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Deep self-cleansing for medical image segmentation with noisy labels

文献类型：期刊论文

作者Dong, Jiahua2; Zhang, Yue1,3; Wang, Qiuli4; Tong, Ruofeng2; Ying, Shihong5; Gong, Shaolin5; Zhang, Xuanpu5; Lin, Lanfen2; Chen, Yen-Wei6; Zhou, Shaohua Kevin3,7,8,9
刊名MEDICAL PHYSICS
出版日期2025-09-22
卷号52期号:10页码:17
关键词label self-cleansing medical image segmentation noisy labels
ISSN号0094-2405
DOI10.1002/mp.70007
英文摘要Background Medical image segmentation plays a pivotal role in medical imaging, significantly contributing to disease diagnosis and surgical planning. Traditional segmentation methods predominantly rely on supervised deep learning, where the accuracy of manually delineated labels is crucial for model performance. However, these labels often contain noise, such as missing annotations and imprecise boundaries, which can adversely affect the network's ability to accurately model target characteristics.Purpose This study aims to develop a robust segmentation framework capable of mitigating the impact of noisy labels during the training phase. The proposed framework is designed to preserve clean labels while cleansing noisy ones, thereby enhancing the overall segmentation accuracy.Methods We introduce a deep self-cleansing segmentation framework that incorporates two key modules as follows: a Gaussian Mixture Model (GMM)-based label filtering module (LFM) and a label cleansing module (LCM). The GMM-based LFM is employed to differentiate between noisy and clean labels. Subsequently, the LCM generates pseudo low-noise labels for the identified noisy samples. These pseudo-labels, along with the preserved clean labels, are then used to supervise the network training process.Results The framework was evaluated on a clinical liver tumor dataset (231 CT scans) and a public cardiac diagnosis dataset (200 MRI scans). Compared to baseline methods, our approach significantly improves segmentation performance, achieving a +7.31% boost in the B-model and a +12.36% improvement in the L-model. These results demonstrate the framework's ability to effectively suppress the interference of noisy labels and enhance segmentation accuracy. The method's capability to distinguish and cleanse noisy labels ensures more precise modeling of target structures, improving the robustness of the segmentation process.Conclusions The proposed deep self-cleansing segmentation framework offers a promising solution to the challenge of noisy labels in medical image segmentation. By integrating a GMM-based LFM and an LCM, the framework effectively preserves clean labels and generates pseudo low-noise labels, thereby improving the overall segmentation accuracy. The successful validation on both clinical and public datasets underscores the potential of this approach to enhance disease diagnosis and surgical planning in medical imaging.
资助项目Open Fund Project of Guangdong Acedemy of Medical Science
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:001576633200001
出版者WILEY
源URL[http://119.78.100.204/handle/2XEOYT63/41696]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Tong, Ruofeng; Ying, Shihong; Zhou, Shaohua Kevin
作者单位1.Univ Elect Sci & Technol China, Natl Key Lab Intelligent Collaborat Comp, Chengdu, Peoples R China
2.Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou, Peoples R China
3.Univ Sci & Technol China, Suzhou Inst Adv Res, Ctr Med Imaging Robot Analyt Comp & Learning, Hangzhou, Peoples R China
4.Third Mil Med Univ, Army Med Univ, 7T Magnet Resonance Translat Med Res Ctr, Southwest Hosp,Dept Radiol, Chongqing, Peoples R China
5.Zhejiang Univ, Affiliated Hosp 1, Coll Med, Dept Radiol, Hangzhou, Peoples R China
6.Ritsumeikan Univ, Coll Informat Sci & Engn, Kusatsu, Shiga, Japan
7.Univ Sci & Technol China, Sch Biomed Engn, Div Life Sci & Med, Hefei, Anhui, Peoples R China
8.Univ Sci & Technol China, Key Lab Precis & Intelligent Chem, Hefei, Anhui, Peoples R China
9.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Dong, Jiahua,Zhang, Yue,Wang, Qiuli,et al. Deep self-cleansing for medical image segmentation with noisy labels[J]. MEDICAL PHYSICS,2025,52(10):17.
APA Dong, Jiahua.,Zhang, Yue.,Wang, Qiuli.,Tong, Ruofeng.,Ying, Shihong.,...&Zhou, Shaohua Kevin.(2025).Deep self-cleansing for medical image segmentation with noisy labels.MEDICAL PHYSICS,52(10),17.
MLA Dong, Jiahua,et al."Deep self-cleansing for medical image segmentation with noisy labels".MEDICAL PHYSICS 52.10(2025):17.

入库方式： OAI收割

来源：计算技术研究所

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