Cited by 573AI in cancer detectionRadiomics and Machine Learning in Medical ImagingMedical Image Segmentation Techniques

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DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning

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Yan, Ke, Wang, Xiaosong, Lu, Le, Summers, Ronald M.·2018·Journal of medical imaging

DOI 10.1117/1.jmi.5.3.036501PMC PMC6052252

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Abstract

Extracting, harvesting, and building large-scale annotated radiological image datasets is a greatly important yet challenging problem. Meanwhile, vast amounts of clinical annotations have been collected and stored in hospitals' picture archiving and communication systems (PACS). These types of annotations, also known as bookmarks in PACS, are usually marked by radiologists during their daily workflow to highlight significant image findings that may serve as reference for later studies. We propose to mine and harvest these abundant retrospective medical data to build a large-scale lesion image dataset. Our process is scalable and requires minimum manual annotation effort. We mine bookmarks in our institute to develop DeepLesion, a dataset with 32,735 lesions in 32,120 CT slices from 10,594 studies of 4,427 unique patients. There are a variety of lesion types in this dataset, such as lung nodules, liver tumors, enlarged lymph nodes, and so on. It has the potential to be used in various medical image applications. Using DeepLesion, we train a universal lesion detector that can find all types of lesions with one unified framework. In this challenging task, the proposed lesion detector achieves a sensitivity of 81.1% with five false positives per image.

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Authors: Yan, K., Wang, X., Lu, L., & Summers, R. M.
Affiliations: National Institutes of Health Clinical Center (US)
National Institutes of Health Clinical Ctr.
ORCID: Yan, Ke
Wang, Xiaosong
Summers, Ronald M.
Venue: Journal of medical imaging
Publisher: SPIE-Intl Soc Optical Eng
Published: 2018-07-20
Volume: 5
Issue: 03
Pages: 1-11
ISSN: 2329-4302 (ISSN-L)
2329-4310
DOI: 10.1117/1.jmi.5.3.036501
PMC: PMC6052252
OpenAlex: W2883683269
Cited by: 573
References: 30
Citations by year: 2026: 65 · 2025: 81 · 2024: 75 · 2023: 81 · 2022: 78 · 2021: 88 · 2020: 66 · 2019: 36 · 2018: 3
FWCI: 27.16
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: other-oa
Locations: Journal of medical imaging (publishedVersion, other-oa)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: Intramural Research Program of NIH, Clinical Center
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords: lesion detection, CT images, pulmonary nodules, liver tumors, lymphadenopathy, DeepLesion

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DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning

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