Detecting intracranial aneurysm rupture from 3D surfaces using a novel GraphNet approach
This addresses a critical medical challenge for clinicians in diagnosing life-threatening brain conditions, though it appears incremental as it builds on existing graph convolution networks.
The paper tackled the problem of detecting intracranial aneurysm rupture from 3D surface data, achieving an accuracy of 0.82 and AUC of 0.82 in classification, and a dice coefficient of 0.88 in segmentation.
Intracranial aneurysm (IA) is a life-threatening blood spot in human's brain if it ruptures and causes cerebral hemorrhage. It is challenging to detect whether an IA has ruptured from medical images. In this paper, we propose a novel graph based neural network named GraphNet to detect IA rupture from 3D surface data. GraphNet is based on graph convolution network (GCN) and is designed for graph-level classification and node-level segmentation. The network uses GCN blocks to extract surface local features and pools to global features. 1250 patient data including 385 ruptured and 865 unruptured IAs were collected from clinic for experiments. The performance on randomly selected 234 test patient data was reported. The experiment with the proposed GraphNet achieved accuracy of 0.82, area-under-curve (AUC) of receiver operating characteristic (ROC) curve 0.82 in the classification task, significantly outperforming the baseline approach without using graph based networks. The segmentation output of the model achieved mean graph-node-based dice coefficient (DSC) score 0.88.