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Three-Dimensional Shape Modeling and Analysis of Brain Structures
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Thorax shape reconstruction from limited CT-digitized palpable landmarks using statistical shape modeling.

Ricardo Alcobia1, João Folgado1, Carlos Quental2

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

Medical & Biological Engineering & Computing
|July 11, 2026
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Summary

This study developed statistical shape models (SSMs) to reconstruct subject-specific thorax geometries from limited skin landmarks. The methods accurately capture global thorax shape but show limitations in local anatomical detail.

Keywords:
Anatomical landmarksComputational modelingSkin-to-bone landmark mappingStatistical shape modelingSubject-specific

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Computational Anatomy

Background:

  • Accurate 3D thorax reconstruction is crucial for biomechanical analysis and medical applications.
  • Traditional methods often require extensive data, limiting their use in motion analysis studies.
  • Statistical Shape Models (SSMs) offer a promising approach for data-efficient reconstruction.

Purpose of the Study:

  • To develop and evaluate algorithms for reconstructing subject-specific thorax geometries using statistical shape models (SSMs).
  • To assess the accuracy of thorax reconstruction from limited skin landmarks.
  • To compare bone-landmark-based and skin-landmark-embedded SSM approaches.

Main Methods:

  • Developed two thorax SSMs from CT scans of 76 individuals.
  • Implemented bone-landmark-based (SSM-BL) and skin-landmark-embedded (SSM-SL) reconstruction strategies.
  • Quantified skin-to-bone mapping and thorax reconstruction accuracy using MAE and RMSE.

Main Results:

  • Skin-to-bone mapping achieved MAE and RMSE below 5 mm.
  • Thorax reconstruction errors (MAE) ranged from 8.26 ± 1.96 mm to 8.36 ± 1.70 mm.
  • No significant differences were found between the SSM-BL and SSM-SL reconstruction strategies.
  • Global thorax morphology was captured, but local anatomical details were less accurately represented.

Conclusions:

  • SSM-based methods enable thorax reconstruction from sparse skin landmark data.
  • The developed algorithms effectively capture the overall shape of the thorax.
  • Further refinement is needed to improve the representation of local anatomical features.