The National Institutes of Health’s Clinical Center has made a large-scale dataset of CT images publicly available to help the scientific community improve detection accuracy of lesions. While most publicly available medical image datasets have less than a thousand lesions, this dataset, named DeepLesion, has over 32,000 annotated lesions identified on CT images.
The images, which have been thoroughly anonymized, represent 4,400 unique patients, who are partners in research at the NIH.
Once a patient steps out of a CT scanner, the corresponding images are sent to a radiologist to interpret. Radiologists at the Clinical Center then measure and mark clinically meaningful findings with an electronic bookmark tool. Similar to a physical bookmark, radiologists save their place and mark significant findings to be able to come back to at a later time. These bookmarks are complex – they provide arrows, lines, diameters, and text that can tell the exact location and size of a lesion so experts can identify growth or new disease.
The bookmarks, abundant with retrospective medical data, are what scientists used to develop the DeepLesion dataset. DeepLesion is unlike most lesion medical image datasets currently available, which can only detect one type of lesion. The database has great diversity – it contains all kinds of critical radiology findings from across the body, such as lung nodules, liver tumors, enlarged lymph nodes, and so on.
The conventional methods for collecting image labels like a search engine does, cannot be applied in the medical image domain. Medical image annotations require extensive clinical experience. But, that could change. The dataset released is large enough to train a deep neural network – it could enable the scientific community to create a large-scale universal lesion detector with one unified framework.
With the release of the dataset, researchers hope the others will be able to:
In 2017, the research hospital released anonymized chest x-ray images and their corresponding data.