UniToBrain Dataset

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/.<br> 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:%2F%2Frepo.metadatacenter.org%2Ftemplates%2Fe30d8369-6c31-45fa-a10a-2122283a28f2.都灵大学（University of Turin，简称UniTO）为DeepHealth项目中的同名用例3（Use Case 3）采集并发布了开源数据集UniTOBrain，项目官方网址为https://deephealth-project.eu/。 UniToBrain是计算机断层扫描（Computed Tomography，CT）灌注成像（CTP）数据集。该数据集包含100名训练受试者与15名测试受试者，用于一篇已投稿论文中的卷积神经网络（Convolutional Neural Network，CNN）模型的训练与测试，详细研究信息可参见：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。都灵大学团队将该数据集公开上线，它是包含258名受试者的完整数据集的子样本，完整数据集即将通过https://ieee-dataport.org/开放下载。 该258名连续患者的CTP数据均回顾性取自都灵健康与科学之城医院（Molinette医院）的医学影像存档与通信系统（Picture Archiving and Communication Systems，PACS）。CTP采集参数如下：采用GE品牌扫描仪，64层螺旋配置，管电压80kV，管电流150mAs，扫描时长44.5秒，共89帧时序影像（轴向覆盖范围40mm），以4ml/s的流速注射40ml浓度为300mg/ml的碘对比剂。 本数据集附带若干实用工具脚本： dicomtonpy.py：用于将数据集中的DICOM文件转换为NumPy数组，此类数组为三维数组，将同一层面的CT断层影像按时间采集顺序堆叠而成。 dataloader_pytorch.py：适用于PyTorch深度学习框架的数据加载器，可将NumPy数组转换为归一化张量，可作为标准深度学习模型的输入。 dataloader_pyeddl.py：适用于pyeddl深度学习框架的数据加载器，可将NumPy数组转换为归一化张量，可作为基于欧洲开源库EDDL的标准深度学习模型的输入。 访问https://github.com/EIDOSlab/UC3-UNITOBrain可获取完整配套代码，其中包含在该数据集上训练U-Net模型的实现。关于UniToBrain数据集的机器可读格式数据与元数据，请参见https://openview.metadatacenter.org/templates/https:%2F%2Frepo.metadatacenter.org%2Ftemplates%2Fe30d8369-6c31-45fa-a10a-2122283a28f2。