Automated landmarking for palatal shape analysis using geometric deep learning

Objectives To develop and evaluate a geometric deep‐learning network to automatically place seven palatal landmarks on digitized maxillary dental casts. Settings and Sample Population The sample comprised individuals with permanent dentition of various ethnicities. The network was trained from manua...

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Bibliographic Details
Published in:Orthodontics & craniofacial research 2021-12, Vol.24 (S2), p.144-152
Main Authors: Croquet, Balder, Matthews, Harold, Mertens, Jules, Fan, Yi, Nauwelaers, Nele, Mahdi, Soha, Hoskens, Hanne, El Sergani, Ahmed, Xu, Tianmin, Vandermeulen, Dirk, Bronstein, Michael, Marazita, Mary, Weinberg, Seth, Claes, Peter
Format: Article
Language:English
Subjects:
3D shape analysis
automatic landmarking
Deep Learning
Dentition
geometric deep learning
Humans
Imaging, Three-Dimensional
Maxilla
Palate
Quality control
Reproducibility of Results
Teeth
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Summary:Objectives To develop and evaluate a geometric deep‐learning network to automatically place seven palatal landmarks on digitized maxillary dental casts. Settings and Sample Population The sample comprised individuals with permanent dentition of various ethnicities. The network was trained from manual landmark annotations on 732 dental casts and evaluated on 104 dental casts. Materials and Methods A geometric deep‐learning network was developed to hierarchically learn features from point‐clouds representing the 3D surface of each cast. These features predict the locations of seven palatal landmarks. Results Repeat‐measurement reliability was
ISSN:1601-6335
1601-6343
DOI:10.1111/ocr.12513