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Longitudinal Variability Analysis on Low-dose Abdominal CT with Deep Learning-based Segmentation.

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Summary
This summary is machine-generated.

This study developed automated segmentation for low-dose computed tomography (CT) to assess body composition changes over time. Results show stable segmentation for analyzing longitudinal variability in metabolic health.

Keywords:
Body compositionCoefficient of variationIntraclass correlationLongitudinal variabilityLow dose single slice Computed Tomography

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Area of Science:

  • Medical Imaging
  • Radiology
  • Biomedical Engineering

Background:

  • Metabolic health is a key factor in various diseases, necessitating accurate body composition assessment.
  • Low-dose, single-slice computed tomography (CT) offers detailed tissue analysis but requires robust methods for longitudinal studies.
  • Automated segmentation techniques are crucial for quantifying changes in body composition from CT scans.

Purpose of the Study:

  • To develop and validate automated segmentation methods for analyzing longitudinal variability in body composition using low-dose, single-slice CT.
  • To assess the stability and reliability of segmentation for various abdominal tissues over time.
  • To identify factors influencing variability in longitudinal CT-based body composition analysis.

Main Methods:

  • Utilized supervised deep learning and unsupervised clustering for automated segmentation of 1816 abdominal CT slices from the Baltimore Longitudinal Study on Aging (BLSA) dataset.
  • Evaluated longitudinal variability in tissue/organ size and intensity using intraclass correlation coefficient (ICC) and coefficient of variation (CV) in 300 subjects with a two-year scan gap.
  • Calculated Dice similarity coefficients to measure segmentation accuracy for 13 abdominal tissue structures.

Main Results:

  • Achieved stable longitudinal segmentation performance with Dice scores ranging from 0.821 to 0.962 for 13 abdominal tissue structures.
  • Demonstrated low variability (average ICC ≥ 0.8) in muscle, abdominal wall, fat, and body mask, indicating reliability.
  • Observed high variability (ICC < 0.5) in most organs, strongly correlated with the 2D slice's cross-sectional position.

Conclusions:

  • The developed automated segmentation methods are stable and reliable for longitudinal analysis of low-dose, single-slice CT data.
  • Variability in organ measurements is significantly influenced by slice positioning, highlighting the need for careful quality control.
  • This work provides a foundation for quantitative exploration and reduced uncertainty in longitudinal body composition studies.