ImagingDataCommons/nnU-Net-BPR-annotations
Code accompanying this dataset: Krishnaswamy, D., Bontempi, D., Clunie, D., Aerts, H. & Fedorov, A. AI-derived annotations for the NLST and NSCLC-Radiomics computed tomography imaging collections. (2022). doi:10.5281/zenodo.7473970
This project provides AI-generated annotations and radiomics features for lung cancer CT scans from the NSCLC-Radiomics and NLST collections. It takes existing, unlabeled or partially labeled CT images and outputs detailed segmentations of organs like the heart, trachea, aorta, and esophagus, along with 3D shape features. Medical researchers, oncologists, and radiologists can use this data to study lung cancer and radiation therapy planning.
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Use this if you need enriched, AI-derived anatomical annotations and quantitative radiomics features from lung CT scans for research or analysis.
Not ideal if you require solely expert-verified, manually segmented annotations without AI assistance or are working with different imaging modalities.
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BSD-3-Clause
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Jan 22, 2024
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