Computer-Aided Volumetric Quantification of Pre- and Post-Treatment Intracranial Aneurysms in MRA

Abstract

Intracranial aneurysm, a cerebrovascular condition involving abnormal arterial dilation, poses a high risk of subarachnoid hemorrhage upon rupture. Accurate quantification is crucial for diagnosis and follow-up treatment. This paper introduces a novel multi-scale dual-attention network (MSDA-Net) for quantification of intracranial aneurysms in MRA images. The proposed framework includes a context aware patch (CAP) module, multi-scale convolutional blocks, and a dual-attention block, where the CAP module extracts center-line patches to address foreground-background imbalance, the multi-scale and dual-attention blocks enable feature extraction of anatomical dependencies for fine-grained segmentation. The framework leverages three morphological features such as locations of aneurysms, vascular bifurcations, and vessel topology using a multi-task learning scheme for better segmentation. MSDA-Net surpasses state-of-the-art models such as U-Net, residual U-Net, attention U-Net, and nnU-net with an improved dice similarity coefficient of 0.71 and a volume similarity of 0.85. Experiments conducted on the publicly available ADAM challenge dataset and a private post-treatment database demonstrate the reliability and performance of this approach. The method could be used in clinical decision-making in aneurysm follow-up and has profound potential for integration into clinical workflows.