CARDIO-3DNet: CORONARY ARTERY RECONSTRUCTION USING DEEP LEARNING FOR ADVANCED MEDICAL IMAGE AND OBJECT-BASED 3D NETWORK

Abstract

Coronary artery disease is a leading cause of cardiovascular mortality worldwide. Three-dimensional reconstruction of coronary vessels from 2D angiographic and computed tomography (CT) images is vital for stenosis quantification and surgical planning. This paper proposes CARDIO-3DNet, an end-to-end 3D deep learning framework for vessel segmentation and surface reconstruction. The model employs a 3D U-Net backbone with residual dual-attention blocks to maintain fine vascular branches and bifurcations while eliminating cardiac motion artifacts. On clinical CT angiography datasets, CARDIO-3DNet obtains a Dice Similarity Coefficient of 0.912 and Hausdorff Distance of 3.4 mm.