Impact of data synthesis strategies for the classification of craniosynostosis

Author:

Schaufelberger Matthias,Kühle Reinald Peter,Wachter Andreas,Weichel Frederic,Hagen Niclas,Ringwald Friedemann,Eisenmann Urs,Hoffmann Jürgen,Engel Michael,Freudlsperger Christian,Nahm Werner

Abstract

IntroductionPhotogrammetric surface scans provide a radiation-free option to assess and classify craniosynostosis. Due to the low prevalence of craniosynostosis and high patient restrictions, clinical data are rare. Synthetic data could support or even replace clinical data for the classification of craniosynostosis, but this has never been studied systematically.MethodsWe tested the combinations of three different synthetic data sources: a statistical shape model (SSM), a generative adversarial network (GAN), and image-based principal component analysis for a convolutional neural network (CNN)–based classification of craniosynostosis. The CNN is trained only on synthetic data but is validated and tested on clinical data.ResultsThe combination of an SSM and a GAN achieved an accuracy of 0.960 and an F1 score of 0.928 on the unseen test set. The difference to training on clinical data was smaller than 0.01. Including a second image modality improved classification performance for all data sources.ConclusionsWithout a single clinical training sample, a CNN was able to classify head deformities with similar accuracy as if it was trained on clinical data. Using multiple data sources was key for a good classification based on synthetic data alone. Synthetic data might play an important future role in the assessment of craniosynostosis.

Funder

KIT-Publication Fund of the Karlsruhe Institute of Technology

Publisher

Frontiers Media SA

Subject

Management Science and Operations Research,Mechanical Engineering,Energy Engineering and Power Technology

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