Knowledge Commons of Institute of Automation,CAS
Automatic polyp segmentation via image-level and surrounding-level context fusion deep neural network | |
Wang, Changwei1,3![]() ![]() ![]() ![]() ![]() | |
发表期刊 | Engineering Applications of Artificial Intelligence
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2023 | |
卷号 | 123期号:2023页码:106168 |
摘要 | More than 95% of colorectal cancers are gradually transformed from polyps, so regular colonoscopy polyp examination plays an important role in cancer prevention and early treatment. However, automatic polyp segmentation remains a challenging task due to the low-contrast tissue environment and the small size and variety (e.g., shape, color, texture) of polyps. In this case, the rich context information in colonoscopy images is worth exploring to address the above issues. On the one hand, the image-level context with a global receptive field can be used to enhance the discrimination between the foreground and the background to alleviate the occult and indistinguishability of polyps in colonoscopy images. On the other hand, the surrounding-level context focused on the surrounding pathological region of the polyp has more detailed features that are beneficial for polyp segmentation. Therefore, we propose a novel network named ISCNet that aims to fuse image-level and surrounding-level context information for polyp segmentation. Specifically, we first introduce the Global-Guided Context Aggregation (GGCA) module to explicitly model the foreground and background of polyp segmentation through image-level context, thereby flexibly enhancing polyp-related features and suppressing background-related features. Then, we design the Diverse Surrounding Context Focus (DSCF) module to focus on the surrounding area of the polyp to extract diverse local contexts to refine the segmentation results. Finally, we fuse the feature maps derived from these two modules so that our ISCNet can enjoy the facilitation of both the image-level and surrounding-level context information. To verify the effectiveness of our method, we conduct comprehensive experimental evaluations on three challenging datasets. The quantitative and qualitative experimental results confirm that our ISCNet outperforms current state-of-the-art methods by a large margin. Our code is available at https://github.com/vvmedical/ISCNet. |
关键词 | Context information fusion Colonoscopy Polyp segmentation Image-level and surrounding-level context |
学科门类 | 工学 |
DOI | https://doi.org/10.1016/j.engappai.2023.106168 |
URL | 查看原文 |
收录类别 | SCI |
WOS记录号 | WOS:001008843700001 |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51579 |
专题 | 多模态人工智能系统全国重点实验室 多模态人工智能系统全国重点实验室_三维可视计算 |
通讯作者 | Wang, Changwei |
作者单位 | 1.MAIS, Institute of Automation, Chinese Academy of Sciences, China 2.School of Artificial Intelligence, Beijing University of Posts and Telecommunications, China 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wang, Changwei,Xu, Rongtao,Xu, Shibiao,et al. Automatic polyp segmentation via image-level and surrounding-level context fusion deep neural network[J]. Engineering Applications of Artificial Intelligence,2023,123(2023):106168. |
APA | Wang, Changwei,Xu, Rongtao,Xu, Shibiao,Meng, Weiliang,&Zhang, Xiaopeng.(2023).Automatic polyp segmentation via image-level and surrounding-level context fusion deep neural network.Engineering Applications of Artificial Intelligence,123(2023),106168. |
MLA | Wang, Changwei,et al."Automatic polyp segmentation via image-level and surrounding-level context fusion deep neural network".Engineering Applications of Artificial Intelligence 123.2023(2023):106168. |
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Automatic polyp segm(2131KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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