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DICOM re‐encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules
Author(s) -
Fedorov Andrey,
Hancock Matthew,
Clunie David,
Brochhausen Mathias,
Bona Jonathan,
Kirby Justin,
Freymann John,
Pieper Steve,
J. W. L. Aerts Hugo,
Kikinis Ron,
Prior Fred
Publication year - 2020
Publication title -
medical physics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14445
Subject(s) - dicom , computer science , nodule (geology) , medical imaging , information retrieval , database , artificial intelligence , biology , paleontology
The dataset contains annotations for lung nodules collected by the Lung Imaging Data Consortium and Image Database Resource Initiative (LIDC) stored as standard DICOM objects. The annotations accompany a collection of computed tomography (CT) scans for over 1000 subjects annotated by multiple expert readers, and correspond to "nodules ≥ 3 mm", defined as any lesion considered to be a nodule with greatest in-plane dimension in the range 3-30 mm regardless of presumed histology. The present dataset aims to simplify reuse of the data with the readily available tools, and is targeted towards researchers interested in the analysis of lung CT images.