Joint multi-object registration and segmentation of left and right cardiac ventricles in 4D cine MRI

Jan Ehrhardt; Timo Kepp; Alexander Schmidt-Richberg; Heinz Handels

doi:10.1117/12.2043583

21 March 2014 Joint multi-object registration and segmentation of left and right cardiac ventricles in 4D cine MRI

Jan Ehrhardt, Timo Kepp, Alexander Schmidt-Richberg, Heinz Handels

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 90340M (2014) https://doi.org/10.1117/12.2043583
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

The diagnosis of cardiac function based on cine MRI requires the segmentation of cardiac structures in the images, but the problem of automatic cardiac segmentation is still open, due to the imaging characteristics of cardiac MR images and the anatomical variability of the heart. In this paper, we present a variational framework for joint segmentation and registration of multiple structures of the heart. To enable the simultaneous segmentation and registration of multiple objects, a shape prior term is introduced into a region competition approach for multi-object level set segmentation. The proposed algorithm is applied for simultaneous segmentation of the myocardium as well as the left and right ventricular blood pool in short axis cine MRI images. Two experiments are performed: first, intra-patient 4D segmentation with a given initial segmentation for one time-point in a 4D sequence, and second, a multi-atlas segmentation strategy is applied to unseen patient data. Evaluation of segmentation accuracy is done by overlap coefficients and surface distances. An evaluation based on clinical 4D cine MRI images of 25 patients shows the benefit of the combined approach compared to sole registration and sole segmentation.

Citation Download Citation

Jan Ehrhardt, Timo Kepp, Alexander Schmidt-Richberg, and Heinz Handels "Joint multi-object registration and segmentation of left and right cardiac ventricles in 4D cine MRI", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90340M (21 March 2014); https://doi.org/10.1117/12.2043583

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