Coronary Artery Segmentation by Deep Learning Neural Networks on Computed Tomographic Coronary Angiographic Images

Coronary artery lumen delineation, to localize and grade stenosis, is an important but tedious and challenging task for coronary heart disease evaluation. Deep learning has recently been successful applied to many applications, including medical imaging. However for small imaged objects such as coronary arteries and their segmentation, it remains a challenge. This paper investigates coronary artery lumen segmentation using 3D U-net convolutional neural networks, and tests its utility with multiple datasets on two settings. We adapted the computed tomography coronary angiography (CTCA) volumes into small patches for the networks and tuned the kernels, layers and the batch size for machine learning. Our experiment involves additional efforts to select and test various data transform, so as to reduce the problem of overfitting. Compared with traditional normalization of data, we showed that subject-specific normalization of dataset was superior to patch based normalization. The results also showed that the proposed deep learning approach outperformed other methods, evaluated by the Dice coefficients.