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. 2026 Jan:273:109113.
doi: 10.1016/j.cmpb.2025.109113. Epub 2025 Oct 11.

Automated detection of mandibular landmarks in CT data using a dual-input approach in a two-stage design

Matthias Deitermann  1 , Tobias Pankert  2 , Srikrishna Jaganathan  3 , Oliver Röhrle  4 , Frank Hölzle  5 , Ali Modabber  5 , Stefan Raith  6
Affiliations
Free article

Automated detection of mandibular landmarks in CT data using a dual-input approach in a two-stage design

Matthias Deitermann et al. Comput Methods Programs Biomed. 2026 Jan.
Free article

Abstract

Background and objective: Identification of anatomical landmarks in 3D imaging data is an essential step in patient-specific cranio-maxillofacial surgery. Today, precise landmark localization remains largely manual, prone to inter-operator variability, and a bottleneck in streamlined workflows of digitalized preoperative planning, that have in recent years, become a key aspect of cranio-maxillofacial surgery. In clinical practice, bone segmentation and landmark detection in CT imaging is often avoided and automated solutions fall back to the analysis of 2D cephalograms.

Methods: This work investigates different pipelines to automate the process of landmark localization in the mandible from volumetric CT imaging using convolutional neural networks. As a central element, a 3D U-Net architecture is employed to treat landmark localization and classification like a multi-label segmentation problem. We leverage a two-stage coarse-to-fine approach to tackle heterogeneous input data and preserve high resolution for the final prediction. Our primary innovation is a novel dual-input architecture for the second stage, which uses both the cropped CT data and a mandible segmentation to provide the model with explicit geometric priors for improved accuracy. The method was developed and tested on a clinical dataset comprising 287 CT datasets to localize nine different landmarks on the human mandible, including the Condyles, Coronoids, Gonions, Pogonion, Gnathion and Menton.

Results: On a test dataset of 29 CTs, landmarks were predicted with a mean absolute error of 1.40±1.04 while successfully predicting 99.6% of all landmarks.

Conclusion: The proposed method demonstrates high accuracy, robustness, and speed suggesting strong potential for integration into clinical workflows for automated, patient-specific surgical planning in cranio-maxillofacial surgery.

Keywords: CT; Cranio-maxillofacial surgery; Deep learning; Landmark detection; Mandibular landmarks; Surgical planning; UNet.

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Conflict of interest statement

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Stefan Raith reports a relationship with Inzipio GmbH that includes: employment and equity or stocks. Tobias Pankert reports a relationship with Inzipio GmbH that includes: employment and equity or stocks. Ali Modabber reports a relationship with Inzipio GmbH that includes: equity or stocks. Srikrishna Jaganathan reports a relationship with Inzipio GmbH that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.