AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons
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 w...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05666-6 |
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| _version_ | 1849333763825205248 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Gowtham Krishnan Murugesan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2dec3d0c9d64422bb78a48e3439b3adb |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-2dec3d0c9d64422bb78a48e3439b3adb2025-08-20T03:45:45ZengNature PortfolioScientific Data2052-44632025-07-011211810.1038/s41597-025-05666-6AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data CommonsGowtham Krishnan Murugesan0Diana McCrumb1Rahul Soni2Jithendra Kumar3Leonard Nuernberg4Linmin Pei5Ulrike Wagner6Sutton Granger7Andrey Y. Fedorov8Stephen Moore9Jeff Van Oss10BAMF HealthBAMF HealthBAMF HealthBAMF HealthBrigham and Women’s Hospital and Harvard Medical SchoolFrederick National Laboratory for Cancer ResearchFrederick National Laboratory for Cancer ResearchNational Institute of HealthBrigham and Women’s Hospital and Harvard Medical SchoolBAMF HealthBAMF HealthAbstract 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.https://doi.org/10.1038/s41597-025-05666-6 |
| spellingShingle | 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 AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons Scientific Data |
| title | AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons |
| title_full | AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons |
| title_fullStr | AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons |
| title_full_unstemmed | AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons |
| title_short | AI generated annotations for Breast, Brain, Liver, Lungs, and Prostate cancer collections in the National Cancer Institute Imaging Data Commons |
| title_sort | ai generated annotations for breast brain liver lungs and prostate cancer collections in the national cancer institute imaging data commons |
| url | https://doi.org/10.1038/s41597-025-05666-6 |
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