Xie Ke, Yu Mingqian, Liu Jeremy Ho-Pak, Ma Qixiang, Zou Limin, Man Gene Chi-Wai, Xu Jiankun, Yung Patrick Shu-Hang, Li Zheng, Ong Michael Tim-Yun
Department of Surgery, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
Department of Orthopaedics and Traumatology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
Bioengineering (Basel). 2025 May 15;12(5):527. doi: 10.3390/bioengineering12050527.
Evaluating bone tunnels is crucial for assessing functional recovery after anterior cruciate ligament reconstruction. Conventional methods are imprecise, time-consuming, and labor-intensive. This study introduces a novel deep learning-based system for accurate bone tunnel segmentation and assessment. The system has two primary stages. Firstly, the ResNet50-Unet network is employed to capture the bone tunnel area in each slice. Subsequently, in the bone texture analysis, the open-source software 3D Slicer is leveraged to execute three-dimensional reconstruction based on the segmented outcomes from the previous stage. The ResNet50-Unet network was trained and validated using a newly developed dataset named tunnel bone segmentation (TB-Seg). The outcomes reveal commendable performance metrics, with mean intersection over union (mIoU), mean average precision (mAP), precision, and recall on the validation set reaching 76%, 85%, 88%, and 85%, respectively. To assess the robustness of our innovative bone texture system, we conducted tests on a cohort of 24 patients, successfully extracting bone volume/total volume, trabecular thickness, trabecular separation, trabecular number, and volumetric information. The system excels with substantial significance in facilitating the subsequent analysis of the intricate interplay between bone tunnel characteristics and the postoperative recovery trajectory after anterior cruciate ligament reconstruction. Furthermore, in our five randomly selected cases, clinicians utilizing our system completed the entire analytical workflow in a mere 357-429 s, representing a substantial improvement compared to the conventional duration exceeding one hour.
评估骨隧道对于评估前交叉韧带重建术后的功能恢复至关重要。传统方法不精确、耗时且劳动强度大。本研究引入了一种基于深度学习的新型系统，用于准确的骨隧道分割和评估。该系统有两个主要阶段。首先，使用ResNet50-Unet网络来捕捉每个切片中的骨隧道区域。随后，在骨纹理分析中，利用开源软件3D Slicer基于上一阶段的分割结果进行三维重建。ResNet50-Unet网络使用一个名为隧道骨分割（TB-Seg）的新开发数据集进行训练和验证。结果显示出令人满意的性能指标，验证集上的平均交并比（mIoU）、平均平均精度（mAP）、精度和召回率分别达到76%、85%、88%和85%。为了评估我们创新的骨纹理系统的稳健性，我们对24名患者进行了测试，成功提取了骨体积/总体积、小梁厚度、小梁间距、小梁数量和体积信息。该系统在促进对骨隧道特征与前交叉韧带重建术后恢复轨迹之间复杂相互作用的后续分析方面具有显著优势。此外，在我们随机选择的5个病例中，使用我们系统的临床医生仅用357 - 429秒就完成了整个分析流程，与超过一小时的传统时长相比有了显著改善。
