Abstract: Automatic analysis of lumbar disc herniation requires precise segmentation of the multifidus muscle’s fatty infiltration site in spinal MRI ( Magnetic Resonance Imaging) images. An attention-based approach for segmenting the multifidus muscle in lumbar disc herniation patients is proposed to address issues including ambiguous boundaries between segmentation targets and adjacent components. The network utilizes an encoder- decoder structure, and the addition of an attention mechanism module to increase the network segmentation accuracy. After feature extraction, an atrous spatial pyramid pooling module is added to combine contextual data improving the performance of the network model. In comparison to the traditional U-Net algorithm, the experimental results demonstrate that this model improves the segmentation accuracy of the fatty infiltrated regions of multifidus muscle by improving the Dice coefficient by 7. 8% , Jaccard similarity coefficient by 10. 1% , and Hausdorff Distance by 69. 5% . 

Key words: lumbar disc herniation(LDH), magnetic resonance imaging(MRI), U-Net algorithm, attention mechanism, atrous spatial pyramid pooling(ASPP)