A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866929681126653952 |
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| author | Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu |
| author_facet | Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu |
| contents | 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 issues capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more outstanding medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in this 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, then detail benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp sizes, considering the pain points of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in this field. The models, benchmark datasets, and source code links we collected are all published at https://github.com/taozh2017/Awesome-Polyp-Segmentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_18373 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu Computer Vision and Pattern Recognition 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 issues capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more outstanding medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in this 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, then detail benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp sizes, considering the pain points of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in this field. The models, benchmark datasets, and source code links we collected are all published at https://github.com/taozh2017/Awesome-Polyp-Segmentation. |
| title | A Survey on Deep Learning for Polyp Segmentation: Techniques, Challenges and Future Trends |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.18373 |