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Updated: Sep 18, 2025

Measurement of Dynamic Scapular Kinematics Using an Acromion Marker Cluster to Minimize Skin Movement Artifact

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Reconstruction of Scapula Bone Shapes from Digitized Skin Landmarks Using Statistical Shape Modeling and Multiple

Augusto Marques¹, João Folgado¹, Carlos Quental²

¹IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1049-001, Lisbon, Portugal.

Annals of Biomedical Engineering

|June 19, 2025

Summary

This summary is machine-generated.

This study developed an algorithm to reconstruct scapula bone shapes from skin landmarks using a statistical shape model (SSM). The method accurately generates subject-specific computational models without medical imaging.

Keywords:

3D shape reconstruction Bone landmark prediction Computational modeling Scapula Statistical shape model Subject-specific

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Last Updated: Sep 18, 2025

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Area of Science:

Biomechanical Engineering
Medical Imaging
Computational Anatomy

Background:

Accurate 3D scapula models are crucial for biomechanical analysis and surgical planning.
Current methods often require extensive medical imaging, increasing cost and complexity.
Developing non-invasive techniques for scapula shape reconstruction is highly desirable.

Purpose of the Study:

To develop and validate an algorithm for reconstructing subject-specific scapula bone shapes.
To utilize skin surface landmarks and a statistical shape model (SSM) for this reconstruction.
To assess the accuracy and reliability of the developed algorithm.

Main Methods:

Utilized 56 scapula segmentations and 4 key scapular bone and skin landmarks.
Developed regression models to predict bone landmark coordinates from skin landmarks, incorporating subject-specific variables (sex, age, weight, height).
Reconstructed scapula shapes by fitting the SSM's mean shape bone landmarks to the predicted bone landmarks.

Main Results:

Regression models achieved R² values from 0.70 to 0.98, with a maximum median error of 4 mm.
Average surface-to-surface errors were 2.41 mm (digitized) and 2.45 mm (predicted bone landmarks).
No significant statistical differences were found between shapes reconstructed using digitized versus predicted bone landmarks.

Conclusions:

The developed algorithm reliably derives subject-specific scapula shapes from experimentally acquired data.
Scapula shape reconstruction using a limited set of landmarks can generate accurate computational models.
This approach obviates the need for additional medical imaging, offering a more accessible method.