Cardiac chamber segmentation has proved to be essential in many clinical applications including cardiac functional analysis, myocardium analysis and electrophysiology studies for ablation planning. Traditional rule-based or modelbased approaches have been widely developed and employed, however these methods can be time consuming to run and sometimes fail when certain rules are not met. Recent advances in deep learning provide a new approach in solving these segmentation problems. In this work we employ a TensorFlow implementation of the 3D U-Net trained with 413 cardiac CTA volumes to segment the left ventricle (LV) and the left atrium (LA). The network is tested on 162 unseen volumes. For LV the Dice similarity coefficient (DSC) reaches 90.2±2.6% and for LA 87.6±7.5%. The number of training and testing samples far exceeds the common use of datasets seen in literature thanks to the existing rule-based algorithm in Vitrea®’s Cardiac Functional CT protocol which was used to provide the segmentation labels. The labels are manually filtered, and only accurate labels are kept for training and testing. For the datasets with inaccurate labels, the trained network has proved to perform better in generating more accurate boundaries around the aortic valve, mitral valve and the apex of LV. The TensorFlow implementation allows for faster training which takes 3-4 hours and inferencing which takes less than 6 seconds to simultaneously segment 12 CT volumes. This significantly reduces the pre-processing time required for cardiac functional CT studies which usually consist of 10-20 cardiac phases and take minutes to segment with traditional methods.
tomographic angiography (CTA) being noninvasive, economical and informative, has become a common modality for
monitoring disease status and treatment effects. Here, we present a new method for detecting and quantifying coronary
arterial stenosis via CTA using fuzzy distance transform (FDT) approach. FDT computes local depth at each image point
in the presence of partial voluming. Coronary arterial stenoses are detected and their severities are quantified by
analyzing FDT values along the medial axis of an artery obtained by skeletonization. Also, we have developed a new
skeletal pruning algorithm toward improving quality of medial axes and therefore, enhancing the accuracy of stenosis
detection and quantification. The method is completed using the following steps - (1) fuzzy segmentation of coronary
artery via CTA, (2) FDT computation of coronary arteries, (3) medial axis computation, (4) estimation of local diameter
along arteries and (5) stenosis detection and quantification of arterial blockage. Performance of the method has been
quantitatively evaluated on a realistic coronary artery phantom dataset with randomly simulated stenoses and the results
are compared with a classical binary algorithm. The method has also been applied on a clinical CTA dataset from
thirteen patients with 59 stenoses and the results are compared with an expert's quantitative assessment of stenoses.
Results of the phantom experiment indicate that the new method is significantly more accurate as compared to the
conventional binary method. Also, the results of the clinical study indicate that the computerized method is highly in
agreement with the expert's assessments.