中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Lung Nodule Segmentation and Uncertain Region Prediction With an Uncertainty-Aware Attention Mechanism

文献类型:期刊论文

作者Yang, Han1,2; Wang, Qiuli3,4; Zhang, Yue3,4; An, Zhulin5; Liu, Chen6; Zhang, Xiaohong7; Zhou, S. Kevin3,4,8
刊名IEEE TRANSACTIONS ON MEDICAL IMAGING
出版日期2024-04-01
卷号43期号:4页码:1284-1295
关键词Lung nodules segmentation uncertainty multiple annotations computed tomography
ISSN号0278-0062
DOI10.1109/TMI.2023.3332944
英文摘要Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty. Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations. However, these approaches fail to leverage the valuable information inherent in the consensus and disagreements among the multiple annotations. In this paper, we propose an Uncertainty-Aware Attention Mechanism (UAAM) that utilizes consensus and disagreements among multiple annotations to facilitate better segmentation. To this end, we introduce the Multi-Confidence Mask (MCM), which combines a Low-Confidence (LC) Mask and a High-Confidence (HC) Mask. The LC mask indicates regions with low segmentation confidence, where radiologists may have different segmentation choices. Following UAAM, we further design an Uncertainty-Guide Multi-Confidence Segmentation Network (UGMCS-Net), which contains three modules: a Feature Extracting Module that captures a general feature of a lung nodule, an Uncertainty-Aware Module that produces three features for the annotations' union, intersection, and annotation set, and an Intersection-Union Constraining Module that uses distances between the three features to balance the predictions of final segmentation and MCM. To comprehensively demonstrate the performance of our method, we propose a Complex-Nodule Validation on LIDC-IDRI, which tests UGMCS-Net's segmentation performance on lung nodules that are difficult to segment using common methods. Experimental results demonstrate that our method can significantly improve the segmentation performance on nodules that are difficult to segment using conventional methods.
资助项目Natural Science Foundation of China
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:001196733400022
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/39878]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Qiuli; Zhou, S. Kevin
作者单位1.Chinese Acad Sci, Inst Comp Technol, Domain Oriented Comp Technol Res Ctr, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci UCAS, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
3.Univ Sci & Technol China, Sch Biomed Engn, Div Life Sci & Med, Hefei 230026, Anhui, Peoples R China
4.Univ Sci & Technol China, Suzhou Inst Adv Res, Ctr Med Imaging Robot Analyt Comp & Learning MIRAC, Suzhou 215123, Jiangsu, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
6.Army Med Univ, Dept Radiol, Affiliated Hosp 1, Chongqing 400032, Peoples R China
7.Chongqing Univ, Sch Big Data & Software Engn, Chongqing 401331, Peoples R China
8.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714
Yang, Han,Wang, Qiuli,Zhang, Yue,et al. Lung Nodule Segmentation and Uncertain Region Prediction With an Uncertainty-Aware Attention Mechanism[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2024,43(4):1284-1295.
APA Yang, Han.,Wang, Qiuli.,Zhang, Yue.,An, Zhulin.,Liu, Chen.,...&Zhou, S. Kevin.(2024).Lung Nodule Segmentation and Uncertain Region Prediction With an Uncertainty-Aware Attention Mechanism.IEEE TRANSACTIONS ON MEDICAL IMAGING,43(4),1284-1295.
MLA Yang, Han,et al."Lung Nodule Segmentation and Uncertain Region Prediction With an Uncertainty-Aware Attention Mechanism".IEEE TRANSACTIONS ON MEDICAL IMAGING 43.4(2024):1284-1295.

入库方式: OAI收割

来源:计算技术研究所

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