Merged Multi-Organ Abdominal CT Segmentation Dataset

<p><strong>Version 2.0</strong></p> <p>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.<br></p> <p>&nbsp;</p> <p><strong>Description</strong></p> <p>The abdominal CT images and reference segmentations were drawn from three datasets: the&nbsp;<a href="https://doi.org/10.7303/syn3193805" target="_blank" rel="noopener">Beyond the Cranial Vault (BTCV) Abdomen dataset</a> [1], the <a href="https://amos22.grand-challenge.org/" target="_blank" rel="noopener">Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022 dataset</a> [2], and the <a href="../records/10047292">TotalSegmentator dataset</a> [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:</p> <ul> <li>Spleen</li> <li>Right Kidney</li> <li>Left Kidney</li> <li>Gallbladder</li> <li>Esophagus</li> <li>Liver</li> <li>Stomach</li> <li>Aorta</li> <li>Inferior Vena Cava</li> <li>Pancreas</li> <li>Right Adrenal Gland</li> <li>Left Adrenal Gland</li> </ul> <p>The original work for which this dataset was created can be found in <a href="https://github.com/nicoalpis/Robust-Abdominal-Segmentation" target="_blank" rel="noopener">this GitHub repository</a>.&nbsp;</p> <p>&nbsp;</p> <p><strong>Terms of use</strong></p> <p>The terms of use of this data set include the terms of use of&nbsp;the&nbsp;<a href="https://doi.org/10.7303/syn3193805">Beyond the Cranial Vault (BTCV) Abdomen dataset</a> (<a href="https://doi.org/10.7303/syn3193805">terms of use</a>; after <a href="https://www.synapse.org/#!Synapse:syn3193805/wiki/217753">registration</a>, you can <a href="https://www.synapse.org/#!Synapse:syn3376386">access the data</a>), <a href="https://amos22.grand-challenge.org/" target="_blank" rel="noopener">Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022 dataset</a> (<a href="https://doi.org/10.5281/zenodo.7155725" target="_blank" rel="noopener">terms of use and data access</a>), and <a href="../records/10047292">TotalSegmentator dataset</a> (<a href="../records/10047292">terms of use and data access</a>). If you use these reference segmentations, please cite the references below.</p> <p><strong>References</strong></p> <p>[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.</p> <p>[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.</p> <p>[3] Wasserthal, J., Breit, H. C., Meyer, M. T., Pradella, M., Hinck, D., Sauter, A. W., ... &amp; Segeroth, M. (2023). Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images. Radiology: Artificial Intelligence, 5(5).</p>