Two stage registration-based automatic left ventricle myocardium segmentation of cardiac 4DCT images

Xu Zhou; Jiajun Wang; Guanzhong Gong

doi:10.1117/12.2557542

3 January 2020 Two stage registration-based automatic left ventricle myocardium segmentation of cardiac 4DCT images

Xu Zhou, Jiajun Wang, Guanzhong Gong

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113731D (2020) https://doi.org/10.1117/12.2557542
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

Cardiac pathologies are generally associated with regional ventricular dysfunction. The left ventricular myocardium (LVM) segmentation is essential to clinical quantification and diagnosis of cardiac images. In this paper, a two stage registration-based segmentation method is proposed to segment the LVM from the cardiac 4DCT. In the first stage, a coarse global registration procedure is implemented in between the first time phase and other phases to spatially map the manually extracted ROI including the LVM in the first time phase to those in other phases. In the second stage, a fine local registration procedure is performed with respect to ROIs in between the first time phase and other phases to spatially map the manually obtained segmentation labels of the LVM in the first time phase to other phases to be segmented. In this way, the whole LVM segmentation results of 4DCT datasets can be obtained by propagating the labels from the 3D dataset in one phase to that in other phases. Experimental results showed that our proposed two stage registration-based segmentation method outperformed the global automatic registration-based segmentation method in effectiveness.

Citation Download Citation

Xu Zhou, Jiajun Wang, and Guanzhong Gong "Two stage registration-based automatic left ventricle myocardium segmentation of cardiac 4DCT images", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113731D (3 January 2020); https://doi.org/10.1117/12.2557542

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