MRF ‐ DPUNet ++: An Improved U‐Net++ Network for Bone Segmentation in CT Images of Tibial and Fibular Fractures

Accurate bone segmentation in CT images of tibial and fibular fractures is essential for fracture‐related three‐dimensional reconstruction, preoperative planning, and robot‐assisted reduction. However, the tibia and fibula are elongated bones with complex boundaries, and fracture cases often involve cortical disruption, displacement, fragment overlap, and imaging artifacts. This study aimed to develop an improved U‐Net++ network for automatic bone segmentation in CT images of tibial and fibular fractures. We proposed MRF‐DPUNet++, which incorporates a Depthwise Multi‐Receptive‐Field Block (DMRF Block) into the encoder and a Dual‐Pooling Feature Enhancement Module (DPFEM) before the prediction layer. The DMRF Block uses multi‐branch depthwise dilated convolutions to enhance multi‐scale bone‐structure representation, whereas DPFEM combines average‐pooling and max‐pooling responses to improve feature discrimination immediately before prediction. Experiments were conducted on preoperative CT data from 45 patients with tibial and fibular fractures. MRF‐DPUNet++ achieved a Dice score of 94.86% and an IoU of 93.09% on the tibial and fibular fracture CT segmentation task, outperforming the comparative methods in the main overlap metrics. Ablation experiments showed that both the DMRF Block and DPFEM contributed to improved segmentation performance. The proposed MRF‐DPUNet++ improves bone‐region segmentation accuracy and prediction‐feature discrimination in CT images of tibial and fibular fractures, providing segmentation support for fracture‐region analysis, three‐dimensional reconstruction, and robot‐assisted reduction.