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

Journal: Scientific data
Published Date: Jul 29, 2025

Abstract

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.

Authors

  • Gowtham Krishnan Murugesan
    Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA.
  • Diana McCrumb
    BAMF Health, Grand Rapids, MI, USA.
  • Rahul Soni
    Department of Urology and Renal Transplant, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, 226014, India.
  • Jithendra Kumar
    BAMF Health, Grand Rapids, MI, USA.
  • Leonard Nuernberg
    Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
  • Linmin Pei
  • Ulrike Wagner
    From the Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 399 Revolution Dr, Somerville, MA 02145 (A.F., D.K., V.K.T., C.C., R.K.); Institute for Systems Biology, Seattle, Wash (W.J.R.L., D.L.G.); General Dynamics Information Technology, Rockville, Md (D.P.); PixelMed Publishing, Bangor, Pa (D.A.C.); Isomics, Cambridge, Mass (S.D.P.); Departments of Radiology (C.B.) and Pathology (M.D.H.), Massachusetts General Hospital and Harvard Medical School, Boston, Mass; Fraunhofer MEVIS, Bremen, Germany (A.H., D.P.S.); Radical Imaging, Boston, Mass (R.L.); Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Boston, Mass (H.J.W.L.A., D.B.); Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A., D.B.); Frederick National Laboratory for Cancer Research, Rockville, Md (T.P., U.W.); and National Cancer Institute, Bethesda, Md (K.F., E.K.).
  • Sutton Granger
    National Institute of Health, Bethesda, MD, USA.
  • Andrey Y Fedorov
    Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  • Stephen Moore
    Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada.
  • Jeff Van Oss
    BAMF Health, Grand Rapids, MI, USA.

Keywords

External Resources

Popular Topics
Recent Journals