UniToBrain Dataset
Creators
- 1. Neuroscience Department, University of Turin (Italy)
- 2. Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands, Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
- 3. Informatics Department, University of Turin, Turin (Italy)
Description
The University of Turin (UniTO) released the open-access dataset UniTOBrain collected for the homonymous Use Case 3 in the DeepHealth project (https://deephealth-project.eu/). UniToBrain is a dataset of Computed Tomography (CT) perfusion images (CTP). The dataset includes 100 training subjects and 15 testing subjects used in a submitted publication for the training and the testing of a Convolutional Neural Network (CNN, see for details: https://arxiv.org/abs/2101.05992, https://paperswithcode.com/paper/neural-network-derived-perfusion-maps-a-model, https://www.medrxiv.org/content/10.1101/2021.01.13.21249757v1). At this stage, the UniTO team released this dataset privately, but soon it will be public. This is a subsample of a greater dataset of 258 subjects that will be soon available for download at https://ieee-dataport.org/.
CTP data from 258 consecutive patients were retrospectively obtained from the hospital PACS of Città della Salute e della Scienza di Torino (Molinette). CTP acquisition parameters were as follows: Scanner GE, 64 slices, 80 kV, 150 mAs, 44.5 sec duration, 89 volumes (40 mm axial coverage), injection of 40 ml of Iodine contrast agent (300 mg/ml) at 4 ml/s speed.
Files
dati_ctp.csv
Files
(50.8 GB)
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Additional details
Related works
- Cites
- Preprint: https://arxiv.org/abs/2101.05992 (URL)
- Report: https://paperswithcode.com/paper/neural-network-derived-perfusion-maps-a-model (URL)
- Preprint: 10.1101/2021.01.13.21249757 (DOI)
Funding
References
- Bennink E, Oosterbroek J, Kudo K, Viergever MA, Velthuis BK, de Jong HWAM. Fast nonlinear regression method for CT brain perfusion analysis. Journal of Medical Imaging 2016. https://doi.org/10.1117/1.jmi.3.2.026003.
- Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW. Elastix: A toolbox for intensity-based medical image registration. IEEE Transactions on Medical Imaging 2010. https://doi.org/10.1109/TMI.2009.2035616.
- Tomasi C, Manduchi R. Bilateral filtering for gray and color images. Proceedings of the IEEE International Conference on Computer Vision, 1998. https://doi.org/10.1109/iccv.1998.710815.