V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentation
Atrial Fibrillation (AF) is a common electro-physiological cardiac disorder that causes changes in the anatomy of the atria. A better characterization of these changes is desirable for the definition of clinical biomarkers. There is thus a need for its fully automatic segmentation from clinical images. This work presents an architecture based on 3D-convolution kernels, a Volumetric Fully Convolution Neural Network (V-FCNN), able to segment the entire atrial anatomy in a one-shot from high-resolution images (\(640\times 640\) pixels). A loss function based on the mixture of both Mean Square Error (MSE) and Dice Loss (DL) is used, in an attempt to combine the ability to capture the bulk shape as well as the reduction of local errors caused by over-segmentation.