A Self-Supervised Deep Framework for Reference Bony Shape Estimation in Orthognathic Surgical Planning
This work addresses the need for objective guidance in virtual orthognathic surgical planning to improve accuracy, which is currently experience-dependent and suboptimal, representing an incremental advancement in medical imaging.
The paper tackles the problem of estimating reference facial bony shape models for orthognathic surgical planning by proposing a self-supervised deep framework, which generates clinically acceptable shape models and significantly outperforms a state-of-the-art supervised method in accuracy.
Virtual orthognathic surgical planning involves simulating surgical corrections of jaw deformities on 3D facial bony shape models. Due to the lack of necessary guidance, the planning procedure is highly experience-dependent and the planning results are often suboptimal. A reference facial bony shape model representing normal anatomies can provide an objective guidance to improve planning accuracy. Therefore, we propose a self-supervised deep framework to automatically estimate reference facial bony shape models. Our framework is an end-to-end trainable network, consisting of a simulator and a corrector. In the training stage, the simulator maps jaw deformities of a patient bone to a normal bone to generate a simulated deformed bone. The corrector then restores the simulated deformed bone back to normal. In the inference stage, the trained corrector is applied to generate a patient-specific normal-looking reference bone from a real deformed bone. The proposed framework was evaluated using a clinical dataset and compared with a state-of-the-art method that is based on a supervised point-cloud network. Experimental results show that the estimated shape models given by our approach are clinically acceptable and significantly more accurate than that of the competing method.