3D_CAS (Feature-Fusion-and-Rectification 3D-UNet)

Automatic coronary artery segmentation pipeline for coronary CT angiography (CCTA). A 2D DenseNet classifier first screens out CT slices that don't contain coronary artery, then a 3D-UNet -- enhanced with dense blocks in the encoder for richer feature extraction and residual, feature-rectifying blocks in the decoder -- segments the coronary artery tree in the remaining slices. A Gaussian-weighted merging scheme combines overlapping 3D patch predictions, up-weighting the more reliable predictions near each patch's center. On the authors' in-house CCTA dataset, the method achieved a Dice similarity coefficient of 0.826.