AutoSkull: Learning-Based Skull Estimation for Automated Pipelines
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Author / Producer
Date
2024
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
In medical imaging, accurately representing facial features is crucial for applications such as radiation-free medical visualizations and treatment simulations. We aim to predict skull shapes from 3D facial scans with high accuracy, prioritizing simplicity for seamless integration into automated pipelines. Our method trains an MLP network on PCA coefficients using data from registered skin- and skull-mesh pairs obtained from CBCT scans, which is then used to infer the skull shape for a given skin surface. By incorporating teeth positions as additional prior information extracted from intraoral scans, we further improve the accuracy of the model, outperforming previous work. We showcase a clinical application of our work, where the inferred skull information is used in an FEM model to compute the outcome of an orthodontic treatment.
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Publication status
published
External links
Book title
Medical Image Computing and Computer Assisted Intervention – MICCAI 2024: 27th International Conference, Marrakesh, Morocco, October 6–10, 2024, Proceedings, Part VII
Journal / series
Volume
15007
Pages / Article No.
109 - 118
Publisher
Springer
Event
27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Machine Learning; Digital Patient; Skull Estimation; Mesh Processing
Organisational unit
03420 - Gross, Markus (emeritus) / Gross, Markus (emeritus)
08736 - Solenthaler, Barbara (Tit.-Prof.) / Solenthaler, Barbara (Tit.-Prof.)