UniTOBrain

The University of Turin (UniTO) released the open-access dataset Stoke 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 258 consecutive patients, a subsample of 100 training subjects and 15 testing subjects was 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.CTP data 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（UniTO）发布了为DeepHealth项目（https://deephealth-project.eu/）中同名的用例3所收集的开放访问数据集Stoke。UniToBrain是一个包含计算机断层扫描（CT）灌注图像（CTP）的数据集。该数据集包含258名连续患者，其中100名训练样本和15名测试样本被用于提交的出版物中，用于卷积神经网络（CNN）的训练和测试（详见：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）。UniTO团队公开发布了此数据集。CTP数据是从都灵Città della Salute e della Scienza医院的PACS系统中回顾性获取的。CTP的采集参数如下：GE扫描仪，64个切片，80 kV，150 mAs，持续时间44.5秒，89个体积（轴向覆盖40毫米），以4 ml/s的速度注射40 ml的碘对比剂（300 mg/ml）。除了数据集外，我们还提供了一些实用文件。
dicomtonpy.py：它将数据集中的dicom文件转换为numpy数组。这些是3D数组，其中在同一高度上的CT切片被堆叠在时间采集上。
dataloader_pytorch.py：Pytorch深度学习框架的加载数据器。它将numpy数组转换为归一化的张量，可以作为标准深度学习模型的输入。
dataloader_pyeddl.py：Pyeddl深度学习框架的加载数据器。它将numpy数组转换为归一化的张量，可以使用欧洲库EDDL将它们作为输入提供给标准深度学习模型。请访问https://github.com/EIDOSlab/UC3-UNITOBrain以获取一个完整的配套代码，其中在一个U-Net模型上使用此数据集进行训练。

搜集汇总

背景与挑战

背景概述

UniTOBrain是一个开放的CT灌注图像数据集，包含258名患者的CTP数据，用于支持深度学习在医学影像处理中的应用。数据集由都灵大学发布，并提供了配套的工具和代码，便于研究人员进行数据转换和模型训练。