资源介绍
# Introduction The DeepLesion dataset contains 32,120 axial computed tomography (CT) slices from 10,594 CT scans (studies) of 4,427 unique patients. There are 1–3 lesions in each image with accompanying bounding boxes and size measurements, adding up to 32,735 lesions altogether. The full data in its 220GB glory along with substantially more documentation and some test code to explore with are all available from the official site at https://nihcc.app.box.com/v/DeepLesion/ # Data The lesion annotations were mined from NIH’s picture archiving and communication system (PACS). Some meta-data are also provided. The contents include: - Folder “Images_png”: png image files. We named each slice with the format “{patient index}_{study index}_{series index}_{slice index}.png”, with the last underscore being / or \ to indicate sub-folders. The images are stored in unsigned 16 bit. One should subtract 32768 from the pixel intensity to obtain the original Hounsfield unit (HU) values. We provide not only the key CT slice that contains the lesion annotation, but also its 3D context (30mm extra slices above and below the key slice). Due to the large size of the data (221GB), this dataset only includes 8GB - DL_info.csv: The annotations and meta-data. See Section “Annotations” below. # Acknowledgements / Reference If you find the dataset useful for your research projects, please cite our JMI 2018 paper: - Ke Yan, Xiaosong Wang, Le Lu, Ronald M. Summers, "DeepLesion: Automated Mining of Large-Scale Lesion Annotations and Universal Lesion Detection with Deep Learning
