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Accurate quantification of bone, muscle, and their components is still an unmet need in the musculoskeletal field. Current methods to quantify tissue volumes in 3D images are expensive, labor-intensive, and time-consuming; thus, a reliable, valid, and quick application is highly needed. Tissue Compass is a standalone software for semiautomatic segmentation and automatic quantification of musculoskeletal organs. To validate the software, cross-sectional micro-CT scans images of rat femur (n = 19), and CT images of hip and abdomen (n = 100) from the Osteoporotic Fractures in Men (MrOS) Study were used to quantify bone, hematopoietic marrow (HBM), and marrow adipose tissue (MAT) using commercial manual software as a comparator. Also, abdominal CT scans (n = 100) were used to quantify psoas muscle volumes and intermuscular adipose tissue (IMAT) using the same software. We calculated Pearson's correlation coefficients, individual intra-class correlation coefficients (ICC), and Bland-Altman limits of agreement together with Bland-Altman plots to show the inter- and intra-observer agreement between Tissue Compass and commercially available software. In the animal study, the agreement between Tissue Compass and commercial software was r &gt; 0.93 and ICC &gt; 0.93 for rat femur measurements. Bland-Altman limits of agreement was - 720.89 (- 1.5e+04, 13,074.00) for MAT, 4421.11 (- 1.8e+04, 27,149.73) for HBM and - 6073.32 (- 2.9e+04, 16,388.37) for bone. The inter-observer agreement for QCT human study between two observers was r &gt; 0.99 and ICC &gt; 0.99. Bland-Altman limits of agreement was 0.01 (- 0.07, 0.10) for MAT in hip, 0.02 (- 0.08, 0.12) for HBM in hip, 0.05 (- 0.15, 0.25) for bone in hip, 0.02 (- 0.18, 0.22) for MAT in L1, 0.00 (- 0.16, 0.16) for HBM in L1, and 0.02 (- 0.23, 0.27) for bone in L1. The intra-observer agreement for QCT human study between the two applications was r &gt; 0.997 and ICC &gt; 0.99. Bland-Altman limits of agreement was 0.03 (- 0.13, 0.20) for MAT in hip, 0.05 (- 0.08, 0.18) for HBM in hip, 0.05 (- 0.24, 0.34) for bone in hip, - 0.02 (- 0.34, 0.31) for MAT in L1, - 0.14 (- 0.44, 0.17) for HBM in L1, - 0.29 (- 0.62, 0.05) for bone in L1, 0.03 (- 0.08, 0.15) for IMAT in psoas, and 0.02 (- 0.35, 0.38) for muscle in psoas. Compared to a conventional application, Tissue Compass demonstrated high accuracy and non-inferiority while also facilitating easier analyses. Tissue Compass could become the tool of choice to diagnose tissue loss/gain syndromes in the future by requiring a small number of CT sections to detect tissue volumes and fat infiltration.

Validation of a Semiautomatic Image Analysis Software for the Quantification of Musculoskeletal Tissues