Photographic cranial shape analysis using deep learning

Mohammad Ali Yektaie; Zahra Ghasemi; S. Hossein Hezaveh; Fereshteh Aalamifar; Reza Seifabadi; Marius George Linguraru

doi:10.1117/12.2581990

15 February 2021 Photographic cranial shape analysis using deep learning

Mohammad Ali Yektaie, Zahra Ghasemi, S. Hossein Hezaveh, Fereshteh Aalamifar, Reza Seifabadi, Marius George Linguraru

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 1159724 (2021) https://doi.org/10.1117/12.2581990
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Purpose: To determine the feasibility of using deep learning algorithms that can identify and classify types of cranial malformations, i.e., craniosynostosis and deformational plagiocephaly and brachycephaly (DPB), using top view photographs of the infant head. Method: We used 72 3D head volumes of infants with normal (13), DPB (34), and craniosynostosis (25). These volumes contain only information about the head shape and lack texture. From these 3D volumes, top-view 2D renderings were generated from different viewing angles. We generated 37 2D files were generated from each 3D volume, and we applied additional data augmentation to obtain a total of 5,254 2D images. We then used this dataset to investigate the performance of a well-known deep learning architectures for image classification, i.e., LeNet. The data were divided into training and test sets (85% and 15%, respectively with minimum one data of each class in the test set). We performed model evaluation by cross-validation. Results: The overall accuracy of the cranial shape analysis model was 87.5 ± 5.59%. Cases with craniosynostosis were identified with 0.99±0.01 accuracy, while subjects with DPB were identified with 0.78 ± 0.1. The accuracy of the model to identify normal cases without cranial deformation was 0.96 ± 0.04. Conclusions: Deep learning-based methods can be used for the accurate detection, and classification of different types of conditions with head malformation using 2D photographic data. These algorithms will be packaged as a mobile health solution to make a decision support tool available at the point-of-care.

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Citation Download Citation

Mohammad Ali Yektaie, Zahra Ghasemi, S. Hossein Hezaveh, Fereshteh Aalamifar, Reza Seifabadi, and Marius George Linguraru "Photographic cranial shape analysis using deep learning", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 1159724 (15 February 2021); https://doi.org/10.1117/12.2581990

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