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

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.