Automatic left ventricular cavity segmentation via deep spatial sequential network in 4D computed tomography.

• We propose a spatial-sequential CNN network for temporal left ventricle segmentation on 4D cardiac sequence. • We utilize the sequential consistency (cardiac motion) to improve the LV segmentation during cardiac systole. • We present an unsupervised sequential CNN network to capture cardiac motion in heart cycle. • We introduce a bi-directional learning strategy to further refine the results. • We achieve higher accuracy compared to the state-of-the-art methods on 4D cardiac CT dataset. Automated segmentation of left ventricular cavity (LVC) in temporal cardiac image sequences (consisting of multiple time-points) is a fundamental requirement for quantitative analysis of cardiac structural and functional changes. Deep learning methods for segmentation are the state-of-the-art in performance; however, these methods are generally formulated to work on a single time-point, and thus disregard the complementary information available from the temporal image sequences that can aid in segmentation accuracy and consistency across the time-points. In particular, single time-point segmentation methods perform poorly in segmenting the end-systole (ES) phase image in the cardiac sequence, where the left ventricle deforms to the smallest irregular shape, and the boundary between the blood chamber and the myocardium becomes inconspicuous and ambiguous. To overcome these limitations in automatically segmenting temporal LVCs, we present a spatial sequential network (SS-Net) to learn the deformation and motion characteristics of the LVCs in an unsupervised manner; these characteristics are then integrated with sequential context information derived from bi-directional learning (BL) where both chronological and reverse-chronological directions of the image sequence are used. Our experimental results on a cardiac computed tomography (CT) dataset demonstrate that our spatial-sequential network with bi-directional learning (SS-BL-Net) outperforms existing methods for spatiotemporal LVC segmentation. [ABSTRACT FROM AUTHOR]