Multilabel CNN for Atrial Fibrosis Assessment (CemrgApp)

Fully automatic, open-source deep learning pipeline for estimating left atrial fibrosis from late gadolinium enhancement (LGE) cardiac MRI, built to remove the operator-dependent steps that limit reproducibility of conventional atrial LGE analysis. A multilabel convolutional neural network delineates the left atrial blood pool, pulmonary veins, and mitral valve; these structures are then used to automatically calculate fibrosis burden via established image-intensity-ratio thresholds, without manual tracing. Validated on a 3D LGE-CMR dataset of 207 scans, the pipeline's automatic segmentation achieved a 91% Dice score against manual tracing, and its fully automatic fibrosis quantification closely matched semi-automatic reference methods. The CNN and pipeline are distributed as part of the open-source CemrgApp platform.