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). 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/.<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.都灵大学（University of Turin，缩写UniTO）发布了开放获取数据集UniTOBrain，该数据集是为DeepHealth项目中的同名用例3（Use Case 3）收集所得（项目官网：https://deephealth-project.eu/）。UniTOBrain是计算机断层扫描灌注成像（Computed Tomography Perfusion，CTP）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，详细信息参见：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）的训练与测试。当前该团队以私有形式发布此数据集，后续将正式公开上线。本数据集是包含258名受试者的更大规模数据集的子样本，完整数据集即将通过https://ieee-dataport.org/开放下载。 258名连续就诊患者的CTP数据回顾性采集自都灵健康与科学城医院（Città della Salute e della Scienza di Torino，又称Molinette医院）的医院影像归档和通信系统（Picture Archiving and Communication Systems，PACS）。CTP采集参数如下：采用GE品牌64层扫描仪，管电压80kV，管电流150mAs，扫描时长44.5秒，共采集89幅影像（轴向覆盖范围40mm），以4ml/s的速率注射40ml碘造影剂（浓度300mg/ml）。 随数据集一同提供了若干实用工具脚本： 1. `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，同一层面的CT影像切片将按时间采集顺序堆叠。 2. `dataloader_pytorch.py`：适配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，可直接作为标准深度学习模型的输入。 3. `dataloader_pyeddl.py`：适配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，可作为使用欧洲开源深度学习库EDDL的标准深度学习模型的输入。 可访问https://github.com/EIDOSlab/UC3-UNITOBrain获取完整配套代码，其中包含在该数据集上训练U-Net模型的完整流程。