SHAPE: A visual computing pipeline for interactive landmarking of 3D photograms and patient reporting for assessing craniosynostosis.
Carsten G��rg, Connor Elkhill, Jasmine Chaij, Kristin Royalty, Phuong D Nguyen, Brooke French, Ines A Cruz-Guerrero, Antonio R Porras
Author Information
Carsten G��rg: Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 East 17th Place, Aurora, CO 80045, USA.
Connor Elkhill: Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 East 17th Place, Aurora, CO 80045, USA.
Jasmine Chaij: Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, 13123 E 16th Ave, Aurora, CO 80045, USA.
Kristin Royalty: Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, 13123 E 16th Ave, Aurora, CO 80045, USA.
Phuong D Nguyen: Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, 13123 E 16th Ave, Aurora, CO 80045, USA.
Brooke French: Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, 13123 E 16th Ave, Aurora, CO 80045, USA.
Ines A Cruz-Guerrero: Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 East 17th Place, Aurora, CO 80045, USA.
Antonio R Porras: Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 East 17th Place, Aurora, CO 80045, USA.
3D photogrammetry is a cost-effective, non-invasive imaging modality that does not require the use of ionizing radiation or sedation. Therefore, it is specifically valuable in pediatrics and is used to support the diagnosis and longitudinal study of craniofacial developmental pathologies such as craniosynostosis - the premature fusion of one or more cranial sutures resulting in local cranial growth restrictions and cranial malformations. Analysis of 3D photogrammetry requires the identification of craniofacial landmarks to segment the head surface and compute metrics to quantify anomalies. Unfortunately, commercial 3D photogrammetry software requires intensive manual landmark placements, which is time-consuming and prone to errors. We designed and implemented SHAPE, a System for Head-shape Analysis and Pediatric Evaluation. It integrates our previously developed automated landmarking method in a visual computing pipeline to evaluate a patient's 3D photogram while allowing for manual confirmation and correction. It also automatically computes advanced metrics to quantify craniofacial anomalies and automatically creates a report that can be uploaded to the patient's electronic health record. We conducted a user study with a professional clinical photographer to compare SHAPE to the existing clinical workflow. We found that SHAPE allows for the evaluation of a craniofacial 3D photogram more than three times faster than the current clinical workflow (3.85 �� 0.99 vs. 13.07 �� 5.29 minutes, < 0.001). Our qualitative study findings indicate that the SHAPE workflow is well aligned with the existing clinical workflow and that SHAPE has useful features and is easy to learn.
