Automated pericardium-segmentation using an attention-based convolutional neural network

Abstract

Accurate segmentation of the pericardium from Coronary Artery Calcium Scoring (CACS) scans is of prime importance in many emerging clinical-science and technology applications. To this end we propose an attention-based convolutional neural network for accurate detection and segmentation of the pericardium from non-contrast CT scans of the coronary artery (CT-CA). This would serve to be of paramount importance in a clinical routine for diagnosis, prognosis, and risk assessment of Cardio-Vascular Disease (CVD) -the highest cause of mortality worldwide, as it enables quick, reliable, and accurate classification of cardiac fat(s) and their quantification. This is in clear contrast to manual and analytical approaches which are not just time-consuming and laborious but are highly prone to errors. Our novel framework is a customized CNN based on a 3-D encoder-decoder architecture with attention blocks coupled with a context encoding block, and the deep learning model has leveraged a few hundred CACS stacks for training, validation, and out-of-sample testing. Through extensive experimentation, optimization and hyperparameter tuning, followed by a comprehensive validation of results, we have achieved a state-of-the-art clinically acceptable dice score of 0.94, along with a miss-rate (false negative rate) of 6% and a fall-out (false positive rate) of 0.5%. Our results indicate that this approach holds promise for a reliable & precise biomarker based cardiac risk-stratification.