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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2507.13901 |
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| _version_ | 1866912490671046656 |
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| author | Xu, Lei Brismar, Torkel B |
| author_facet | Xu, Lei Brismar, Torkel B |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13901 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Software architecture and manual for novel versatile CT image analysis toolbox -- AnatomyArchive Xu, Lei Brismar, Torkel B Image and Video Processing Computer Vision and Pattern Recognition 62H35, 68U10 I.4.10; I.4.7; J.3 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. |
| title | Software architecture and manual for novel versatile CT image analysis toolbox -- AnatomyArchive |
| topic | Image and Video Processing Computer Vision and Pattern Recognition 62H35, 68U10 I.4.10; I.4.7; J.3 |
| url | https://arxiv.org/abs/2507.13901 |