AnatomyArchive可自动分割人体CT图像并精准分析体成分，助力医学影像研究。
Software architecture and manual for novel versatile CT image analysis toolbox -- AnatomyArchive
- 基于TotalSegmentator构建，支持用户自定义解剖结构的自动选/剔
- 实现2D/3D体成分分析，自动裁剪身体并排除手臂干扰
- 集成放射组学特征提取与可视化，适合医学影像与机器学习研究者
我们开发了名为AnatomyArchive的新一代CT图像分析工具包，基于最新的全身体积分割模型TotalSegmentator。该工具提供根据用户配置的解剖结构自动选择和剔除目标体积的功能，支持上、下限体积分析。其采用基于知识图谱的高效解剖结构分割掩膜管理与医学图像数据库维护机制。AnatomyArchive可自动进行体部裁剪，以及自动检测并排除手臂区域，提升2D与3D体成分分析精度。支持基于体素的放射组学特征提取、特征可视化及统计分析工具链集成。此外，还提供基于Python的GPU加速近似逼真度分割融合的复合影像渲染功能。本文介绍了其软件架构设计，演示了算法工作流程与应用实例，展示其在辅助现代机器学习模型开发中的潜力。开源代码将仅用于科研与教育目的，发布于https://github.com/lxu-medai/AnatomyArchive。
原文摘要 · Abstract (English)
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.
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