Software architecture and manual for novel versatile CT image analysis toolbox -- AnatomyArchive

TL;DR

AnatomyArchive is a comprehensive, open-source CT image analysis toolbox that automates segmentation, body volume analysis, and radiomic feature extraction, supporting advanced medical imaging research and machine learning development.

Contribution

It introduces a novel, integrated software platform built on TotalSegmentator, combining segmentation, anatomy management, and visualization tools for CT image analysis.

Findings

Automates target volume selection and deselection based on user-configured anatomies.

Enables automatic body volume cropping and arm exclusion for precise analysis.

Provides GPU-accelerated segmentation and composite rendering for enhanced visualization.

Abstract

We have developed a novel CT image analysis package named AnatomyArchive, built on top of the recent full body segmentation model TotalSegmentator. It provides automatic target volume selection and deselection capabilities according to user-configured anatomies for volumetric upper- and lower-bounds. It has a knowledge graph-based and time efficient tool for anatomy segmentation mask management and medical image database maintenance. AnatomyArchive enables automatic body volume cropping, as well as automatic arm-detection and exclusion, for more precise body composition analysis in both 2D and 3D formats. It provides robust voxel-based radiomic feature extraction, feature visualization, and an integrated toolchain for statistical tests and analysis. A python-based GPU-accelerated nearly photo-realistic segmentation-integrated composite cinematic rendering is also included. We present here its software architecture design, illustrate its workflow and working principle of algorithms as well provide a few examples on how the software can be used to assist development of modern machine learning models. Open-source codes will be released at https://github.com/lxu-medai/AnatomyArchive for only research and educational purposes.