BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients

BIMCV-COVID19+ dataset is a large dataset with chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19 patients along with their radiographic findings, pathologies, polymerase chain reaction (PCR), immunoglobulin G (IgG) and immunoglobulin M (IgM) diagnostic antibody tests and radiographic reports from Medical Imaging Databank in Valencian Region Medical Image Bank (BIMCV). The findings are mapped onto standard Unified Medical Language System (UMLS) terminology and they cover a wide spectrum of thoracic entities, contrasting with the much more reduced number of entities annotated in previous datasets. Images are stored in high resolution and entities are localized with anatomical labels in a Medical Imaging Data Structure (MIDS) format. In addition, 23 images were annotated by a team of expert radiologists to include semantic segmentation of radiographic findings. Moreover, extensive information is provided,including the patient’s demographic information, type of projection and acquisition parameters for the imaging study, among others. These iterations of the database include 7377 CR, 9463 DX and 6687 CT studies. This work is first and foremost an open and free contribution from the authors in the working group with support from the Regional Ministry of Innovation, Universities, Science and Digital Society grant awarded through decree 51/2020 by the Valencian Innovation Agency (Spain) and Regional Ministry of Health in Valencia Region. This research is also supported by the University of Alicante’s UACOVID-19-18 project. Part of the infrastructure used has been cofunded by the European Union through the Operational Program of the European Fund of Regional Development (FEDER) of the Valencian Community 2014-2020. The Medical Image Bank of the Valencian Community was partially funded by the European Union’s Horizon 2020 Framework Programme under grant agreement 688945 (Euro-BioImaging PrepPhase II). This work is undertaken in the context of the DeepHealth project, “Deep-Learning and HPC to Boost Biomedical Applications for Health” (https://deephealth-project.eu/) which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825111”.

BIMCV-COVID19+ 数据集系一庞大集合，囊括了 COVID-19 患者的胸部 X 射线图像 CXR（CR, DX）及计算机断层扫描（CT）成像，并附有相应的放射学发现、病理学、聚合酶链反应（PCR）、免疫球蛋白 G（IgG）及免疫球蛋白 M（IgM）诊断抗体检测和放射学报告。这些发现均对应于标准的统一医学语言系统（UMLS）术语，并涵盖了广泛的胸部实体，与先前数据集中标注的实体数量形成鲜明对比。图像以高分辨率存储，并以解剖学标签在医学影像数据结构（MIDS）格式中定位实体。此外，由一组资深放射学家标注的 23 张图像，以包括放射学发现的语义分割。此外，数据集还提供了详尽的信息，包括患者的流行病学信息、成像研究的投影类型和采集参数等。该数据库的这些版本包括 7377 例 CR、9463 例 DX 和 6687 例 CT 研究。此项工作首先是作者工作组的开放和免费贡献，由瓦伦西亚自治区创新、大学、科学和数字社会部通过 2020 年第 51 号法令授予的瓦伦西亚创新局（西班牙）和瓦伦西亚自治区卫生部的创新、大学、科学和数字社会部资助支持。此研究还得到了阿利坎特大学的 UACOVID-19-18 项目支持。部分基础设施资金由欧盟通过瓦伦西亚社区 2014-2020 年区域发展基金（FEDER）的操作计划共同资助。瓦伦西亚社区医学影像库部分资金由欧盟地平线 2020 基金计划下的框架协议 688945（Euro-BioImaging PrepPhase II）提供。此项工作是在 DeepHealth 项目“深度学习和高性能计算以促进生物医学应用健康”（https://deephealth-project.eu/）的背景下进行的，该项目已获得欧盟地平线 2020 研究和创新计划的资金支持，资助协议编号为 No 825111。”
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