A 3D convolutional neural network architecture for early detection of coronary artery blockage (Coronary-3D-UNet)

Abstract

Coronary Artery Disease (CAD) is a major cause of death all over the globe where the estimated number of annual deaths are 9 million. Early and precise diagnosis of the stenosis of the coronary arteries is critical in providing clinical intervention and better patient outcomes. Despite the fact that invasive coronary angiography (ICA) is regarded as the most effective diagnostic tool, the procedure has certain risks and is not convenient enough to be used widely because of its inapplicability to whole population screening. Coronary Computed Tomography Angiography (CCTA) is a non-invasive, high-resolution alternative but manual analysis of CCTA images is time consuming and subject to inter-observer error. This paper presents Coronary-3D-Unet, a parameter-efficient 3D convolutional neural network to perform automated stenosis assessment and coronary artery segmentation on CCTA volumes. The residual learning and dual attention mechanisms (spatial and channel) and multiscale feature fusion are incorporated into the proposed architecture to improve vessel representation and resistance to various challenges such as small vessel structures, low contrast, and imaging artifacts. The framework allows automatic stenosis grading of severity as mild, moderate, and severe geometrically. On a publicly available benchmark dataset, experimental results demonstrate that Coronary-3D-Unet reaches a mean Dice Similarity Coefficient (DSC) of 76.6% and bests the official ImageCAS baseline with a significantly lower model complexity. The model is based on an efficient number of 3.8 million parameters, which is 83% less than the conventional dense 3D networks, so the model can be used to infer efficiently and be deployed practically. The model can also be integrated with clinical Picture Archiving and Communication Systems (PACS) that facilitate real-time analyses and better clinical usability.