基于感受野与多尺度路径增强的腰椎图像分割网络DPS-UNetOA

To address segmentation challenges of blurred boundaries and background noise in lumbar MRI images,this paper proposes DPS-UNet,a segmentation network based on receptive field amplification and path augmentation.Firstly,a 7-layer deep encoder is constructed to amplify the global receptive field,effectively capturing complex topological features of the lumbar spine and overcoming the limitations in receptive field of traditional networks.Secondly,a bottom-up path augmentation structure is introduced to efficiently transmit shallow spatial localization information to deep layers,enhancing the expressive power of feature pyramid.Simultaneously,a parameter-free SimAM attention module is embedded to adaptively suppress soft tissue noise and reinforce edge responses.Experimental results demonstrate that DPS-UNet significantly outperforms mainstream methods,with the HD95 distance decreasing from 7.21 to 2.78 and mIoU reaching 86.45%.The ablation experiments further validate the synergistic effectiveness of the deep encoder and feature enhancement strategies,indicating promising clinical application potential.