Apte Aditya P, LoCastro Eve, Iyer Aditi, Elguindi Sharif, Jiang Jue, Oh Jung Hun, Veeraraghavan Harini, Shukla-Dave Amita, Deasy Joseph O
bioRxiv. 2025 Jan 24:2025.01.19.633756. doi: 10.1101/2025.01.19.633756.
This work introduces a user-friendly, cloud-based software framework for conducting Artificial Intelligence (AI) analyses of medical images. The framework allows users to deploy AI-based workflows by customizing software and hardware dependencies. The components of our software framework include the Python-native Computational Environment for Radiological Research (pyCERR) platform for radiological image processing, Cancer Genomics Cloud (CGC) for accessing hardware resources and user management utilities for accessing images from data repositories and installing AI models and their dependencies. GNU-GPL copyright pyCERR was ported to Python from MATLAB-based CERR to enable researchers to organize, access, and transform metadata from high dimensional, multi-modal datasets to build cloud-compatible workflows for AI modeling in radiation therapy and medical image analysis. pyCERR provides an extensible data structure to accommodate metadata from commonly used medical imaging file formats and a viewer to allow for multi-modal visualization. Analysis modules are provided to facilitate cloud-compatible AI-based workflows for image segmentation, radiomics, DCE MRI analysis, radiotherapy dose-volume histogram-based features, and normal tissue complication and tumor control models for radiotherapy. Image processing utilities are provided to help train and infer convolutional neural network-based models for image segmentation, registration and transformation. The framework allows for round-trip analysis of imaging data, enabling users to apply AI models to their images on CGC and retrieve and review results on their local machine without requiring local installation of specialized software or GPU hardware. The deployed AI models can be accessed using APIs provided by CGC, enabling their use in a variety of programming languages. In summary, the presented framework facilitates end-to-end radiological image analysis and reproducible research, including pulling data from sources, training or inferring from an AI model, utilities for data management, visualization, and simplified access to image metadata.
这项工作介绍了一个用户友好的、基于云的软件框架，用于对医学图像进行人工智能（AI）分析。该框架允许用户通过定制软件和硬件依赖项来部署基于AI的工作流程。我们软件框架的组件包括用于放射图像的Python原生放射学研究计算环境（pyCERR）平台、用于访问硬件资源的癌症基因组学云（CGC）以及用于从数据存储库访问图像并安装AI模型及其依赖项的用户管理实用程序。GNU - GPL版权所有的pyCERR从基于MATLAB的CERR移植到Python，使研究人员能够组织、访问和转换来自高维、多模态数据集的元数据，以构建用于放射治疗和医学图像分析中AI建模的云兼容工作流程。pyCERR提供了一个可扩展的数据结构来容纳常用医学成像文件格式的元数据，以及一个用于多模态可视化的查看器。提供了分析模块，以促进用于图像分割、放射组学、动态对比增强磁共振成像（DCE MRI）分析、基于放射治疗剂量体积直方图的特征以及放射治疗的正常组织并发症和肿瘤控制模型的云兼容基于AI的工作流程。提供了图像处理实用程序，以帮助训练和推断基于卷积神经网络的图像分割、配准和变换模型。该框架允许对成像数据进行往返分析，使用户能够在CGC上对其图像应用AI模型，并在本地机器上检索和查看结果，而无需在本地安装专门的软件或GPU硬件。可以使用CGC提供API来访问部署的AI模型，从而使其能够在各种编程语言中使用。总之，所提出的框架促进了端到端的放射图像分析和可重复研究，包括从数据源提取数据、从AI模型进行训练或推断、数据管理实用程序、可视化以及简化对图像元数据的访问。
