Artificial intelligence apps for medical image analysis using pyCERR and cancer genomics cloud

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2026-10-21 DOI:10.1002/mp.70701

Aditya P. Apte, Eve LoCastro, Aditi Iyer, Sharif Elguindi, Jue Jiang, Maria Thor, Jung Hun Oh, Harini Veeraraghavan, Amita Shukla-Dave, Joseph O. Deasy

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引用次数: 0

Abstract

Purpose

To provide a software resource that enables medical researchers and practitioners to develop and apply reproducible, out-of-the-box, AI-based analysis workflows for radiological and radiotherapy data.

Development and validation methods

The framework comprises two main components: the Python-native computational environment for radiological research (pyCERR) for radiological image processing, and the cancer genomics cloud (CGC), a National Cancer Institute–funded resource that supports cloud-hosted collaborative projects, with tools for managing collaborators and compute-hardware from providers such as Amazon Web Services and Google Cloud Platform. pyCERR is open-source (GNU GPL) and distributed at https://github.com/cerr/pycerr; it provides user-friendly data structures, simplified access to metadata across radiological imaging modalities, and tools for data transformation, AI-based image segmentation, radiomics, dynamic contrast-enhanced MRI analysis, radiotherapy dose–volume histogram (DVH) feature extraction, and normal-tissue complication and tumor-control models. Radiomics features comply with the Image biomarker standardization initiative (IBSI) standards and are validated against published reference values. Unit tests covering data input/output, radiomics, DVH features, and visualization run on every code update. Workflows are containerized for reproducible, identical execution on the CGC and on local hardware. The CGC enables AI inference from radiological images to be combined with other data types such as genomics and proteomics. Our public CGC project (https://cgc.sbgenomics.com/u/sevenbridges/pycerr-radiological-radiotherapy-imageanalysis), accessible with free registration, currently includes apps for organ-at-risk (OAR) and lung-nodule segmentation based on published models trained in-house and clinically validated. These apps can be run interactively from a web browser or programmatically via a REST API with client support for multiple programming languages. Notebooks demonstrating the CGC API and pyCERR capabilities are provided at https://github.com/cerr/pycerr-notebooks.

Potential applications

The framework facilitates end-to-end radiological image analysis and reproducible research, including data access from public and private repositories, training and inference with AI models, and utilities for data management, visualization, and simplified access to image metadata.

查看原文本刊更多论文

使用pyCERR和癌症基因组云进行医学图像分析的人工智能应用程序

目的提供一种软件资源，使医学研究人员和从业人员能够开发和应用可重复的、开箱即用的、基于人工智能的放射和放疗数据分析工作流程。该框架包括两个主要组成部分：用于放射图像处理的python原生放射研究计算环境（pyCERR）和癌症基因组云（CGC），这是国家癌症研究所资助的资源，支持云托管的协作项目，具有管理合作者和来自亚马逊网络服务和谷歌云平台等提供商的计算硬件的工具。pyCERR是开源的（GNU GPL），发布于https://github.com/cerr/pycerr；它提供了用户友好的数据结构，简化了对放射成像模式元数据的访问，以及用于数据转换、基于人工智能的图像分割、放射组学、动态对比增强MRI分析、放疗剂量-体积直方图（DVH）特征提取、正常组织并发症和肿瘤控制模型的工具。放射组学特征符合图像生物标志物标准化倡议（IBSI）标准，并根据公布的参考值进行验证。覆盖数据输入/输出、放射组学、DVH特性和可视化的单元测试会在每次代码更新时运行。工作流被容器化，以便在CGC和本地硬件上进行可重复的、相同的执行。CGC使人工智能能够从放射图像中进行推断，并与基因组学和蛋白质组学等其他数据类型相结合。我们的公共CGC项目（https://cgc.sbgenomics.com/u/sevenbridges/pycerr-radiological-radiotherapy-imageanalysis）可以免费注册，目前包括基于内部训练和临床验证的已发表模型的危险器官（OAR）和肺结节分割应用程序。这些应用程序可以通过web浏览器以交互方式运行，也可以通过支持多种编程语言的REST API以编程方式运行。演示CGC API和pyCERR功能的笔记本在https://github.com/cerr/pycerr-notebooks上提供。该框架促进了端到端的放射图像分析和可重复研究，包括来自公共和私人存储库的数据访问、人工智能模型的训练和推理，以及用于数据管理、可视化和简化图像元数据访问的实用程序。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.