2011
DOI: 10.1118/1.3528204
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Abstract: Purpose:The development of computer-aided diagnostic ͑CAD͒ methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography ͑CT͒ scans. The Lung Image Database Consortium ͑LIDC͒ and Image Database Resource Initiative ͑IDRI͒ completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute ͑NCI͒, further advanced by the Founda… Show more

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Cited by 1,825 publications
(942 citation statements)
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References 46 publications
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“…The proposed segmentation method is evaluated on 891 nodules in Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database [2]. In LIDC/IDRI database, 928 lesions are annotated by Fig.…”
Section: Database Of Lung Ct Imagesmentioning
confidence: 99%
See 1 more Smart Citation
“…The proposed segmentation method is evaluated on 891 nodules in Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database [2]. In LIDC/IDRI database, 928 lesions are annotated by Fig.…”
Section: Database Of Lung Ct Imagesmentioning
confidence: 99%
“…In LIDC/IDRI database, 928 lesions are annotated by Fig. 3 Proposed framework for segmentation of pulmonary nodules all four radiologists [2]. Out of 928 nodules, the boundary annotation by all four radiologists is available for 891 nodules.…”
Section: Database Of Lung Ct Imagesmentioning
confidence: 99%
“…Generally, these databases are used to train students, to serve as a repository for rare cases, and to allow comparisons between the performance of different CADe systems [61]. Among the more important public databases available are: Lung Image Database Consortium (LIDC) [62,63], Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) [64,65], Early Lung Cancer Action Program (ELCAP) [7], Nederlands Leuvens Longkanker Screeningsonderzoek (NELSON) [66] and Automatic Nodule Detection 2009 (ANODE09) [67,68].…”
Section: Acquisition Of Datamentioning
confidence: 99%
“…The database contains 7,371 lesions marked "nodule" by at least one radiologist, and 2,669 of these lesions were marked "nodule ≥ 3 mm" by at least one radiologist, of which 928 (34.7%) received the same ratings from all four radiologists. These 2,669 lesions include nodule outlines and subjective nodule characteristic ratings [64,65].…”
Section: Acquisition Of Datamentioning
confidence: 99%
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