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

Computer Methods and Programs in Biomedicine, vol. 273, pp. 109113

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.

Authors 7

1. Matthias Deitermann

   University of Stuttgart

   Affiliation as printed

   Inzipio GmbH, Krantzstraße 7, Aachen, 52070, Germany; University of Stuttgart, Keplerstraße 7, Stuttgart, 70174, Germany

2. Tobias Pankert Aachen Inzipio GmbH Department of Oral and Maxillofacial Surgery

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Inzipio GmbH, Krantzstraße 7, Aachen, 52070, Germany; Department of Oral and Maxillofacial Surgery, University Hospital RWTH, Pauwelstraße 30, Aachen, 52074, Germany

3. Srikrishna Jaganathan Aachen Inzipio GmbH

   RWTH Aachen University

   Affiliation as printed

   Inzipio GmbH, Krantzstraße 7, Aachen, 52070, Germany

4. Oliver Röhrle

   University of Stuttgart

   Affiliation as printed

   Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Pfaffenwaldring 5a, Stuttgart, 70569, Germany

5. Frank Hölzle Aachen Department of Oral and Maxillofacial Surgery

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Oral and Maxillofacial Surgery, University Hospital RWTH, Pauwelstraße 30, Aachen, 52074, Germany

6. Ali Modabber Aachen Department of Oral and Maxillofacial Surgery

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Oral and Maxillofacial Surgery, University Hospital RWTH, Pauwelstraße 30, Aachen, 52074, Germany

7. Stefan Raith corresponding Aachen Inzipio GmbH Department of Oral and Maxillofacial Surgery

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Inzipio GmbH, Krantzstraße 7, Aachen, 52070, Germany; Department of Oral and Maxillofacial Surgery, University Hospital RWTH, Pauwelstraße 30, Aachen, 52074, Germany. Electronic address: sraith@ukaachen.de