filename     : Lin25a.pdf
entry        : Article
conference   : IPCAI 2025, Berlin, Germany 17-18 June, 2025
pages        : 1471–1479
year         : 2025
month        : May
title        : Evaluation of synthetic training data for 3D intraoral reconstruction of cleft patients from single images
subtitle     :
author       : Lasse Lingens, Yoriko Lill, Prasad Nalabothu, Benito K. Benitez, Andreas A. Mueller, Markus Gross, Barbara Solenthaler
booktitle    : 
ISSN/ISBN    : 1861-6429
editor       : 
publisher    : International Journal of Computer Assisted Radiology and Surgery
publ.place   :
volume       : 20
issue        : 7
language     : English
keywords     : Computer vision · Medical image processing · Registration and image fusion · Data annotation
abstract     : Purpose: This study investigates the effectiveness of synthetic training data in predicting 2D landmarks for 3D intraoral reconstruction in cleft lip and palate patients. We take inspiration from existing landmark prediction and 3D reconstruction techniques for faces and demonstrate their potential in medical applications. <br/><br/>

Methods: We generated both real and synthetic datasets from intraoral scans and videos. A convolutional neural network was trained using a negative-Gaussian log-likelihood loss function to predict 2D landmarks and their corresponding confidence scores. The predicted landmarks were then used to fit a statistical shape model to generate 3D reconstructions from individual images. We analyzed the model's performance on real patient data and explored the dataset size required to overcome the domain gap between synthetic and real images. <br/><br/>

Results: Our approach generates satisfying results on synthetic data and shows promise when tested on real data. The method achieves rapid 3D reconstruction from single images and can therefore provide significant value in day-to-day medical work. <br/><br/>

Conclusion: Our results demonstrate that synthetic training data are viable for training models to predict 2D landmarks and reconstruct 3D meshes in patients with cleft lip and palate. This approach offers an accessible, low-cost alternative to traditional methods, using smartphone technology for noninvasive, rapid, and accurate 3D reconstructions in clinical settings.