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). The UniTO team released this dataset publicly. 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. Along with the dataset, we provide some utility files. dicomtonpy.py: It converts the dicom files in the dataset to numpy arrays. These are 3D arrays, where CT slices at the same height are piled-up over the temporal acquisition. dataloader_pytorch.py: Dataloader for the pytorch deep learning framework. It converts the numpy arrays in normalized tensors, which can be provided as input to standard deep learning models. dataloader_pyeddl.py: Dataloader for the pyeddl deep learning framework. It converts the numpy arrays in normalized tensors, which can be provided as input to standard deep learning models using the european library EDDL. Visit https://github.com/EIDOSlab/UC3-UNITOBrain to have a full companion code where a U-Net model is trained over the dataset. As for UniToBrain Data and Metadata in machine-readable format see https://openview.metadatacenter.org/templates/https://repo.metadatacenter.org/templates/e30d8369-6c31-45fa-a10a-2122283a28f2.

UniToBrain Dataset

Umberto Gava
First
;
Federico D'Agata
;
Enzo Tartaglione;Daniele Perlo;Annamaria Vernone;Francesca Bertolino;Eleonora Ficiarà;Alessandro Cicerale;Fabrizio Pizzagalli;Caterina Guiot;Marco Grangetto;Mauro Bergui
Last
2021

Abstract

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). The UniTO team released this dataset publicly. 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. Along with the dataset, we provide some utility files. dicomtonpy.py: It converts the dicom files in the dataset to numpy arrays. These are 3D arrays, where CT slices at the same height are piled-up over the temporal acquisition. dataloader_pytorch.py: Dataloader for the pytorch deep learning framework. It converts the numpy arrays in normalized tensors, which can be provided as input to standard deep learning models. dataloader_pyeddl.py: Dataloader for the pyeddl deep learning framework. It converts the numpy arrays in normalized tensors, which can be provided as input to standard deep learning models using the european library EDDL. Visit https://github.com/EIDOSlab/UC3-UNITOBrain to have a full companion code where a U-Net model is trained over the dataset. As for UniToBrain Data and Metadata in machine-readable format see https://openview.metadatacenter.org/templates/https://repo.metadatacenter.org/templates/e30d8369-6c31-45fa-a10a-2122283a28f2.
ZENODO
https://zenodo.org/record/5109415
Stoke, Open Science, Open Data, Deep Learning, Image Processing, Medical Imaging, Machine Learning, Artificial Intelligence, Brain Perfusion
Umberto Gava; Federico D'Agata; Edwin Bennink; Enzo Tartaglione; Daniele Perlo; Annamaria Vernone; Francesca Bertolino; Eleonora Ficiarà; Alessandro Cicerale; Fabrizio Pizzagalli; Caterina Guiot; Marco Grangetto; Mauro Bergui
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1794859
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