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<p>The VerSe2020 dataset will include 300 multidetector computed tomography (MDCT) image series of the spine. Please read the readme.txt and licence.txt.</p> <p><strong>By downloading the data you must cite these papers:</strong></p> <ol> <li>Löffler M, Sekuboyina A, Jakob A, Grau AL, Scharr A, Husseini ME, Herbell M, Zimmer C, Baum T, Kirschke JS., A Vertebral Segmentation Dataset with Fracture Grading. Radiology: Artificial Intelligence, 2020 (In Press). </li> <li>Sekuboyina A. et al., Labelling Vertebrae with 2D Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy. Radiology: Artificial Intelligence, 2020. </li> <li>Sekuboyina A, Bayat AH, Husseini ME, Löffler M, Menze BM, ..., Kirschke JS. VerSe: A Vertebrae Labelling and Segmentation Benchmark. <a href="https://arxiv.org/abs/2001.09193" rel="nofollow">https://arxiv.org/abs/2001.09193</a></li> </ol> <p>Please respect our work, as we spent - next to more than two years of work for algorithmic development - more than 1000 hours for manually correcting the segmentations, in addition to the 2000h for verse19. </p> <p>The work has been supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 637164 — iBack — ERC-2014-STG) </p> <p>The scans are stored as NIFTI files and separated equally in test and training data; training data includes vertebra-level segmentations (NIFTI format) and labels (JSON format).</p> <p>The dataset has been prepared for the MICCAI 2020 challenge "VerSe": <a href="https://verse2020.grand-challenge.org/" rel="nofollow">https://verse2020.grand-challenge.org/</a></p> <p>The data is published under the licence CC BY-SA 4.0 (see licence.txt). When using the data you must cite the three papers mentioned above.</p> <p>Ethical approval to publish this data has been obtained from the local ethics committee at TUM (Proposal 27/19 S-SR).</p>
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