基于级联YOLO和U-Net的腰椎图像分割模型YOLOMACR-NetOA

Aiming at the problems of missed detection of critical structures,coarse segmentation boundaries,and parameter redundancy caused by variable vertebral morphology,complex background structures,and low tissue contrast in lumbar MRI,this paper proposes YOLOMACR-Net,a lightweight lumbar segmentation model integrating multi-scale feature enhancement and a cascade architecture.Firstly,a Multiscale Asymmetric Cavity Residual(MACR)module is designed within the YOLOv5n framework,utilizing asymmetric convolution to adapt to vertebral geometric features and expand the receptive field to address missed detections in single-stage detectors.Secondly,it constructs a"localization-segmentation"cascade architecture,uses the localization results to filter background noise,and guides U-Net for fine-grained segmentation.Experimental results on public datasets show that YOLOMACR-Net achieves a Structure Capture Rate(SCR)of 100%,with mIoU,Dice coefficient,and HD95 reaching 88.17%,93.71%,and 3.37 mm,respectively,while the parameter count is only 1.65M.The results demonstrate that the model effectively integrates multi-scale information and significantly improves segmentation accuracy in complex scenes while maintaining a lightweight design.