A Self-Supervised Deep Framework for Reference Bony Shape Estimation in Orthognathic Surgical Planning

TL;DR

This paper introduces a self-supervised deep learning framework that automatically estimates reference bony shape models to enhance orthognathic surgical planning accuracy, reducing reliance on expert experience.

Contribution

The proposed end-to-end trainable framework uniquely combines a simulator and corrector to generate patient-specific normal bone models without requiring labeled data.

Findings

Outperforms state-of-the-art supervised methods in accuracy

Produces clinically acceptable reference bone models

Significantly improves surgical planning precision

Abstract

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.