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Related Experiment Video

Updated: Jun 4, 2025

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Exploring shape changes in healthy bone growth through 3D spatiotemporal statistical shape models: A scoping review.

Lily E de Vries^1,2, Derek F R van Loon¹, Eline M van Es¹

¹Erasmus MC, University Medical Center Rotterdam, Department of Orthopedics and Sports Medicine, 3000 CA Rotterdam, the Netherlands.

|December 19, 2024

Summary

Spatiotemporal statistical shape modeling reveals how bone shape changes during healthy growth. This technique can predict bone geometry using factors like age and sex.

Keywords:

Bone growth Morphological model Patient-specific model Statistical shape model Three-dimensional imaging

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Last Updated: Jun 4, 2025

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Area of Science:

Biomedical Engineering
Orthopedics
Developmental Biology

Background:

Analyzing population trends in bone shape variation offers insights into growth processes.
3D skeletal structures are crucial for understanding human development.

Purpose of the Study:

To review state-of-the-art spatiotemporal statistical shape modeling techniques.
To emphasize their application to 3D skeletal structures during healthy growth.

Main Methods:

Searched PubMed and Scopus for relevant studies using pediatric spatiotemporal datasets of 3D healthy bone models.
Extracted dataset characteristics, shape model development, analyses, and potential clinical use.
Focused on Principal Component Analysis (PCA) on point distribution models for statistical shape modeling.

Main Results:

Fourteen studies analyzed various bones (lower limb, mandible, skull, vertebrae).
Unscaled models yielded more compact statistical shape models than scaled models.
A significant correlation between the first shape mode and age was reported in four studies.
3D model reconstruction using prediction features achieved Root Mean Squared Errors below 3 mm.

Conclusions:

Spatiotemporal statistical shape modeling provides insights into growth-related shape variations.
These models can identify predictive factors (e.g., age, sex) for bone geometry prediction.