalantar Gigin Lin Jessica M. Winfield Christina Messiou Susan Lalondrelle Matthew D. Blackledge Dow-Mu Koh Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges
description	The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.
format	article
author	Reza Kalantar Gigin Lin Jessica M. Winfield Christina Messiou Susan Lalondrelle Matthew D. Blackledge Dow-Mu Koh
author_facet	Reza Kalantar Gigin Lin Jessica M. Winfield Christina Messiou Susan Lalondrelle Matthew D. Blackledge Dow-Mu Koh
author_sort	Reza Kalantar
title	Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges
title_short	Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges
title_full	Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges
title_fullStr	Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges
title_full_unstemmed	Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges
title_sort	automatic segmentation of pelvic cancers using deep learning: state-of-the-art approaches and challenges
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7aff8e47b4f149db9b8f67a8f0fb6c4f
work_keys_str_mv	AT rezakalantar automaticsegmentationofpelviccancersusingdeeplearningstateoftheartapproachesandchallenges AT giginlin automaticsegmentationofpelviccancersusingdeeplearningstateoftheartapproachesandchallenges AT jessicamwinfield automaticsegmentationofpelviccancersusingdeeplearningstateoftheartapproachesandchallenges AT christinamessiou automaticsegmentationofpelviccancersusingdeeplearningstateoftheartapproachesandchallenges AT susanlalondrelle automaticsegmentationofpelviccancersusingdeeplearningstateoftheartapproachesandchallenges AT matthewdblackledge automaticsegmentationofpelviccancersusingdeeplearningstateoftheartapproachesandchallenges AT dowmukoh automaticsegmentationofpelviccancersusingdeeplearningstateoftheartapproachesandchallenges
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Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

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