Version 2.0 This version maintains the same dataset as Version 1.0, the only addition is the CSV metadata file. Please note that the BTCV images do not include metadata information. Description The abdominal CT images and reference segmentations were drawn from three datasets: the Beyond the Cranial Vault (BTCV) Abdomen dataset [1], the Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022 dataset [2], and the TotalSegmentator dataset [3]. Given the class differences among the three datasets, their consolidation requires the elimination of several classes, resulting in a unified dataset of 680 CT images comprising 12 classes common to all three, including: Spleen Right Kidney Left Kidney Gallbladder Esophagus Liver Stomach Aorta Inferior Vena Cava Pancreas Right Adrenal Gland Left Adrenal Gland The original work for which this dataset was created can be found in this GitHub repository. Terms of use The terms of use of this data set include the terms of use of the Beyond the Cranial Vault (BTCV) Abdomen dataset (terms of use; after registration, you can access the data), Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022 dataset (terms of use and data access), and TotalSegmentator dataset (terms of use and data access). If you use these reference segmentations, please cite the references below. References [1] Landman BA, Xu Z, Igelsias JE, Styner M, Langerak TR, and Klein A, "MICCAI multi-atlas labeling beyond the cranial vault - workshop and challenge," 2015, https://doi.org/10.7303/syn3193805. [2] Ji, Yuanfeng, et al. "Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation." Advances in Neural Information Processing Systems 35 (2022): 36722-36732. [3] Wasserthal, J., Breit, H. C., Meyer, M. T., Pradella, M., Hinck, D., Sauter, A. W., ... & Segeroth, M. (2023). Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images. Radiology: Artificial Intelligence, 5(5).