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<p>The Artificial Intelligence in Medical Imaging (AIMI) initiative aims to enhance the National Cancer Institute’s (NCI) Image Data Commons (IDC) by releasing fully reproducible nnU-Net models, along with AI-assisted segmentation for cancer radiology images. In this extension of our earlier work, we created high-quality, AI-annotated imaging datasets for 11 IDC collections, spanning computed tomography (CT) and magnetic resonance imaging (MRI) of the lungs, breast, brain, kidneys, prostate, and liver. Each nnU-Net model was trained on open-source datasets, and a portion of the AI-generated annotations was reviewed and corrected by board-certified radiologists. Both the AI and radiologist annotations were encoded in compliance with the Digital Imaging and Communications in Medicine (DICOM) standard, ensuring seamless integration into the IDC collections. By making these models, images, and annotations publicly accessible, we aim to facilitate further research and development in cancer imaging.</p>

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AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月29日

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  • Gowtham Krishnan Murugesan,
  • Diana McCrumb,
  • Rahul Soni,
  • Jithendra Kumar,
  • Leonard Nuernberg,
  • Linmin Pei,
  • Ulrike Wagner,
  • Sutton Granger,
  • Andrey Y. Fedorov,
  • Stephen Moore,
  • Jeff Van Oss

摘要

The Artificial Intelligence in Medical Imaging (AIMI) initiative aims to enhance the National Cancer Institute’s (NCI) Image Data Commons (IDC) by releasing fully reproducible nnU-Net models, along with AI-assisted segmentation for cancer radiology images. In this extension of our earlier work, we created high-quality, AI-annotated imaging datasets for 11 IDC collections, spanning computed tomography (CT) and magnetic resonance imaging (MRI) of the lungs, breast, brain, kidneys, prostate, and liver. Each nnU-Net model was trained on open-source datasets, and a portion of the AI-generated annotations was reviewed and corrected by board-certified radiologists. Both the AI and radiologist annotations were encoded in compliance with the Digital Imaging and Communications in Medicine (DICOM) standard, ensuring seamless integration into the IDC collections. By making these models, images, and annotations publicly accessible, we aim to facilitate further research and development in cancer imaging.