人工智能
标杆管理
分割
计算机科学
图像分割
结肠镜检查
计算机视觉
窄带成像
像素
深度学习
模式识别(心理学)
医学
放射科
结直肠癌
内窥镜检查
癌症
内科学
营销
业务
作者
Guanghui Yue,Guibin Zhuo,Siying Li,Tianwei Zhou,Jingfeng Du,Weiqing Yan,Jingwen Hou,Weide Liu,Tianfu Wang
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-07-01
卷期号:27 (7): 3360-3371
被引量:2
标识
DOI:10.1109/jbhi.2023.3270724
摘要
In recent years, there has been significant progress in polyp segmentation in white-light imaging (WLI) colonoscopy images, particularly with methods based on deep learning (DL). However, little attention has been paid to the reliability of these methods in narrow-band imaging (NBI) data. NBI improves visibility of blood vessels and helps physicians observe complex polyps more easily than WLI, but NBI images often include polyps with small/flat appearances, background interference, and camouflage properties, making polyp segmentation a challenging task. This paper proposes a new polyp segmentation dataset (PS-NBI2K) consisting of 2,000 NBI colonoscopy images with pixel-wise annotations, and presents benchmarking results and analyses for 24 recently reported DL-based polyp segmentation methods on PS-NBI2K. The results show that existing methods struggle to locate polyps with smaller sizes and stronger interference, and that extracting both local and global features improves performance. There is also a trade-off between effectiveness and efficiency, and most methods cannot achieve the best results in both areas simultaneously. This work highlights potential directions for designing DL-based polyp segmentation methods in NBI colonoscopy images, and the release of PS-NBI2K aims to drive further development in this field.
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