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

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

SUBMITTER: Elkhill C 

PROVIDER: S-EPMC10527531 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature