Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, 77030, USA.
Int J Comput Assist Radiol Surg. 2024 Jul;19(7):1439-1447. doi: 10.1007/s11548-024-03123-0. Epub 2024 Jun 13.
Accurate estimation of reference bony shape models is fundamental for orthognathic surgical planning. Existing methods to derive this model are of two types: one determines the reference model by estimating the deformation field to correct the patient's deformed jaw, often introducing distortions in the predicted reference model; The other derives the reference model using a linear combination of their landmarks/vertices but overlooks the intricate nonlinear relationship between the subjects, compromising the model's precision and quality.
We have created a self-supervised learning framework to estimate the reference model. The core of this framework is a deep query network, which estimates the similarity scores between the patient's midface and those of the normal subjects in a high-dimensional space. Subsequently, it aggregates high-dimensional features of these subjects and projects these features back to 3D structures, ultimately achieving a patient-specific reference model.
Our approach was trained using a dataset of 51 normal subjects and tested on 30 patient subjects to estimate their reference models. Performance assessment against the actual post-operative bone revealed a mean Chamfer distance error of 2.25 mm and an average surface distance error of 2.30 mm across the patient subjects.
Our proposed method emphasizes the correlation between the patients and the normal subjects in a high-dimensional space, facilitating the generation of the patient-specific reference model. Both qualitative and quantitative results demonstrate its superiority over current state-of-the-art methods in reference model estimation.
准确估计参考骨形状模型是正颌手术规划的基础。现有的推导该模型的方法有两种类型：一种通过估计校正患者变形下颌的变形场来确定参考模型，这通常会在预测的参考模型中引入扭曲；另一种方法是使用其地标/顶点的线性组合来推导参考模型，但忽略了主体之间复杂的非线性关系，从而影响模型的精度和质量。
我们创建了一个自监督学习框架来估计参考模型。该框架的核心是一个深度查询网络，它在高维空间中估计患者的中面部与正常受试者的相似性得分。然后，它聚合这些受试者的高维特征，并将这些特征投影回 3D 结构，最终实现患者特定的参考模型。
我们的方法使用 51 名正常受试者的数据集进行训练，并在 30 名患者受试者上进行测试，以估计他们的参考模型。对实际术后骨骼的性能评估显示，患者组的平均 Chamfer 距离误差为 2.25 毫米，平均表面距离误差为 2.30 毫米。
我们提出的方法强调了患者和正常受试者在高维空间中的相关性，有助于生成患者特定的参考模型。定性和定量结果都表明，该方法在参考模型估计方面优于当前最先进的方法。
