SCUX‐Net: Integrating Multi‐Scale Features and Channel‐Spatial Attention Model for Intracranial Aneurysm Segmentation

ABSTRACT Intracranial aneurysm is a common cerebrovascular condition, due to the small size and complex anatomical location of intracranial aneurysms, it remains a challenging task to accurately segmenting the intracranial aneurysms in computed tomography angiography (CTA) images. To address these challenges, we propose SCUX‐Net, a novel lightweight convolutional neural network designed to facilitate the segmentation of intracranial aneurysms. SCUX‐Net builds upon the 3D UX‐Net by introducing two key innovations: (1) a spatial adaptive feature module, integrated before each 3D UX‐Net block, enabling multi‐scale feature fusion for long‐range information interaction; (2) a convolutional block attention module, applied after each downsampling block to emphasize important features across channel and spatial dimensions, suppressing irrelevant information. Experimental results substantiate the effectiveness of SCUX‐Net in segmenting intracranial aneurysms on CTA images, achieving a dice similarity coefficient of 80% on the test set. Notably, SCUX‐Net excels in detecting small aneurysms (3 mm) and multiple aneurysms, showcasing its potential for clinical application.