Yu Hui, Mu Qiyue, Wang Zhi, Guo Yu, Zhao Jing, Wang Guangpu, Wang Qingsong, Meng Xianghong, Dong Xiaoman, Wang Shuo, Sun Jinglai
State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University School of Medicine, Tianjin, China.
Department of Biomedical Engineering, Tianjin University School of Medicine, Tianjin, China.
Front Med (Lausanne). 2025 Mar 24;12:1547588. doi: 10.3389/fmed.2025.1547588. eCollection 2025.
Early diagnosis of non-union fractures is vital for treatment planning, yet studies using bone morphometric parameters for this purpose are scarce. This study aims to create a fracture micro-CT image dataset, design a deep learning algorithm for fracture segmentation, and develop an early diagnosis model for fracture non-union.
Using fracture animal models, micro-CT images from 12 rats at various healing stages (days 1, 7, 14, 21, 28, and 35) were analyzed. Fracture lesion frames were annotated to create a high-resolution dataset. We proposed the Vision Mamba Triplet Attention and Edge Feature Decoupling Module UNet (VM-TE-UNet) for fracture area segmentation. And we extracted bone morphometric parameters to establish an early diagnostic evaluation system for the non-union of fractures.
A dataset comprising 2,448 micro-CT images of the rat fracture lesions with fracture Region of Interest (ROI), bone callus and healing characteristics was established and used to train and test the proposed VM-TE-UNet which achieved a Dice Similarity Coefficient of 0.809, an improvement over the baseline's 0.765, and reduced the 95th Hausdorff Distance to 13.1. Through ablation studies, comparative experiments, and result analysis, the algorithm's effectiveness and superiority were validated. Significant differences ( < 0.05) were observed between the fracture and fracture non-union groups during the inflammatory and repair phases. Key indices, such as the average CT values of hematoma and cartilage tissues, BS/TS and BS/TV of mineralized cartilage, BS/TV of osteogenic tissue, and BV/TV of osteogenic tissue, align with clinical methods for diagnosing fracture non-union by assessing callus presence and local soft tissue swelling. On day 14, the early diagnosis model achieved an AUC of 0.995, demonstrating its ability to diagnose fracture non-union during the soft-callus phase.
This study proposed the VM-TE-UNet for fracture areas segmentation, extracted micro-CT indices, and established an early diagnostic model for fracture non-union. We believe that the prediction model can effectively screen out samples of poor fracture rehabilitation caused by blood supply limitations in rats 14 days after fracture, rather than the widely accepted 35 or 40 days. This provides important reference for the clinical prediction of fracture non-union and early intervention treatment.
骨折不愈合的早期诊断对于治疗方案的制定至关重要，但利用骨形态计量学参数进行此类诊断的研究较少。本研究旨在创建一个骨折微计算机断层扫描（micro-CT）图像数据集，设计一种用于骨折分割的深度学习算法，并开发一种骨折不愈合的早期诊断模型。
使用骨折动物模型，分析了12只大鼠在不同愈合阶段（第1、7、14、21、28和35天）的micro-CT图像。对骨折病变帧进行标注以创建一个高分辨率数据集。我们提出了视觉曼巴三重注意力和边缘特征解耦模块U-Net（VM-TE-UNet）用于骨折区域分割。并且我们提取了骨形态计量学参数以建立骨折不愈合的早期诊断评估系统。
建立了一个包含2448张大鼠骨折病变的micro-CT图像的数据集，这些图像具有骨折感兴趣区域（ROI）、骨痂和愈合特征，并用于训练和测试所提出的VM-TE-UNet，该模型的骰子相似系数达到0.809，比基线的0.765有所提高，并将第95百分位豪斯多夫距离降低到13.1。通过消融研究、对比实验和结果分析，验证了该算法的有效性和优越性。在炎症期和修复期，骨折组与骨折不愈合组之间观察到显著差异（<0.05）。关键指标，如血肿和软骨组织的平均CT值、矿化软骨的骨表面积/组织表面积（BS/TS）和骨表面积/组织体积（BS/TV）、成骨组织的BS/TV以及成骨组织的骨体积/组织体积（BV/TV），与通过评估骨痂存在和局部软组织肿胀来诊断骨折不愈合的临床方法一致。在第14天，早期诊断模型的曲线下面积（AUC）达到0.995，表明其能够在软骨痂期诊断骨折不愈合。
本研究提出了VM-TE-UNet用于骨折区域分割，提取了micro-CT指标，并建立了骨折不愈合的早期诊断模型。我们认为该预测模型能够有效筛选出骨折后14天因血供受限导致骨折愈合不良的样本，而不是广泛认可的35天或40天。这为骨折不愈合的临床预测和早期干预治疗提供了重要参考。
