Academic Journal
A survey on deep learning for polyp segmentation: techniques, challenges and future trends
| Title: | A survey on deep learning for polyp segmentation: techniques, challenges and future trends |
|---|---|
| Authors: | Jiaxin Mei, Tao Zhou, Kaiwen Huang, Yizhe Zhang, Yi Zhou, Ye Wu, Huazhu Fu |
| Source: | Visual Intelligence, Vol 3, Iss 1, Pp 1-20 (2025) |
| Publisher Information: | Springer, 2025. |
| Publication Year: | 2025 |
| Collection: | LCC:Electronic computers. Computer science LCC:Neurophysiology and neuropsychology |
| Subject Terms: | Polyp segmentation, Deep learning, Comprehensive evaluation, Medical imaging, Electronic computers. Computer science, QA75.5-76.95, Neurophysiology and neuropsychology, QP351-495 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2097-3330 2731-9008 |
| Relation: | https://doaj.org/toc/2097-3330; https://doaj.org/toc/2731-9008 |
| DOI: | 10.1007/s44267-024-00071-w |
| Access URL: | https://doaj.org/article/94e683176c724ee5aac950da342fa5a0 |
| Accession Number: | edsdoj.94e683176c724ee5aac950da342fa5a0 |
| Database: | Directory of Open Access Journals |
| ISSN: | 20973330 27319008 |
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| DOI: | 10.1007/s44267-024-00071-w |
| Published in: | Visual Intelligence |
| Language: | English |