DeepLesion is a large-scale and diverse database of lesions in CT images. It was collected based on the bookmarks in the picture archiving and communication system of NIH. Currently, it contains 32,735 lesions from 32,120 axial CT slices of 4,427 patients. It can be used in studies of multi-class lesion detection, retrieval, segmentation, etc.
DeepLesion has been released. Download it without any paperwork here: https://nihcc.app.box.com/v/DeepLesion.
Please refer to our paper:
- DeepLesion: Automated Mining of Large-Scale Lesion Annotations and Universal Lesion Detection with Deep Learning (paper) Ke Yan, Xiaosong Wang, Le Lu, Ronald M. Summers, Journal of Medical Imaging, 2018.7.
- Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database (paper, supplementary) Ke Yan, Xiaosong Wang, Le Lu, Ling Zhang, Adam P. Harrison, Mohammadhadi Bagheri, and Ronald M. Summers, IEEE CVPR, 2018.6.
Other papers related with DeepLesion:
- Lesion detection
- Lesion segmentation
- Jinzheng Cai*, Youbao Tang*, Le Lu, Adam P. Harrison, Ke Yan, Jing Xiao, Lin Yang, Ronald M. Summers, "Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST," MICCAI, 2018
- Lesion measurement
- Youbao Tang, Adam P. Harrison, Mohammadhadi Bagheri, Jing Xiao, Ronald M. Summers , "Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks," MICCAI, 2018
For those who are not familiar with medical image analysis, I recommend you to take a look at our lesion detection code for basic techniques such as loading annotations and 3D data, intensity windowing of 16-bit png files, pixel spacing normalization, etc.