Interpretable Prediction of Post-Infarct Ventricular Arrhythmia Using Graph Convolutional Network

Abstract

Heterogeneity of left ventricular (LV) myocardium infarction scar plays an important role as anatomical substrate in ventricular arrhythmia (VA) mechanism. LV myocardium thinning, as observed on cardiac computed tomography (CT), has been shown to correlate with LV myocardial scar and with abnormal electrical activity. In this project, we propose an automatic pipeline for VA prediction, based on CT images, using a Graph Convolutional Network (GCN). The pipeline includes the segmentation of LV masks from the input CT image, the short-axis orientation reformatting, LV myocardium thickness computation and mid-wall surface mesh generation. An average LV mesh was computed and fitted to every patient in order to use the same number of vertices with point-to-point correspondence. The GCN model was trained using the thickness value as the node feature and the atlas edges as the adjacency matrix. This allows the model to process the data on the 3D patient anatomy and bypass the “grid" structure limitation of the traditional convolutional neural network. The model was trained and evaluated on a dataset of 600 patients (27$\%$ VA), using 451 (3/4) and 149 (1/4) patients as training and testing data, respectively. The evaluation results showed that the graph model ($81\%$ accuracy) outperformed the clinical baseline ($67\%$), the left ventricular ejection fraction, and the scar size ($73\%$). We further studied the interpretability of the trained model using LIME and integrated gradients and found promising results on the personalised discovering of the specific regions within the infarct area related to the arrhythmogenesis.