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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2409.20342 |
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| _version_ | 1866917790792810496 |
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| author | Murugesan, Gowtham Krishnan McCrumb, Diana Soni, Rahul Kumar, Jithendra Nuernberg, Leonard Pei, Linmin Wagner, Ulrike Granger, Sutton Fedorov, Andrey Y. Moore, Stephen Van Oss, Jeff |
| author_facet | Murugesan, Gowtham Krishnan McCrumb, Diana Soni, Rahul Kumar, Jithendra Nuernberg, Leonard Pei, Linmin Wagner, Ulrike Granger, Sutton Fedorov, Andrey Y. Moore, Stephen Van Oss, Jeff |
| contents | AI in Medical Imaging project aims to enhance the National Cancer Institute's (NCI) Image Data Commons (IDC) by developing nnU-Net models and providing AI-assisted segmentations for cancer radiology images. We created high-quality, AI-annotated imaging datasets for 11 IDC collections. These datasets include images from various modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), covering the lungs, breast, brain, kidneys, prostate, and liver. The nnU-Net models were trained using open-source datasets. A portion of the AI-generated annotations was reviewed and corrected by radiologists. Both the AI and radiologist annotations were encoded in compliance with the the Digital Imaging and Communications in Medicine (DICOM) standard, ensuring seamless integration into the IDC collections. All models, images, and annotations are publicly accessible, facilitating further research and development in cancer imaging. This work supports the advancement of imaging tools and algorithms by providing comprehensive and accurate annotated datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20342 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons Murugesan, Gowtham Krishnan McCrumb, Diana Soni, Rahul Kumar, Jithendra Nuernberg, Leonard Pei, Linmin Wagner, Ulrike Granger, Sutton Fedorov, Andrey Y. Moore, Stephen Van Oss, Jeff Image and Video Processing Computer Vision and Pattern Recognition AI in Medical Imaging project aims to enhance the National Cancer Institute's (NCI) Image Data Commons (IDC) by developing nnU-Net models and providing AI-assisted segmentations for cancer radiology images. We created high-quality, AI-annotated imaging datasets for 11 IDC collections. These datasets include images from various modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), covering the lungs, breast, brain, kidneys, prostate, and liver. The nnU-Net models were trained using open-source datasets. A portion of the AI-generated annotations was reviewed and corrected by radiologists. Both the AI and radiologist annotations were encoded in compliance with the the Digital Imaging and Communications in Medicine (DICOM) standard, ensuring seamless integration into the IDC collections. All models, images, and annotations are publicly accessible, facilitating further research and development in cancer imaging. This work supports the advancement of imaging tools and algorithms by providing comprehensive and accurate annotated datasets. |
| title | AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.20342 |