Artificial intelligence (AI) is about to transform medical imaging. The Research Consortium for Medical Image Analysis (RECOMIA), a not-for-profit organisation, has developed an online platform to facilitate collaboration between medical researchers and AI researchers. The aim is to minimise the time and effort researchers need to spend on technical aspects, such as transfer, display, and annotation of images, as well as legal aspects, such as de-identification. The purpose of this article is to present the RECOMIA platform and its AI-based tools for organ segmentation in computed tomography (CT), which can be used for extraction of standardised uptake values from the corresponding positron emission tomography (PET) image. The RECOMIA platform includes modules for (1) local de-identification of medical images, (2) secure transfer of images to the cloud-based platform, (3) display functions available using a standard web browser, (4) tools for manual annotation of organs or pathology in the images, (5) deep learning-based tools for organ segmentation or other customised analyses, (6) tools for quantification of segmented volumes, and (7) an export function for the quantitative results. The AI-based tool for organ segmentation in CT currently handles 100 organs (77 bones and 23 soft tissue organs). The segmentation is based on two convolutional neural networks (CNNs): one network to handle organs with multiple similar instances, such as vertebrae and ribs, and one network for all other organs. The CNNs have been trained using CT studies from 339 patients. Experienced radiologists annotated organs in the CT studies. The performance of the segmentation tool, measured as mean Dice index on a manually annotated test set, with 10 representative organs, was 0.93 for all foreground voxels, and the mean Dice index over the organs were 0.86 (0.82 for the soft tissue organs and 0.90 for the bones). The paper presents a platform that provides deep learning-based tools that can perform basic organ segmentations in CT, which can then be used to automatically obtain the different measurement in the corresponding PET image. The RECOMIA platform is available on request at www.recomia.org for research purposes.

中文翻译：

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RECOMIA-基于云的核医学和放射学人工智能研究平台。

人工智能（AI）将改变医学成像。非营利组织医学图像分析研究联盟（RECOMIA）开发了一个在线平台，以促进医学研究人员和AI研究人员之间的合作。目的是最大程度地减少研究人员在技术方面（如图像的传输，显示和注释）以及法律方面（如取消标识）上花费的时间和精力。本文的目的是介绍用于计算机断层摄影（CT）中器官分割的RECOMIA平台及其基于AI的工具，该工具可用于从相应的正电子发射断层摄影（PET）图像中提取标准化摄取值。RECOMIA平台包括用于（1）对医学图像进行本地去识别的模块，（2）将图像安全传输到基于云的平台，（3）使用标准Web浏览器可用的显示功能，（4）用于手动注释图像中器官或病理的工具，（5）基于深度学习的工具器官分割或其他自定义分析；（6）定量分割体积的工具；（7）定量结果的导出功能。基于AI的CT器官分割工具目前可处理100个器官（77个骨骼和23个软组织器官）。分割基于两个卷积神经网络（CNN）：一个用于处理具有多个相似实例（例如椎骨和肋骨）的器官的网络，以及一个用于所有其他器官的网络。使用339位患者的CT研究训练了CNN。经验丰富的放射科医生在CT研究中注释了器官。分割工具的性能（在带有10个代表性器官的手动注释测试集上以平均Dice指数衡量）对于所有前景体素均为0.93，并且在器官上的平均Dice指数为0.86（软组织器官为0.82，0.90为骨头）。本文提出了一个平台，该平台提供了基于深度学习的工具，可以在CT中执行基本的器官分割，然后可以将其用于自动获得相应PET图像中的不同测量值。RECOMIA平台可应要求在www.recomia.org上用于研究目的。本文介绍了一个平台，该平台提供了基于深度学习的工具，可以在CT中执行基本的器官分割，然后可以将其用于自动获得相应PET图像中的不同测量值。RECOMIA平台可应要求在www.recomia.org上用于研究目的。本文介绍了一个平台，该平台提供了基于深度学习的工具，可以在CT中执行基本的器官分割，然后可以将其用于自动获得相应PET图像中的不同测量值。RECOMIA平台可应要求在www.recomia.org上用于研究目的。