Memon Afaque Rafique, Shi Haochen, Memon Tarique Rafique, Egger Jan, Chen Xiaojun
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Department of Mechanical Engineering, Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan.
Med Biol Eng Comput. 2025 May 2. doi: 10.1007/s11517-025-03363-5.
Defects to human crania are one kind of head bone damages, and cranial implants can be used to repair the defected crania. The automation of the implant design process is crucial in reducing the corresponding therapy time. Taking the cranial implant design problem as a special kind of shape completion task, an automatic cranial implant design workflow is proposed, which consists of a deep neural network for the direct shape prediction of the missing part of the defective cranium and conventional post-processing steps to refine the automatically generated implant. To evaluate the proposed workflow, we employ cross-validation and report an average Dice Similarity Score and boundary Dice Similarity Score of 0.81 and 0.81, respectively. We also measure the surface distance error using the 95th quantile of the Hausdorff Distance, which yields an average of 3.01 mm. Comparison with the manual cranial implant design procedure also revealed the convenience of the proposed workflow. In addition, a plugin is developed for 3D Slicer, which implements the proposed automatic cranial implant design workflow and can facilitate the end-users.
人类颅骨缺损是一种头部骨骼损伤，颅骨植入物可用于修复缺损的颅骨。植入物设计过程的自动化对于减少相应的治疗时间至关重要。将颅骨植入物设计问题视为一种特殊的形状完成任务，提出了一种自动颅骨植入物设计工作流程，该流程由一个用于直接预测缺损颅骨缺失部分形状的深度神经网络和用于细化自动生成的植入物的传统后处理步骤组成。为了评估所提出的工作流程，我们采用交叉验证，并报告平均骰子相似性分数和边界骰子相似性分数分别为0.81和0.81。我们还使用豪斯多夫距离的第95百分位数测量表面距离误差，其平均值为3.01毫米。与手动颅骨植入物设计程序的比较也揭示了所提出工作流程的便利性。此外，还为3D Slicer开发了一个插件，该插件实现了所提出的自动颅骨植入物设计工作流程，并可以为最终用户提供便利。
