Summary
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Sparsely Annotated Region and Organ Segmentation (SAROS) contributes a large heterogeneous semantic segmentation annotation dataset for existing CT imaging cases on TCIA. The goal of this dataset is to provide high-quality annotations for building body composition analysis tools (see [Koitka 2020: https://doi.org/10.1007/s00330-020-07147-3]). Existing in-house segmentation models were employed to generate annotation candidates on randomly selected cases. All generated annotations were manually reviewed and corrected by medical residents and students on every fifth axial slice while other slices were set to an ignore label (numeric value 255). 900 CT series from 882 patients were randomly selected from the following TCIA collections (number of CTs per collection in parenthesis): ACRIN-FLT-Breast (32), ACRIN-HNSCC-FDG-PET/CT (48), ACRIN-NSCLC-FDG-PET (129), Anti-PD-1_Lung (12), Anti-PD-1_MELANOMA (2), C4KC-KiTS (175), COVID-19-NY-SBU (1), CPTAC-CM (1), CPTAC-LSCC (3), CPTAC-LUAD (1), CPTAC-PDA (8), CPTAC-UCEC (26), HNSCC (17), Head-Neck Cetuximab (12), LIDC-IDRI (133), Lung-PET-CT-Dx (17), NSCLC Radiogenomics (7), NSCLC-Radiomics (56), NSCLC-Radiomics-Genomics (20), Pancreas-CT (58), QIN-HEADNECK (94), Soft-tissue-Sarcoma (6), TCGA-HNSC (1), TCGA-LIHC (33), TCGA-LUAD (2), TCGA-LUSC (3), TCGA-STAD (2), TCGA-UCEC (1). A script to download and resample the images is provided in our GitHub repository: https://github.com/UMEssen/saros-dataset The annotations are provided in NIfTI format and were performed on 5mm slice thickness. The annotation files define foreground labels on the same axial slices and match pixel-perfect. In total, 13 semantic body regions and 6 body partpart labels were annotated (with an index that corresponds to a numeric value in the segmentation file ). Both annotation files define foreground labels on the same axial slices and match pixel-perfect.. Body Regions:
Body Parts:
The labels which were modified or require further commentary are listed and explained below:
For reproducibility on downstream tasks, five cross-validation folds and a test set were pre-defined and are described in the provided spreadsheet. Segmentation was conducted strictly in accordance with anatomical guidelines and only modified if required for the gain of segmentation efficiency. Benefit to Researchers: Researchers can build upon this data to develop fully automated body composition analysis pipelines or use this data for different purposes in medical image segmentation. |
Acknowledgements
To the entire annotation lab team at the Institute for Artificial Intelligence in Medicine (IKIM, University Hospital Essen), we express our profound gratitude for your meticulous efforts in data segmentation. Your dedication ensures accuracy and efficiency, paving the way for this collection. Thank you for your invaluable contribution.
To all collections that shared their data and made it possible that we could prepare the segmentations: thank you! Your contributions made it possible to provide an open available segmentation dataset for CT based body composition analysis.
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TCIA Collections All 900 sample cases, 450 female and 450 male, were randomly selected from the following TCIA collections (number of cases in parenthesis): ACRIN-FLT-Breast (32), ACRIN-HNSCC-FDG-PET-CT (48), ACRIN-NSCLC-FDG-PET (129
For reproducibility on downstream tasks, five cross-validation folds and a test set were pre-defined. Segmentation was conducted strictly in accordance with anatomical guidelines and only modified if required for the gain of segmentation efficiency. Benefit to Researchers: Researchers can build upon this data to develop fully automated body composition analysis pipelines or use this data for different purposes in medical image segmentation. |
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
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Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.
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Note to curators! The link below is an example for NCTN trials and will need to be tailored to the proper URL for the corresponding data on the NCTN Data Archive.
Note to curators! Below are examples for what to do with other external resources/links that don't fit into the above categories. The following external resources have been made available by the data submitters. These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.
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