ArticleIEEE transactions on bio-medical engineering2022
Predictive Statistical Model of Early Cranial Development.
Article in IEEE transactions on bio-medical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Population-Driven Synthesis of Personalized Cranial Development From Cross-Sectional Pediatric CT Images.IEEE transactions on bio-medical engineering · 2025Article
- Cranial Bone Changes Associated With Intracranial Hypertension in Apert Syndrome: Insights for Early Surgical Intervention.Plastic and reconstructive surgery. Global open · 2025Article
- Investigation of Cranial Bone Changes Indicative of Increased Intracranial Pressure in Diverse Phenotypes of Craniosynostosis.Plastic and reconstructive surgery. Global open · 2025Article
- Personalized phenotype encoding and prediction of pathological head development from cross-sectional images.... International Symposium on Medical Information Processing and Analysis. International SIPAIM Workshop · 2024Article
- Cranial bone thickness and density anomalies quantified from CT images can identify chronic increased intracranial pressure.Neuroradiology · 2024Article
- Data-driven cranial suture growth model enables predicting phenotypes of craniosynostosis.Scientific reports · 2023Article
- Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry.Computer methods and programs in biomedicine · 2023Article
- NEC-NET : Segmentation and Feature Extraction Network for the Neurocranium in Early Childhood.Proceedings of SPIE--the International Society for Optical Engineering · 2022Article
- Data-driven Normative Reference of Pediatric Cranial Bone Development.Plastic and reconstructive surgery. Global open · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveWe present a data-driven method to build a spatiotemporal statistical shape model predictive of normal cranial growth from birth to the age of 2 years.
methodsThe model was constructed using a normative cross-sectional computed tomography image dataset of 278 subjects. First, we propose a new standard representation of the calvaria using spherical maps to establish anatomical correspondences between subjects at the cranial sutures - the main areas of cranial bone expansion. Then, we model the cranial bone shape as a bilinear function of two factors: inter-subject anatomical variability and temporal growth. We estimate these factors using principal component analysis on the spatial and temporal dimensions, using a novel coarse-to-fine temporal multi-resolution approach to mitigate the lack of longitudinal images of the same patient.
resultsOur model achieved an accuracy of 1.54 ± 1.05 mm predicting development on an independent longitudinal dataset. We also used the model to calculate the cranial volume, cephalic index and cranial bone surface changes during the first two years of age, which were in agreement with clinical observations. SIGNIFICANCE: To our knowledge, this is the first data-driven and personalized predictive model of cranial bone shape development during infancy and it can serve as a baseline to study abnormal growth patterns in the population.
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Registered trials
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