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

Journal: International journal of oral and maxillofacial surgery
Published Date: Nov 22, 2025

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.

Authors

  • A Rashad
    Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Aachen, Germany.
  • M Beyer
    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: [email protected].
  • S Eftimie
    Department of Maxillofacial Surgery and Radiology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.
  • B Hinrichs-Puladi
    Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Aachen, Germany; Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany.
  • O Vladu
    Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Aachen, Germany.
  • K Xie
    Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Aachen, Germany; Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany.
  • D Truhn
    Department of Diagnostic and Interventional Radiology, Aachen University Hospital, Aachen, 52074, Germany; Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, 52074, Germany. Electronic address: [email protected].
  • F M Thieringer
    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.
  • T Gander
    Department of Oral and Maxillofacial Surgery, Lucerne Cantonal Hospital, Lucerne, Switzerland.
  • F Hölzle
    Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Aachen, Germany.
  • J Egger
    Institute for AI in Medicine (IKIM), University Hospital Essen, Essen, Germany.
  • R R Ilesan
    Department of Oral and Maxillofacial Surgery, Lucerne Cantonal Hospital, Lucerne, Switzerland.

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