Automated Coronary Artery Segmentation with 3D PSPNET using Global Processing and Patch Based Methods on CCTA Images.

The prevalence of coronary artery disease (CAD) has become the major cause of death across the world in recent years. The accurate segmentation of coronary artery is important in clinical diagnosis and treatment of coronary artery disease (CAD) such as stenosis detection and plaque analysis. Deep learning techniques have been shown to assist medical experts in diagnosing diseases using biomedical imaging. There are many methods which employ 2D DL models for medical image segmentation. The 2D Pyramid Scene Parsing Neural Network (PSPNet) has potential in this domain but not explored for the segmentation of coronary arteries from 3D Coronary Computed Tomography Angiography (CCTA) images. The contribution of present research work is to propose the modification of 2D PSPNet into 3D PSPNet for segmenting the coronary arteries from 3D CCTA images. The innovative factor is to evaluate the network performance by employing Global processing and Patch based processing methods. The experimental results achieved a Dice Similarity Coefficient (DSC) of 0.76 for Global process method and 0.73 for Patch based method using a subset of 200 images from the ImageCAS dataset.