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

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 ($\le$3 mm) and multiple aneurysms, showcasing its potential for clinical application.