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Related Concept Videos

Deformations in a Transverse Cross Section01:21

Deformations in a Transverse Cross Section

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When a material is subjected to uniaxial stress, it elongates or contracts in the direction of the applied force, and also undergoes changes in the perpendicular directions. This behavior is crucial for understanding how materials behave under stress and is governed by mechanical properties such as Poisson's ratio v, which measures the ratio of transverse strain to axial strain.
As the material stretches, it expands or contracts in orthogonal directions to the load. This phenomenon varies...
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Related Experiment Video

Updated: Apr 22, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

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Diffeomorphic shape trajectories for improved longitudinal segmentation and statistics.

Prasanna Muralidharan, James Fishbaugh, Hans J Johnson

    Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
    |October 17, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel two-stage statistical method to analyze longitudinal shape data, reducing variability from imaging and algorithms. The approach enhances statistical power for distinguishing shape trajectory differences in diseases like Huntington's disease (HD).

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    Area of Science:

    • Medical imaging analysis
    • Statistical modeling
    • Neurodegenerative disease research

    Background:

    • Longitudinal imaging studies track changes over time to quantify biological shape variability and differentiate between healthy and diseased populations.
    • Data variability in these studies is often influenced by image acquisition, calibration, expert judgment, and algorithmic limitations in segmentation and registration.
    • Accurate statistical analysis of shape changes over time is crucial for understanding disease progression and developing effective interventions.

    Purpose of the Study:

    • To propose a robust two-stage method for the statistical analysis of longitudinal shape data, minimizing unwanted variability.
    • To enhance statistical power for detecting differences in shape trajectories between distinct subject groups.
    • To validate the method's effectiveness using a longitudinal database from the PREDICT-HD study.

    Main Methods:

    • A two-stage statistical approach for longitudinal shape analysis.
    • Stage 1: Estimation of diffeomorphic shape trajectories to minimize cross-time segmentation inconsistencies.
    • Stage 2: Application of a longitudinal mixed-effects model to test for group differences in shape trajectories.

    Main Results:

    • The proposed method effectively reduces unwanted variability in both shape and derived measures (e.g., volume) from longitudinal imaging data.
    • Application to the PREDICT-HD database demonstrated improved statistical power.
    • The method successfully distinguished shape trajectory differences between healthy subjects and those with a genetic biomarker for Huntington's disease (HD).

    Conclusions:

    • The novel two-stage method provides a more accurate and powerful approach to analyzing longitudinal shape data.
    • This technique is valuable for identifying subtle shape changes associated with neurodegenerative diseases like Huntington's disease.
    • The findings suggest improved capabilities for distinguishing disease-related trajectories in clinical research and diagnostics.