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A Review of Deep Learning‐Based Medical Image Segmentation

Xinyue Zhang Orcid Logo, Jianfeng Wang, Cheng Cheng Orcid Logo, Junran Li

IET Image Processing, Volume: 19, Issue: 1

Swansea University Author: Cheng Cheng Orcid Logo

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DOI (Published version): 10.1049/ipr2.70163

Abstract

Medical image segmentation, the process of precisely delineating regions of interest (e.g. organs, lesions, cells) within medical images, is a pivotal technique in medical image analysis. It finds widespread application in computer-aided diagnosis, surgical planning, radiation therapy, and pathologi...

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Published in: IET Image Processing
ISSN: 1751-9659 1751-9667
Published: Institution of Engineering and Technology (IET) 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa69929
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spelling 2025-07-28T14:01:56.8751723 v2 69929 2025-07-09 A Review of Deep Learning‐Based Medical Image Segmentation 11ddf61c123b99e59b00fa1479367582 0000-0003-0371-9646 Cheng Cheng Cheng Cheng true false 2025-07-09 MACS Medical image segmentation, the process of precisely delineating regions of interest (e.g. organs, lesions, cells) within medical images, is a pivotal technique in medical image analysis. It finds widespread application in computer-aided diagnosis, surgical planning, radiation therapy, and pathological analysis, thus playing a crucial role in enabling precision medicine and enhancing the quality of clinical care. Traditional medical image segmentation methods often rely on hand-crafted features and rule-based approaches, which struggle to handle the inherent complexity and variability of medical imagery, leading to limitations in segmentation accuracy and robustness. Recently, deep learning methodologies, driven by their powerful capabilities in automatic feature learning and non-linear modelling, have overcome the limitations of traditional methods and achieved significant advancements in the field of medical image segmentation. This review provides a comprehensive overview and summary of recent progress in deep learning-based medical image segmentation, with a particular focus on fully supervised learning paradigms leveraging convolutional neural networks, transformers, and the segment anything model. We delve into the underlying principles, network architectures, advantages, and limitations of these approaches. Furthermore, we systematically compare their performance across diverse imaging modalities, anatomical structures, and pathological targets. We also present a curated compilation of commonly used datasets, evaluation metrics, and loss functions relevant to medical image segmentation. Finally, we discuss future research directions and potential challenges, offering insights into the evolving landscape of this critical field. Journal Article IET Image Processing 19 1 Institution of Engineering and Technology (IET) 1751-9659 1751-9667 medical image segmentation; fully-supervised learning; survey 1 1 2025 2025-01-01 10.1049/ipr2.70163 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University SU Library paid the OA fee (TA Institutional Deal) This work was supported by UKRI EPSRC Grant funded Doctoral Training Centre at Swansea University, through PhD project RS718 on Explainable AI. Authors also have been supported by UKRI EPSRC Grant EP/W020408/1 Project SPRITE+ 2: The Security, Privacy, Identity and Trust Engagement Network plus (phase 2). 2025-07-28T14:01:56.8751723 2025-07-09T14:40:14.8648726 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Xinyue Zhang 0009-0003-5832-8148 1 Jianfeng Wang 2 Cheng Cheng 0000-0003-0371-9646 3 Junran Li 4 69929__34852__e5969ec0b1bf47e284ad0c2b64989a9c.pdf 69929.VoR.pdf 2025-07-28T13:59:29.2312432 Output 3438699 application/pdf Version of Record true © 2025 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0 ). true eng http://creativecommons.org/licenses/by/4.0/
title A Review of Deep Learning‐Based Medical Image Segmentation
spellingShingle A Review of Deep Learning‐Based Medical Image Segmentation
Cheng Cheng
title_short A Review of Deep Learning‐Based Medical Image Segmentation
title_full A Review of Deep Learning‐Based Medical Image Segmentation
title_fullStr A Review of Deep Learning‐Based Medical Image Segmentation
title_full_unstemmed A Review of Deep Learning‐Based Medical Image Segmentation
title_sort A Review of Deep Learning‐Based Medical Image Segmentation
author_id_str_mv 11ddf61c123b99e59b00fa1479367582
author_id_fullname_str_mv 11ddf61c123b99e59b00fa1479367582_***_Cheng Cheng
author Cheng Cheng
author2 Xinyue Zhang
Jianfeng Wang
Cheng Cheng
Junran Li
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container_volume 19
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publishDate 2025
institution Swansea University
issn 1751-9659
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doi_str_mv 10.1049/ipr2.70163
publisher Institution of Engineering and Technology (IET)
college_str Faculty of Science and Engineering
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hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description Medical image segmentation, the process of precisely delineating regions of interest (e.g. organs, lesions, cells) within medical images, is a pivotal technique in medical image analysis. It finds widespread application in computer-aided diagnosis, surgical planning, radiation therapy, and pathological analysis, thus playing a crucial role in enabling precision medicine and enhancing the quality of clinical care. Traditional medical image segmentation methods often rely on hand-crafted features and rule-based approaches, which struggle to handle the inherent complexity and variability of medical imagery, leading to limitations in segmentation accuracy and robustness. Recently, deep learning methodologies, driven by their powerful capabilities in automatic feature learning and non-linear modelling, have overcome the limitations of traditional methods and achieved significant advancements in the field of medical image segmentation. This review provides a comprehensive overview and summary of recent progress in deep learning-based medical image segmentation, with a particular focus on fully supervised learning paradigms leveraging convolutional neural networks, transformers, and the segment anything model. We delve into the underlying principles, network architectures, advantages, and limitations of these approaches. Furthermore, we systematically compare their performance across diverse imaging modalities, anatomical structures, and pathological targets. We also present a curated compilation of commonly used datasets, evaluation metrics, and loss functions relevant to medical image segmentation. Finally, we discuss future research directions and potential challenges, offering insights into the evolving landscape of this critical field.
published_date 2025-01-01T05:29:58Z
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