Sparse group composition for robust left ventricular epicardium segmentation. (December 2015)

• Record Type: Journal Article
• Authors: Wang, Bing; Gu, Xiaomeng; Fan, Chonghao; Xie, Hongzhi; Zhang, Shuyang; Tian, Xuedong; Gu, Lixu
• Is Part Of: Computerized medical imaging and graphics. Volume 46:Part 1(2015)
• Page Start: 56
• Page End: 63
• Publication Date: 2015-12
• Subjects: LV epicardium segmentation -- Sparse group composition -- Multi-shape prior modeling -- Gradient Chan-Vese (GCV) model; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2015.06.003
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: Left ventricular (LV) epicardium segmentation in cardiac magnetic resonance images (MRIs) is still a challenging task, where the a-priori knowledge like those that incorporate the heart shape model is usually used to derive reasonable segmentation results. In this paper, we propose a sparse group composition (SGC) approach to model multiple shapes simultaneously, which extends conventional sparsity-based single shape prior modeling to incorporate a-priori spatial constraint information among multiple shapes on-the-fly. Multiple interrelated shapes (shapes of epi- and endo-cardium of myocardium in the case of LV epicardium segmentation) are regarded as a group, and sparse linear composition of training groups is computed to approximate the input group. A framework of iterative procedure of refinement based on SGC and segmentation based on deformation model is utilized for LV epicardium segmentation, in which an improved shape-constraint gradient Chan-Vese model (GCV) acted as deformation model. Compared with the standard sparsity-based single shape prior modeling, the refinement procedure has strong robust for relative gross and not much sparse errors in the input shape and the initial epicardium location can be estimated without complicated landmark detection due to modeling spatial constraint information among multiple shapes effectively. Proposed method was validated on 45 cardiac cine-MR clinical datasets and the results were compared with expert contours. The average perpendicular distance (APD) error of contours is 1.50 ± 0.29 mm, and the dice metric (DM) is 0.96 ± 0.01. Compared to the state-of-the-art methods, our proposed approach appealed competitive segmentation performance and improved robustness.