Pioneering fully automated bony orbit segmentation: an in silico nnU-Net multicentre approach

A Rashad; M Beyer; S Eftimie; B Hinrichs-Puladi; O Vladu; K Xie; D Truhn; F M Thieringer; T Gander; F Hölzle; J Egger; R R Ilesan

doi:10.1016/j.ijom.2025.11.002

Pioneering fully automated bony orbit segmentation: an in silico nnU-Net multicentre approach

Int J Oral Maxillofac Surg. 2025 Nov 22:S0901-5027(25)01496-1. doi: 10.1016/j.ijom.2025.11.002. Online ahead of print.

Authors

A Rashad¹, M Beyer², S Eftimie³, B Hinrichs-Puladi⁴, O Vladu¹, K Xie⁴, D Truhn⁵, F M Thieringer⁶, T Gander⁷, F Hölzle¹, J Egger⁸, R R Ilesan⁷

Affiliations

¹ Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Aachen, Germany.
² Department of Oral and Craniomaxillofacial Surgery, University Hospital Basel, Basel, Switzerland; Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland. Electronic address: michel.beyer@usb.ch.
³ Department of Maxillofacial Surgery and Radiology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.
⁴ Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Aachen, Germany; Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany.
⁵ Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.
⁶ Department of Oral and Craniomaxillofacial Surgery, University Hospital Basel, Basel, Switzerland; Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland.
⁷ Department of Oral and Maxillofacial Surgery, Lucerne Cantonal Hospital, Lucerne, Switzerland.
⁸ Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany.

PMID: 41274846
DOI: 10.1016/j.ijom.2025.11.002

Abstract

In maxillofacial surgery, orbital reconstruction requires precision to address both functional and aesthetic considerations arising from both acute and elective conditions. This study presents a novel, fully automated segmentation software designed specifically for the orbital floor. This software enhances surgical planning through superior accuracy, efficiency, and usability. The authors' transdisciplinary team compiled a dataset of 1004 expert-segmented orbits from computed tomography images across multiple countries, ensuring broad anatomical representation. Developed with the nnU-Net framework, the software achieved segmentation accuracy with a mean Dice similarity coefficient of 0.935 and a mean surface distance of 0.292 mm for the orbit, and a Dice similarity coefficient of 0.917 and mean surface distance of 0.287 mm for the orbital floor-all within approximately 1 s. This performance surpasses traditional manual segmentation, which averages 25 min per orbit. The system delivered consistent results across a range of imaging sources, affirming its reliability for a wide range of clinical applications. By introducing this fully automated, high-precision tool, this study pioneers advancements in AI-driven orbital reconstructions, setting new standards for patient-specific surgical planning. Further development and integration holds the key to transforming the field, ensuring ethical compliance and enhancing informed consent.

Keywords: Artificial intelligence; Computer-assisted image processing; Computer-assisted surgery; Maxillofacial surgery; Orbit.