Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches

Yan Xu; Rixiang Quan; Weiting Xu; Yi Huang; Fengyuan Liu

doi:10.3390/bioengineering11101034

journal article Open Access Oct 16, 2024

Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches

Yan Xu

Rixiang Quan

Weiting Xu Yi Huang

Fengyuan Liu

Bioengineering Vol. 11 No. 10 pp. 1034 · MDPI AG

View at Publisher Save 10.3390/bioengineering11101034

Abstract

Medical image segmentation plays a critical role in accurate diagnosis and treatment planning, enabling precise analysis across a wide range of clinical tasks. This review begins by offering a comprehensive overview of traditional segmentation techniques, including thresholding, edge-based methods, region-based approaches, clustering, and graph-based segmentation. While these methods are computationally efficient and interpretable, they often face significant challenges when applied to complex, noisy, or variable medical images. The central focus of this review is the transformative impact of deep learning on medical image segmentation. We delve into prominent deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, Recurrent Neural Networks (RNNs), Adversarial Networks (GANs), and Autoencoders (AEs). Each architecture is analyzed in terms of its structural foundation and specific application to medical image segmentation, illustrating how these models have enhanced segmentation accuracy across various clinical contexts. Finally, the review examines the integration of deep learning with traditional segmentation methods, addressing the limitations of both approaches. These hybrid strategies offer improved segmentation performance, particularly in challenging scenarios involving weak edges, noise, or inconsistent intensities. By synthesizing recent advancements, this review provides a detailed resource for researchers and practitioners, offering valuable insights into the current landscape and future directions of medical image segmentation.

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Details

Published: Oct 16, 2024
Vol/Issue: 11(10)
Pages: 1034
License: View

Authors

Y

Yan Xu

School of Electrical, Electronic and Mechanical Engineering, University of Bristol, Bristol BS8 1QU, UK

R

Rixiang Quan

School of Electrical, Electronic and Mechanical Engineering, University of Bristol, Bristol BS8 1QU, UK

W

Weiting Xu

School of Electrical, Electronic and Mechanical Engineering, University of Bristol, Bristol BS8 1QU, UK

Y

Yi Huang

Bristol Medical School, University of Bristol, Bristol BS8 1UD, UK

F

Fengyuan Liu

School of Electrical, Electronic and Mechanical Engineering, University of Bristol, Bristol BS8 1QU, UK

Cite This Article

Yan Xu, Rixiang Quan, Weiting Xu, et al. (2024). Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches. Bioengineering, 11(10), 1034. https://doi.org/10.3390/bioengineering11101034

Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches

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