Spine CT image segmentation based on multi-scale boundary segmentation and hybrid attention mechanism
The early diagnosis of spinal diseases is mainly screened and initially diagnosed through computed tomography(CT).In view of the complex structure of vertebral bones and low segmentation accuracy in spinal CT images,a spinal CT image segmentation network based on 3D U-Net framework is proposed.The network which integrates squeeze-and-excitation residual module,vertebral boundary segmentation model,and improved hybrid channel-spatial attention mechanism is trained and tested on VerSe 19,VerSe 20,and CTSpine1K spinal datasets.Multiple experiments indicate that the model can effectively improve segmentation accuracy and efficiency while demonstrating good generalization and robustness.Compared with other advanced network models,the proposed network achieves higher segmentation accuracy in terms of Dice similarity coefficient,Hausdorff distance,and average symmetric surface distance.The proposed model exhibits superior segmentation performance among the existing spinal segmentation networks,providing radiologists with valuable clinical information.
NETL
NSTL
万方数据
维普
