Automatic two-chamber segmentation in cardiac CTA using 3D fully convolutional neural networks

摘要

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.