Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry

Authors

Connor Elkhill, Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA. Electronic address: connor.2.elkhill@cuanschutz.edu.
Jiawei Liu, Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
Marius George Linguraru, Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, 7144 13th Pl NW, Washington, DC 20012, USA; Departments of Radiology and Pediatrics, George Washington University School of Medicine and Health Sciences, Ross Hall, 2300 Eye Street, NW, Washington, DC 20037, USA.
Scott LeBeau, Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA.
David Khechoyan, Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA; Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA.
Brooke French, Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA; Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA.
Antonio R. Porras, Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA; Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA; Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA; Department of Pediatrics and Department of Neurosurgery, School of Medicine, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA.

Document Type

Journal Article

Publication Date

6-25-2023

Journal

Computer methods and programs in biomedicine

Volume

240

DOI

10.1016/j.cmpb.2023.107689

Keywords

3D photogrammetry; Craniofacial imaging; Craniosynostosis; Geometric learning; Graph convolutional neural network; Landmark detection

Abstract

BACKGROUND AND OBJECTIVE: Accurate and repeatable detection of craniofacial landmarks is crucial for automated quantitative evaluation of head development anomalies. Since traditional imaging modalities are discouraged in pediatric patients, 3D photogrammetry has emerged as a popular and safe imaging alternative to evaluate craniofacial anomalies. However, traditional image analysis methods are not designed to operate on unstructured image data representations such as 3D photogrammetry. METHODS: We present a fully automated pipeline to identify craniofacial landmarks in real time, and we use it to assess the head shape of patients with craniosynostosis using 3D photogrammetry. To detect craniofacial landmarks, we propose a novel geometric convolutional neural network based on Chebyshev polynomials to exploit the point connectivity information in 3D photogrammetry and quantify multi-resolution spatial features. We propose a landmark-specific trainable scheme that aggregates the multi-resolution geometric and texture features quantified at every vertex of a 3D photogram. Then, we embed a new probabilistic distance regressor module that leverages the integrated features at every point to predict landmark locations without assuming correspondences with specific vertices in the original 3D photogram. Finally, we use the detected landmarks to segment the calvaria from the 3D photograms of children with craniosynostosis, and we derive a new statistical index of head shape anomaly to quantify head shape improvements after surgical treatment. RESULTS: We achieved an average error of 2.74 ± 2.70 mm identifying Bookstein Type I craniofacial landmarks, which is a significant improvement compared to other state-of-the-art methods. Our experiments also demonstrated a high robustness to spatial resolution variability in the 3D photograms. Finally, our head shape anomaly index quantified a significant reduction of head shape anomalies as a consequence of surgical treatment. CONCLUSION: Our fully automated framework provides real-time craniofacial landmark detection from 3D photogrammetry with state-of-the-art accuracy. In addition, our new head shape anomaly index can quantify significant head phenotype changes and can be used to quantitatively evaluate surgical treatment in patients with craniosynostosis.

APA Citation

Elkhill, Connor; Liu, Jiawei; Linguraru, Marius George; LeBeau, Scott; Khechoyan, David; French, Brooke; and Porras, Antonio R., "Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry" (2023). GW Authored Works. Paper 2687.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/2687

Department

Radiology