目的 提出基于学习的颅颌面自动修复方法，在自主构建的数据集上进行学习，以自动生成缺损部分的形状，为复杂颅颌面结构的修复提供参考。 方法 基于头颅CT数据重建并标注了125例头骨数据，每一例构建21种缺陷分类，使用图像配准、阈值滤波等技术完成数据预处理，并提出一种新的颅颌面自动修复技术，完成颅颌面缺损部分的形状生成。 结果 提出的方法在CMF Defects数据集上能够重建出兼具美观和保护功能的形状。 结论 颅颌面骨骼形状各异，解剖结构复杂，本研究结合深度学习与数据驱动方法能很好地完成颅颌面骨骼缺损的生成，为颅颌面修复手术的术前规划和术中操作提供可靠的依据。
Objective To propose a learning-based automatic restoration method for craniomaxillofacial defects, which learning from a self-constructed dataset to automatically generate the shape of the defective parts, and providing reference for the restoration of complex craniomaxillofacial structures. Methods Based on the head CT data, 125 cases of skull data were reconstructed and annotated. Each case was categorized into 21 defect classes. Various techniques were used for data preprocessing, including image registration and threshold filtering. A novel craniomaxillofacial automatic restoration technique was introduced to generate shapes for the defective portions. Results The proposed method achieves the state-of-the-art results on the CMF Defects dataset, which can reconstruct shapes that combine aesthetics and protective functionality. Conclusions Craniomaxillofacial bone have diverse shapes and complex anatomical structures. This study, combined with deep learning and data-driven methods, can effectively generate the generation of craniomaxillofacial bone defects, providing a reliable foundation for preoperative planning and intraoperative procedures in craniomaxillofacial restoration surgery.
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