Region Uncertainty Estimation for Medical Image Segmentation With Noisy Labels
文献类型：期刊论文
| 作者 | Han, Kai2; Wang, Shuhui3; Chen, Jun2; Qian, Chengxuan2; Lyu, Chongwen2; Ma, Siqi2; Qiu, Chengjian4; Sheng, Victor S.1; Huang, Qingming5; Liu, Zhe2 |
| 刊名 | IEEE TRANSACTIONS ON MEDICAL IMAGING
 |
| 出版日期 | 2025-12-01 |
| 卷号 | 44期号:12页码:5197-5207 |
| 关键词 | Noise measurement Training Image segmentation Annotations Uncertainty Estimation Medical diagnostic imaging Data models Computational modeling Noise Medical image segmentation noisy label learning uncertainty estimation pseudo label |
| ISSN号 | 0278-0062 |
| DOI | 10.1109/TMI.2025.3589058 |
| 英文摘要 | The success of deep learning in 3D medical image segmentation hinges on training with a large dataset of fully annotated 3D volumes, which are difficult and time-consuming to acquire. Although recent foundation models (e.g., segment anything model, SAM) can utilize sparse annotations to reduce annotation costs, segmentation tasks involving organs and tissues with blurred boundaries remain challenging. To address this issue, we propose a region uncertainty estimation framework for Computed Tomography (CT) image segmentation using noisy labels. Specifically, we propose a sample-stratified training strategy that stratifies samples according to their varying quality labels, prioritizing confident and fine-grained information at each training stage. This sample-to-voxel level processing enables more reliable supervision information to propagate to noisy label data, thus effectively mitigating the impact of noisy annotations. Moreover, we further design a boundary-guided regional uncertainty estimation module that adapts sample hierarchical training to assist in evaluating sample confidence. Experiments conducted across multiple CT datasets demonstrate the superiority of our proposed method over several competitive approaches under various noise conditions. Our proposed reliable label propagation strategy not only significantly reduces the cost of medical image annotation and robust model training but also improves the segmentation performance in scenarios with imperfect annotations, thus paving the way towards the application of medical segmentation foundation models under low-resource and remote scenarios. Code will be available at https://github.com/KHan-UJS/NoisyLabel |
| 资助项目 | National Natural Science Foundation of China[62276116] ; Jiangsu Graduate Research Innovation Program[KY-CX23_3677] ; National Undergraduate Training Program on Innovation and Entrepreneurship[202410299049Z] |
| WOS研究方向 | Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging |
| 语种 | 英语 |
| WOS记录号 | WOS:001631860200019 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42925]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Liu, Zhe |
| 作者单位 | 1.Texas Tech Univ, Dept Comp Sci, Lubbock, TX 79409 USA 2.Jiangsu Univ, Sch Comp Sci & Commun Engn, Zhenjiang 212013, Peoples R China 3.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China 4.Huaiyin Normal Univ, Sch Comp Sci & Technol, Huaian 223399, Peoples R China 5.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China |
| 推荐引用方式 GB/T 7714 | Han, Kai,Wang, Shuhui,Chen, Jun,et al. Region Uncertainty Estimation for Medical Image Segmentation With Noisy Labels[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2025,44(12):5197-5207. |
| APA | Han, Kai.,Wang, Shuhui.,Chen, Jun.,Qian, Chengxuan.,Lyu, Chongwen.,...&Liu, Zhe.(2025).Region Uncertainty Estimation for Medical Image Segmentation With Noisy Labels.IEEE TRANSACTIONS ON MEDICAL IMAGING,44(12),5197-5207. |
| MLA | Han, Kai,et al."Region Uncertainty Estimation for Medical Image Segmentation With Noisy Labels".IEEE TRANSACTIONS ON MEDICAL IMAGING 44.12(2025):5197-5207. |
入库方式： OAI收割
来源：计算技术研究所
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