CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data

This is the train and testing dataset of Combined (CT-MR) Healthy Abdominal Organ Segmentation (CHAOS) Challenge. This data consists of images of Abdominal CT and MRI from different patients. There are 20 training and 20 testing cases in the CT dataset. MRI dataset contains 20 training and 20 testing cases with T1-Dual and T2 SPIR sequences. Train data contains both DICOM images and their ground truth masks. The testing set only contains DICOM images. In CT cases only livers were annotated. In MRI cases, livers, left/right kidneys, and spleens were annotated. For further information about the data and challenge, please visit https://chaos.grand-challenge.org/ and read the CHAOS_Submission_Manual.pdf <em><strong>Important note: </strong>Ground truths/references of the test data are reserved for challenge validation and <strong>will never be shared publicly. </strong>Such requests will be ignored<strong>.</strong></em> Scientists may use this data not only join to the CHAOS challenge but also for other works as long as they give appropriate credit, provide a link to the license, and indicate if changes were made. Bibtex: <pre><code>@dataset{CHAOSdata2019, author = {Ali Emre Kavur and M. Alper Selver and Oğuz Dicle and Mustafa Barış and N. Sinem Gezer}, title = {{CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data}}, month = Apr, year = 2019, publisher = {Zenodo}, version = {v1.03}, doi = {10.5281/zenodo.3362844}, url = {https://doi.org/10.5281/zenodo.3362844} }</code></pre> IEEE Style: <pre><code>Ali Emre Kavur, M. Alper Selver, Oğuz Dicle, Mustafa Barış, and N. Sinem Gezer, "CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data". Zenodo, 11-Apr-2019.</code></pre> APA Style: <pre><code>Ali Emre Kavur, M. Alper Selver, Oğuz Dicle, Mustafa Barış, &amp; N. Sinem Gezer. (2019). CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data (Version v1.03) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3362844</code></pre>