Computed tomography (CT) is routinely used in clinical practice to evaluate a
wide variety of medical conditions. While CT scans provide diagnoses, they also
offer the ability to extract quantitative body composition metrics to analyze
tissue volume and quality. Extracting quantitative body composition measures
manually from CT scans is a cumbersome and time-consuming task. Proprietary
software has been developed recently to automate this process, but the
closed-source nature impedes widespread use. There is a growing need for fully
automated body composition software that is more accessible and easier to use,
especially for clinicians and researchers who are not experts in medical image
processing. To this end, we have built Comp2Comp, an open-source Python package
for rapid and automated body composition analysis of CT scans. This package
offers models, post-processing heuristics, body composition metrics, automated
batching, and polychromatic visualizations. Comp2Comp currently computes body
composition measures for bone, skeletal muscle, visceral adipose tissue, and
subcutaneous adipose tissue on CT scans of the abdomen. We have created two
pipelines for this purpose. The first pipeline computes vertebral measures, as
well as muscle and adipose tissue measures, at the T12 - L5 vertebral levels
from abdominal CT scans. The second pipeline computes muscle and adipose tissue
measures on user-specified 2D axial slices. In this guide, we discuss the
architecture of the Comp2Comp pipelines, provide usage instructions, and report
internal and external validation results to measure the quality of
segmentations and body composition measures. Comp2Comp can be found at
this https URL.

本文介绍了一个基于 Python 的开源软件 Comp2Comp，用于从 CT 扫描中快速、自动分析测量人体的体成分数据，目前计算了腹部 CT 扫描的骨骼肌、内脏脂肪组织和皮下脂肪组织的身体成分数据，并提供了两个流程算法来计算 T12 - L5 椎体水平和用户指定的 2D 轴向切片。