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Abstract

Medicine

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

Published: March 21st, 2021

DOI:

10.3791/61674

1Department of Urology, Emory University School of Medicine, 2Northeast Ohio Medical University, 3Department of Urology, University of Alabama at Birmingham, 4Department of Medical Oncology, Winship Cancer Institute of Emory University, 5Department of Urology, University of Washington

Abstract

Body composition is associated with risk of disease progression and treatment complications in a variety of conditions. Therefore, quantification of skeletal muscle mass and adipose tissues on Computed Tomography (CT) and/or Magnetic Resonance Imaging (MRI) may inform surgery risk evaluation and disease prognosis. This article describes two quantification methods originally described by Mourtzakis et al. and Avrutin et al.: tissue segmentation and linear measurement of skeletal muscle. Patients' cross-sectional image at the midpoint of the third lumbar vertebra was obtained for both measurements. For segmentation, the images were imported into Slice-O-Matic and colored for skeletal muscle, intramuscular adipose tissue, visceral adipose tissue, and subcutaneous adipose tissue. Then, surface areas of each tissue type were calculated using the tag surface area function. For linear measurements, the height and width of bilateral psoas and paraspinal muscles at the level of the third lumbar vertebra are measured and the calculation using these four values yield the estimated skeletal muscle mass. Segmentation analysis provides quantitative, comprehensive information about the patients' body composition, which can then be correlated with disease progression. However, the process is more time-consuming and requires specialized training. Linear measurements are an efficient and clinic-friendly tool for quick preoperative evaluation. However, linear measurements do not provide information on adipose tissue composition. Nonetheless, these methods have wide applications in a variety of diseases to predict surgical outcomes, risk of disease progression and inform treatment options for patients.

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Keywords Segmentation

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