Measuring Abdominal Circumference and Skeletal Muscle From a Single Cross-Sectional Computed Tomography Image: A Step-by-Step Guide for Clinicians Using National Institutes of Health ImageJ.
Bibliographic record
Abstract
Diagnostic computed tomography (CT) scans provide numerous opportunities for body composition analysis, including quantification of abdominal circumference, abdominal adipose tissues (subcutaneous, visceral, and intermuscular), and skeletal muscle (SM). CT scans are commonly performed for diagnostic purposes in clinical settings, and methods for estimating abdominal circumference and whole-body SM mass from them have been reported. A supine abdominal circumference is a valid measure of waist circumference (WC). The valid correlation between a single cross-sectional CT image (slice) at third lumbar (L3) for abdominal SM and whole-body SM is also well established. Sarcopenia refers to the age-associated decreased in muscle mass and function. A single dimensional definition of sarcopenia using CT images that includes only assessment of low whole-body SM has been validated in clinical populations and significantly associated with negative outcomes. However, despite the availability and precision of SM data from CT scans and the relationship between these measurements and clinical outcomes, they have not become a routine component of clinical nutrition assessment. Lack of time, training, and expense are potential barriers that prevent clinicians from fully embracing this technique. This tutorial presents a systematic, step-by-step guide to quickly quantify abdominal circumference as a proxy for WC and SM using a cross-sectional CT image from a regional diagnostic CT scan for clinical identification of sarcopenia. Multiple software options are available, but this tutorial uses ImageJ, a free public-domain software developed by the National Institutes of Health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.115 | 0.096 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".