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Review

A survey on deep Learning for polyp segmentation: techniques, challenges and future trends

Jiaxin Mei1,2, Tao Zhou1,2( ), Kaiwen Huang1,2, Yizhe Zhang1,2, Yi Zhou3, Ye Wu1,2, Huazhu Fu4
PCA Lab, Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Nanjing, China
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
School of Computer Science and Engineering, Southeast University, Nanjing, China
Institute of High Performance Computing, A*STAR, Singapore, Singapore
Show Author Information

Abstract

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field.

Keywords

Polyp segmentationDeep learningComprehensive evaluationMedical imaging

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Visual Intelligence
Volume 3,
2025
Article number: 1
DOI: 10.1007/s44267-024-00071-w
Cite this article:
Mei J, Zhou T, Huang K, et al. A survey on deep Learning for polyp segmentation: techniques, challenges and future trends. Visual Intelligence, 2025, 3: 1. https://doi.org/10.1007/s44267-024-00071-w

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Received: 07 June 2024
Revised: 19 December 2024
Accepted: 19 December 2024
Published: 03 January 2025
© The Author(s) 2025
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