Deep Learning for Ventricular Arrhythmia Prediction Using Fibrosis Segmentations on Cardiac MRI Data

Abstract

Many patients at high risk of life-threatening ventricular arrhythmias (VA) and sudden cardiac death (SCD) who received an implantable cardioverter defibrillator (ICD), never receive appropriate device therapy. The presence of fibrosis on LGE CMR imaging is shown to be associated with increased risk of VA. Therefore, there is a strong need for both automatic segmentation and quantification of cardiac fibrosis as well as better risk stratification for SCD. This study first presents a novel two-stage deep learning network for the segmentation of left ventricle myocardium and fibrosis on LGE CMR images. Secondly it aims to effectively predict device therapy in ICD patients by using a graph neural network approach which incorporates both myocardium and fibrosis features as well as the left ventricle geometry. Our segmentation network outperforms previous state-of-the-art methods on 2D CMR data, reaching a Dice score of 0.82 and 0.77 on myocardium and fibrosis segmentation, respectively. The ICD therapy prediction network reaches an AUC of 0.60 while using only CMR data and outperforms baseline methods based on current guideline markers for ICD implantation. This work lays a strong basis for future research on improved risk stratification for VA and SCD