Lung image database consortium: developing a resource for the medical imaging research community.

By S. Armato, G. McLennan, M. McNitt-Gray, Charles R. Meyer, D. Yankelevitz, D. Aberle, C. Henschke, E. Hoffman, E. Kazerooni, H. MacMahon and 3 more. Radiology, 2004

429 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Clinical Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/aa0658c7b0692a98f3f56b56ead638e7f8e0fddd

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Citations in the corpus

Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans (2006, methodology): “A standardization of the evaluations and thereby, a fair quantitative comparison of algorithms will eventually become possible when initiatives such as the Lung Image Database Consortium (LIDC) [18] have succeeded in providing a sufficiently large, publicly accessible set of dual-scan data for repro”
Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study (2010, methodology): “In the LIDC study four observers indicated nodules in 90 chest CT scans in a two step process, ﬁrst blinded, next unblinded, so that they could see the results of the three other readers.”
Computer analysis of computed tomography scans of the lung: a survey (2006, methodology): “The availability of common databases [110] could spur further development of the CAD systems substantially.”
Diffusion models in medical imaging: A comprehensive survey (2022, background): “[123] introduced in their work, which investigates three datasets (one private and two publicly available ones) with different modalities [127, 128].”
Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets (2009, background): “On the other side, open data repositories for e.g., brain, 3 lung [2] or retinal data 4 provide a comprehensive set of clinical images to evaluate speciﬁc applications.”
3D Segmentation in the Clinic: A Grand Challenge (2007, background): “1 See http://www.tuat.ac.jp/~simizlab/CADM/report_of_competition2005.pdf 2 See http://www.cma.mgh.harvard.edu/ibsr/ x-ray data.”
Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data (2013, background): “The Cancer Imaging Archive (TCIA) [106] is a set of several image collections, each of which was built for a particular purpose, such as the Lung Imaging Database Consortium (LIDC) [107] of chest CTand X-rays.”
Med3D: Transfer Learning for 3D Medical Image Analysis (2019, methodology): “LIDC dataset [40, 17] collected thoracic CT scans from 1,010 patients and nodules of each CT scan were annotated by four radiologists.”
Lung Nodule Classification Using Deep Features in CT Images (2015, background): “LIDC (Lung Cancer Data Consortium) [5], [6] is one such database which contains CT images of thoracic region for 1010 patients along with the annotation data of suspicious nodules (for both benign and malignant cases) for a size greater than 3 mm from upto four radiologists collected over a long…”