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Tutorial for using SliceOmatic to calculate thigh area and composition from computed tomography images from older
Richard A Dennis1,2, Douglas E Long3, Reid D Landes4
1Geriatric Research, Education and Clinical Center, Central Arkansas Veterans Healthcare System, North Little Rock, Arkansas, United States of America.
Plos One
|October 3, 2018
Summary
This study provides a guide for using SliceOmatic software to analyze thigh composition from CT scans. Resistance training increased muscle area, and accounting for skin and marrow was not necessary for accurate results.
Area of Science:
- Medical imaging analysis
- Body composition assessment
- Radiology and biomechanics
Background:
- Thigh tissue composition (muscle, fat, bone) is crucial for assessing health outcomes.
- SliceOmatic software is widely used for this analysis, but its application can be complex for new users.
- Standardized methods are needed for reproducible thigh composition analysis using CT scans.
Purpose of the Study:
- To develop a quick start guide for calculating thigh composition using SliceOmatic software.
- To clarify the methodology for segmenting thigh tissues in CT images.
- To evaluate the impact of tissue density overlap and the necessity of including skin and marrow in the analysis.
Main Methods:
- Computed tomography (CT) images of the thigh were acquired from 24 older adults before and after a 12-week resistance training program.
- SliceOmatic software was employed to segment images into seven density regions, covering a range of -190 to +2000 Hounsfield Units (HU).
- The analysis determined the relative contributions of different tissues to thigh area and assessed the effects of tissue density overlap.
Main Results:
- Normal fat (29.1 ± 7.4%) and muscle (48.9 ± 8.2%) were the largest contributors to thigh area.
- Resistance training significantly increased muscle area across various density ranges (P<0.05).
- Normal fat, very high-density muscle, and bone composition did not change significantly (P>0.05), and results were unaffected by accounting for skin and marrow.
Conclusions:
- SliceOmatic analysis of thigh composition is feasible and reproducible.
- Defining muscle within a broad range (-29 to +200 HU) is recommended, with flexibility to examine specific density sub-ranges.
- Accounting for skin and marrow is likely unnecessary for accurate thigh composition analysis using SliceOmatic.
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