Artificial intelligence-enabled automatic segmentation of skull CT facilitates computer-assisted craniomaxillofacial surgery

Abstract

Background: The image segmentation of skull CT is the cornerstone for the computer-assisted craniomaxillofacial surgery in multiple aspects. This study aims to introduce an AI-enabled automatic segmentation and propose its prospect in facilitating the computer-assisted surgery.

Methods: Three patients enrolled in a clinical trial of computer-assisted craniomaxillofacial surgery were randomly selected for this study. The preoperative helical CT scans of the head and neck region were subjected to the AI-enabled automatic segmentation in Mimics Viewer. The performance of AI segmentation was evaluated based on the requirements of computer-assisted surgery.

Results: All three patients were successfully segmented by the AI-enabled automatic segmentation. The performance of AI segmentation was excellent regarding key anatomical structures. The overall quality of bone surface was satisfying. The median DICE coefficient was 92.4% for the maxilla, and 94.9% for the mandible, which fulfilled the requirements of computer-assisted craniomaxillofacial surgery.

Conclusions: The AI-enabled automatic segmentation could facilitate the preoperative virtual planning and postoperative outcome verification, which formed a feedback loop to enhance the current workflow of computer-assisted surgery. More studies are warranted to confirm the robustness of AI segmentation with more cases.