现代信息科技2026，Vol.10Issue(4)：67-72,6.DOI:10.19850/j.cnki.2096-4706.2026.04.012
基于感受野与多尺度路径增强的腰椎图像分割网络DPS-UNet
Lumbar Image Segmentation Network DPS-UNet Based on Receptive Field Amplification and Multi-scale Path Augmentation
何致远 1汪灿华1
作者信息
- 1. 江西中医药大学,江西 南昌 330004
- 折叠
摘要
Abstract
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.关键词
腰椎图像分割/U-Net/SimAM/路径增强/感受野扩增Key words
lumbar image segmentation/U-Net/SimAM/path augmentation/receptive field amplification分类
信息技术与安全科学引用本文复制引用
何致远,汪灿华..基于感受野与多尺度路径增强的腰椎图像分割网络DPS-UNet[J].现代信息科技,2026,10(4):67-72,6.
