Multi-Scale Hybrid Attention Convolutional Neural Network for Automatic Segmentation of Lumbar Vertebrae From MRI

Due to the high incidence of lumbar vertebral lesions causing spondylolisthesis and intervertebral disc protrusion, lumbar spine (vertebrae and intervertebral discs) MRI image segmentation can provide effective clinical information for the initial diagnosis and early treatment of current lumbar spine-related diseases. However, in MRI images, there is a significant overlap and similarity in features between the vertebral bones and intervertebral discs within the lumbar spine. Therefore, the effective identification and segmentation of each vertebra and intervertebral disc in the lumbar spine pose a significant challenge. We propose a lumbar spine MRI segmentation model based on the 3D Residual U-Net, incorporating boundary segmentation structures and a hybrid attention mechanism. The model achieves boundary-constrained segmentation of individual vertebrae and intervertebral discs by integrating the boundary segmentation module. Additionally, it utilizes a hybrid attention module based on convolutional attention and self-attention mechanisms for multiscale feature extraction in the lumbar spine. The proposed model is trained and validated using the publicly available datasets MRSpineSeg2021 and SpineSagT2Wdataset3. Experimental results demonstrate a significant improvement in segmentation performance, as measured by metrics such as the Dice similarity coefficient (DSC) and Hausdorff distance (HD). This validates the superiority and generalization performance of our proposed lumbar spine MRI.