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Computed Tomography01:10

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models.

Xin Yu1, Qi Yang1, Yucheng Tang2

  • 1Computer Science, Vanderbilt University, Nashville, TN, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|December 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces C-SliceGen, a novel method to harmonize body composition analysis from longitudinal computed tomography (CT) scans. It reduces positional variance in abdominal slices, improving health and aging research.

Keywords:
Abdominal slice generationBody compositionLongitudinal data harmonization

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • 2D abdominal CT slices allow body composition measurement, crucial for aging health studies.
  • Longitudinal analysis is hindered by positional variance in CT slices taken over time.
  • Standardizing these slices is essential for accurate body composition tracking.

Purpose of the Study:

  • To develop a method (C-SliceGen) to reduce positional variance in longitudinal 2D abdominal CT slices.
  • To enable more accurate body composition analysis over time for aging research.
  • To harmonize slices from different vertebral levels to a target slice.

Main Methods:

  • Extended conditional generative models to create C-SliceGen.
  • Model takes an arbitrary abdominal slice as input and generates a defined vertebral level slice.
  • Estimates structural changes in the latent space to account for positional variance.

Main Results:

  • C-SliceGen generates high-quality, realistic, and similar images.
  • Validated on in-house (1170 subjects) and BTCV MICCAI (50 subjects) datasets.
  • Harmonized muscle and visceral fat area in BLSA dataset (20 subjects) longitudinal slices.

Conclusions:

  • C-SliceGen effectively reduces positional variance in longitudinal abdominal CT slices.
  • Provides a promising approach for harmonizing slices for single-slice longitudinal analysis.
  • Facilitates more accurate body composition tracking in aging studies.