Abstract：Computed Tomography (CT) technology is crucial for the diagnosis and treatment of spinal diseases. However, the complex structure and unclear boundaries of the spine in spinal CT images lead to poor segmentation accuracy. To address these issues, a spinal image segmentation network based on multi-dimensional attention and cascaded decoding was proposed in this work. First, a multi-dimensional attention module was added to the encoder, which fully extracted and fused features from multiple dimensions, thereby enhancing the model's representation ability. Second, a cascaded refinement decoding structure was constructed in the decoder. Different segmentation tasks were designed for the two decoders, which decoded the encoded features successively to refine the segmentation boundaries step by step and ensured the recovery of fine features. Finally, the proposed MACD-Net was validated on the VerSe2019 dataset (comprising 160 CT images), with Dice similarity coefficient (DSC) values reaching 91.94 %, 91.59 %, and 72.69% for the cervical, thoracic, and lumbar vertebrae, respectively, and 95th percentile Hausdorffdistance (HD95) values of 2.24 mm, 2.93 mm, and 6.90 mm, respectively. Compared with other models, the proposed model achieved significantly better segmentation results. Additionally, experiments conducted on the CTSpine1K dataset (with 300 randomly selected CT images) further verified the effectiveness of the model. In conclusion, this study demonstrated that MACD-Net achieved precise spinal image segmentation, thereby assisting doctors in diagnosis and treatment planning.
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