Segmentation of the cardiac ventricle using two layer level sets with prior shape constraint. (July 2021)

• Record Type: Journal Article
• Authors: Lu, Junchi; Feng, Chaolu; Yang, Jinzhu; Li, Wei; Zhao, Dazhe; Wan, Chao
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: LV segmentation -- Level set -- Shape constraint -- Detection of ROI; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102671
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Accumulation of temporal difference is used to detect region of the LV. A two-layer level set method is proposed to simultaneously extract endocardium and epicardium of the LV. A shape prior constraint with anatomical structure of the LV is introduced to improve segmentation accuracy. The proposed method manifests best in terms of APD and DSC on representative image repositories. Abstract: The cardiac Left Ventricle (LV) segmentation is still challengeable due to the complex anatomical structure surrounding the LV and intensity overlaps caused by intensity inhomogeneities, which widely exists in cardiac Magnetic Resonance (MR) images. To overcome these problems, a shape prior constrained two-layer level set method is proposed to simultaneously extract the endocardium and epicardium of the LV from cardiac short-axis MR images. The proposed method is validated on MR images from the LV challenge of MICCAI 2009. Quantitative and qualitative experiments and clinical metrics demonstrate that the proposed method has better performance than some representative methods in term of Dice Similarity Coefficient (DSC), Average Perpendicular Distance (APD), Ejection Fraction (EF), and LV mass. Experimental results demonstrate that the proposed method has the priority of preserving cardiac anatomical structure and improving segmentation accuracy.