Segmentation and optical flow estimation in cardiac CT sequences based on a spatiotemporal PDM with a correction scheme and the Hermite transform.

The left ventricle and the myocardium are two of the most important parts of the heart used for cardiac evaluation. In this work a novel framework that combines two methods to isolate and display functional characteristics of the heart using sequences of cardiac computed tomography (CT) is proposed. A shape extraction method, which includes a new segmentation correction scheme, is performed jointly with a motion estimation approach.For the segmentation task we built a Spatiotemporal Point Distribution Model (STPDM) that encodes spatial and temporal variability of the heart structures. Intensity and gradient information guide the STPDM. We present a novel method to correct segmentation errors obtained with the STPDM. It consists of a deformable scheme that combines three types of image features: local histograms, gradients and binary patterns. A bio-inspired image representation model based on the Hermite transform is used for motion estimation. The segmentation allows isolating the structure of interest while the motion estimation can be used to characterize the movement of the complete heart muscle.The work is evaluated with several sequences of cardiac CT. The left ventricle was used for evaluation. Several metrics were used to validate the proposed framework. The efficiency of our method is also demonstrated by comparing with other techniques.The implemented tool can enable physicians to better identify mechanical problems. The new correction scheme substantially improves the segmentation performance. Reported results demonstrate that this work is a promising technique for heart mechanical assessment.