Lung Nodule Segmentation and Uncertain Region Prediction With an Uncertainty-Aware Attention Mechanism

doi:10.1109/TMI.2023.3332944

	Lung Nodule Segmentation and Uncertain Region Prediction With an Uncertainty-Aware Attention Mechanism
	Yang, Han 1,2; Wang, Qiuli 3,4; Zhang, Yue 3,4; An, Zhulin 5; Liu, Chen 6; Zhang, Xiaohong 7; Zhou, S. Kevin 3,4,8
	2024-04-01
发表期刊	IEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN	0278-0062
卷号	43 期号:4 页码:1284-1295
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.
关键词	Lung nodules segmentation uncertainty multiple annotations computed tomography
DOI	10.1109/TMI.2023.3332944
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001196733400022
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39879
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.