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  • 标题:A pediatric wrist trauma X-ray dataset (GRAZPEDWRI-DX) for machine learning
  • 本地全文:下载
  • 作者:Eszter Nagy ; Michael Janisch ; Franko Hržić
  • 期刊名称:Scientific Data
  • 电子版ISSN:2052-4463
  • 出版年度:2022
  • 卷号:9
  • 期号:1
  • 页码:1-10
  • DOI:10.1038/s41597-022-01328-z
  • 语种:English
  • 出版社:Nature Publishing Group
  • 摘要:Digital radiography is widely available and the standard modality in trauma imaging, often enabling to diagnose pediatric wrist fractures. However, image interpretation requires time-consuming specialized training. Due to astonishing progress in computer vision algorithms, automated fracture detection has become a topic of research interest. this paper presents the GRaZPEDWRI-DX dataset containing annotated pediatric trauma wrist radiographs of 6,091 patients, treated at the Department for Pediatric Surgery of the University Hospital Graz between 2008 and 2018. A total number of 10,643 studies (20,327 images) are made available, typically covering posteroanterior and lateral projections . The dataset is annotated with 74,459 image tags and features 67,771 labeled objects . We de-identifed all radiographs and converted the DICOM pixel data to 16-Bit grayscale PNG images . The flenames and the accompanying text fles provide basic patient information (age, sex) . Several pediatric radiologists annotated dataset images by placing lines, bounding boxes, or polygons to mark pathologies like fractures or periosteal reactions. they also tagged general image characteristics. this dataset is publicly available to encourage computer vision research.
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