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Related Experiment Video

Updated: Dec 13, 2025

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

Published on: November 2, 2020

Handling confounding variables in statistical shape analysis - application to cardiac remodelling.

Gabriel Bernardino¹, Oualid Benkarim², María Sanz-de la Garza³

¹BCN Medtech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.

Medical Image Analysis

|July 27, 2020

Summary

Statistical shape analysis can reveal cardiac remodelling in athletes. Accounting for confounding factors like body mass index is crucial for accurate results, especially in imbalanced datasets.

Keywords:

Cardiac remodelling Computational anatomy Confounder correction Statistical shape analysis

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Last Updated: Dec 13, 2025

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Published on: November 2, 2020

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Area of Science:

Biomedical imaging
Statistical modeling
Cardiovascular research

Background:

Statistical shape analysis (SSA) is vital for understanding organ morphology and disease-related changes.
Existing SSA methods often overlook the impact of confounding variables like demographics on shape variability.
Imbalanced confounding factors can invalidate shape analysis results.

Purpose of the Study:

To introduce a linear SSA framework for identifying shape differences independent of confounding variables.
To incorporate confounding deflation and adjustment methods into SSA.
To investigate cardiac remodelling in triathletes versus controls using SSA.

Main Methods:

Developed a linear SSA framework with confounding deflation and adjustment.
Applied the framework to cardiac MRI data of triathletes and controls.
Introduced dataset imbalance by removing controls with low body mass index to test robustness.

Main Results:

Analysis of the full dataset showed increased ventricular volumes and myocardial mass in athletes.
Failure to account for confounders obscured the increase in myocardial mass.
Confounder adjustment methods were essential for detecting true cardiac remodelling patterns in imbalanced data.

Conclusions:

Confounding factors significantly impact SSA results, particularly in imbalanced datasets.
The proposed SSA framework with adjustment methods accurately identifies cardiac remodelling.
Proper handling of confounding variables is critical for reliable shape analysis in biomedical research.