Software architecture and manual for novel versatile CT image analysis toolbox -- AnatomyArchive
This is an incremental tool for medical imaging researchers to streamline CT analysis and support machine learning model development.
The researchers developed AnatomyArchive, a CT image analysis toolbox that automates target volume selection, body composition analysis, and radiomic feature extraction, built on the TotalSegmentator model. It includes tools for segmentation mask management, cinematic rendering, and statistical analysis, with open-source code available for research.
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.