Connectivity-based Cerebrovascular Segmentation in Time-of-Flight Magnetic Resonance Angiography

Accurate segmentation of cerebrovascular structures from Time-of-flight magnetic resonance angiography is vital for treating cerebrovascular diseases. However, existing methods rely on voxel categorization, leading to discontinuities in fine vessel locations. We propose a connectivity-based cerebrovascular segmentation method that considers inter-voxel relationships to alleviate this limitation. By modeling connectivity, we convert voxel classification into inter-voxel connectivity prediction. Given the sparse and widely distributed nature of cerebrovascular structures, we employ a sparse 3D Bi-level routing attention to effectively capture cerebrovascular features. To extract inter-voxel directional information, we utilize a 3D direction excitation block. Additionally, a 3D direction interactive block continuously enhances the directional information within the feature map. This directional enhanced feature map is then concatenated with the feature map from the encoder layer and fed into the decoder layer. We compare our method with current state-of-the-art cerebrovascular segmentation techniques and general medical image segmentation methods. Our method achieved a Dice Similarity Coefficient of 92.413% and 83.197% on the clinical and open cerebrovascular datasets respectively, outperforming existing approaches.