Date: November 2025.
Source: 2025 21st International Symposium on Biomedical Image Processing and Analysis (SIPAIM), Pasto, Colombia, pp. 1-5, doi: 10.1109/SIPAIM67325.2025.11283426.
Abstract: Craniosynostosis is the early fusion of the cranial sutures and requires corrective surgery. Although computer aided surgical planning can help improve treatment outcomes, planning accuracy is limited due to the lack of methods to predict anatomical changes between diagnostic image acquisition and treatment and by the risk of repeated radiation exposure and/or sedation from longitudinal image acquisition. Recently, we leveraged 3D photogrammetry, a popular radiation-free alternative image modality that provides similar anatomical accuracy to computed tomography, to develop a generative neural network trained on cross-sectional data that enabled personalized prediction of pathological development from CT images or 3D photogrammetry. In this work, we present an improved generative network featuring a multi-headed discriminator to enforce conditional independence of age, sex, and pathology, enhanced domain adversarial training to create unbiased latent representations, and utilize bilinear upsampling blocks for higher anatomical fidelity. This model was trained on head surfaces from 744 patients with craniosynostosis and 1,302 normative children (ages 0–5) and evaluated our predictive accuracy on a longitudinal dataset of 76 patients with craniosynostosis and 33 normative subjects. The model outperformed existing methods with head surface growth prediction errors of 2.79 ± 1.01 mm and 2.82 ± 1.22 mm, and volumetric errors 55.9 ± 48.2 mL and 78.7 ± 92.4 mL, in patients with craniosynostosis and normative subjects, respectively. The predictions generated by our model could be used to improve the accuracy of surgical planning.
Article: A generative model with domain adversarial training and independent conditional discriminators to predict the development of craniosynostosis.
Authors: C Elkhill, IA Cruz-Guerrero, MG Linguraru, B French, AR Porras. Children’s Hospital Colorado, Aurora, CO, USA.