Wu Chieh-Tsai, Yang Yao-Hung, Chang Yau-Zen
Department of Neurosurgery, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan.
College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Front Bioeng Biotechnol. 2023 Dec 11;11:1297933. doi: 10.3389/fbioe.2023.1297933. eCollection 2023.
Creating a personalized implant for cranioplasty can be costly and aesthetically challenging, particularly for comminuted fractures that affect a wide area. Despite significant advances in deep learning techniques for 2D image completion, generating a 3D shape inpainting remains challenging due to the higher dimensionality and computational demands for 3D skull models. Here, we present a practical deep-learning approach to generate implant geometry from defective 3D skull models created from CT scans. Our proposed 3D reconstruction system comprises two neural networks that produce high-quality implant models suitable for clinical use while reducing training time. The first network repairs low-resolution defective models, while the second network enhances the volumetric resolution of the repaired model. We have tested our method in simulations and real-life surgical practices, producing implants that fit naturally and precisely match defect boundaries, particularly for skull defects above the Frankfort horizontal plane.
定制颅骨修补植入物成本高昂且在美学上具有挑战性，尤其是对于大面积的粉碎性骨折。尽管二维图像补全的深度学习技术取得了重大进展，但由于三维颅骨模型的维度更高且计算需求更大，生成三维形状的图像修复仍然具有挑战性。在此，我们提出一种实用的深度学习方法，可从CT扫描创建的有缺陷三维颅骨模型生成植入物几何形状。我们提出的三维重建系统由两个神经网络组成，可生成适用于临床的高质量植入物模型，同时减少训练时间。第一个网络修复低分辨率的缺陷模型，而第二个网络提高修复模型的体积分辨率。我们已在模拟和实际手术操作中测试了我们的方法，所生产的植入物贴合自然且能精确匹配缺损边界，特别是对于法兰克福水平面以上的颅骨缺损。
