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Virtual orthognathic surgical planning involves simulating surgicalcorrections of jaw deformities on 3D facial bony shape models. Due to the lackof necessary guidance, the planning procedure is highly experience-dependentand the planning results are often suboptimal. A reference facial bony shapemodel representing normal anatomies can provide an objective guidance toimprove planning accuracy. Therefore, we propose a self-supervised deepframework to automatically estimate reference facial bony shape models. Ourframework is an end-to-end trainable network, consisting of a simulator and acorrector. In the training stage, the simulator maps jaw deformities of apatient bone to a normal bone to generate a simulated deformed bone. Thecorrector then restores the simulated deformed bone back to normal. In theinference stage, the trained corrector is applied to generate apatient-specific normal-looking reference bone from a real deformed bone. Theproposed framework was evaluated using a clinical dataset and compared with astate-of-the-art method that is based on a supervised point-cloud network.Experimental results show that the estimated shape models given by our approachare clinically acceptable and significantly more accurate than that of thecompeting method.

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