ConUDiff: diffusion model with contrastive pretraining and uncertain region optimization for segmentation of left ventricle from echocardiography

doi:10.1007/s10044-025-01509-7

	ConUDiff: diffusion model with contrastive pretraining and uncertain region optimization for segmentation of left ventricle from echocardiography
	Zhang, Guohuan 1; Zhang, Lei 1; Fu, Xuetong 1; Wang, Yuanquan 1; Zhou, Shoujun 2; Wei, Jin 3; Zhao, Di 4
	2025-09-01
发表期刊	PATTERN ANALYSIS AND APPLICATIONS
ISSN	1433-7541
卷号	28 期号:3 页码:11
摘要	Accurate segmentation of the left ventricle (LV) in echocardiograms plays a crucial role in the diagnosis and treatment of cardiovascular diseases. However, manual segmentation of the left ventricle is time-consuming and subject to inter-observer variability. It is crucial to develop an accurate and automatic segmentation method. In this paper, we propose a novel diffusion-based model, called ConUDiff in short, for LV segmentation in echocardiography. The proposed ConUDiff is based on the denoising diffusion probabilistic model and two modules are introduced, i.e., a contrastive pretrained ResNet-50 encoder and an uncertain region optimization module (UROM). The contrastive pretrained ResNet-50 encoder is employed to extract rich feature representations from the original image and enhance the semantic information contained in the feature maps. The UROM module is designed to optimize uncertain regions in the feature maps. We evaluate our method on two public datasets, i.e., the EchoNet-Dynamic dataset and the EchoNet-Pediatric dataset. The experimental results demonstrate that the proposed ConUDiff outperforms some popular networks, achieving a Dice score of 92.68% on the EchoNet-Dynamic dataset and a Dice score of 90.69% on the EchoNet-Pediatric dataset. Our method shows the potential for echocardiographic left ventricle segmentation.
关键词	Diffusion models Echocardiography Left ventricle Segmentation Contrastive learning Uncertain region optimization
DOI	10.1007/s10044-025-01509-7
收录类别	SCI
语种	英语
资助项目	the National Science Foundation of China[61976241] ; the National Science Foundation of China[81827805] ; National Science Foundation of China (NSFC)[2018YFA0704102] ; National key R&D program of China[JCYJ20200109114610201] ; Basic Research Project of Shenzhen Science and Technology Innovation Commission
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001514383900001
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42286
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, Yuanquan; Zhou, Shoujun
作者单位	1.Hebei Univ Technol HeBUT, Sch Artificial Intelligence, Tianjin 300401, Peoples R China 2.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China 3.Third Cent Hosp Tianjin, Tianjin 300171, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Guohuan,Zhang, Lei,Fu, Xuetong,et al. ConUDiff: diffusion model with contrastive pretraining and uncertain region optimization for segmentation of left ventricle from echocardiography[J]. PATTERN ANALYSIS AND APPLICATIONS,2025,28(3):11.
APA	Zhang, Guohuan.,Zhang, Lei.,Fu, Xuetong.,Wang, Yuanquan.,Zhou, Shoujun.,...&Zhao, Di.(2025).ConUDiff: diffusion model with contrastive pretraining and uncertain region optimization for segmentation of left ventricle from echocardiography.PATTERN ANALYSIS AND APPLICATIONS,28(3),11.
MLA	Zhang, Guohuan,et al."ConUDiff: diffusion model with contrastive pretraining and uncertain region optimization for segmentation of left ventricle from echocardiography".PATTERN ANALYSIS AND APPLICATIONS 28.3(2025):11.