3D-2D Distance Maps Conversion Enhances Classification of Craniosynostosis | JoVE Visualize

Area of Science:

Medical Imaging
Artificial Intelligence
Pediatric Surgery

Background:

Craniosynostosis diagnosis traditionally relies on computed tomography (CT), exposing infants to ionizing radiation.
Photogrammetric 3D surface scans offer a radiation-free alternative but require advanced analysis methods.
Developing effective, non-invasive diagnostic tools is crucial for early intervention in pediatric care.

Purpose of the Study:

To develop a novel 3D surface scan to 2D distance map conversion method for craniosynostosis classification.
To enable the application of convolutional neural networks (CNNs) for diagnosing craniosynostosis using 2D representations of 3D data.
To evaluate the performance of this approach against traditional methods, focusing on accuracy, computational efficiency, and data augmentation potential.

Main Methods:

A coordinate transformation, ray casting, and distance extraction process was used to create 2D distance maps from 3D surface scans.
A CNN-based classification pipeline, specifically Resnet18, was implemented and compared with alternative classifiers.
Investigations included low-resolution sampling, data augmentation techniques, and attribution mapping to understand classification drivers.

Main Results:

The Resnet18 classifier achieved a high F1-score of 0.964 and an accuracy of 98.4% on a dataset of 496 patients.
Data augmentation significantly improved classifier performance across all tested models.
Low-resolution sampling reduced computational cost by 256-fold during ray casting while maintaining a high F1-score of 0.92.

Conclusions:

The 2D distance map conversion method effectively enhances craniosynostosis classification using CNNs, offering benefits in data augmentation and anonymity.
Photogrammetric 3D surface scans are a viable tool for clinical craniosynostosis diagnosis, with potential for domain transfer to CT imaging.
This radiation-free method holds promise for reducing ionizing radiation exposure in infants and improving diagnostic workflows.