Segmentation of cardiac tissues and organs for CCTA images based on a deep learning model

Accurate segmentation of cardiac tissues and organs based on cardiac computerized tomography angiography (CCTA) images has played an important role in biophysical modeling and medical diagnosis. The existing research on segmentation of cardiac tissues generally rely on limited public data, which may lead to unsatisfactory performance. In this paper, we first present a unique dataset of three-dimensional (3D) CCTA images collected from multiple centers to remedy this shortcoming. We further propose to efficiently create labels by solving the Laplace's equation with given boundary conditions. The generated images and labels are confirmed by cardiologists. A deep learning algorithm, based on 3D-Unet model trained with a combined loss function, is proposed to simultaneously segment aorta, left ventricle, left atrium, left atrial appendage and myocardium from the CCTA images. Experimental evaluations show that the model trained with a proposed combined loss function can improve the segmentation accuracy and robustness. By efficiently producing a patient-specific geometry for simulation, we believe that this learning-based approach could provide an avenue to combine with biophysical modeling for the study of hemodynamics in cardiac tissues.