Literature DB >> 15333795

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

Samuel G Armato1, Geoffrey McLennan, Michael F McNitt-Gray, Charles R Meyer, David Yankelevitz, Denise R Aberle, Claudia I Henschke, Eric A Hoffman, Ella A Kazerooni, Heber MacMahon, Anthony P Reeves, Barbara Y Croft, Laurence P Clarke.   

Abstract

To stimulate the advancement of computer-aided diagnostic (CAD) research for lung nodules in thoracic computed tomography (CT), the National Cancer Institute launched a cooperative effort known as the Lung Image Database Consortium (LIDC). The LIDC is composed of five academic institutions from across the United States that are working together to develop an image database that will serve as an international research resource for the development, training, and evaluation of CAD methods in the detection of lung nodules on CT scans. Prior to the collection of CT images and associated patient data, the LIDC has been engaged in a consensus process to identify, address, and resolve a host of challenging technical and clinical issues to provide a solid foundation for a scientifically robust database. These issues include the establishment of (a) a governing mission statement, (b) criteria to determine whether a CT scan is eligible for inclusion in the database, (c) an appropriate definition of the term qualifying nodule, (d) an appropriate definition of "truth" requirements, (e) a process model through which the database will be populated, and (f) a statistical framework to guide the application of assessment methods by users of the database. Through a consensus process in which careful planning and proper consideration of fundamental issues have been emphasized, the LIDC database is expected to provide a powerful resource for the medical imaging research community. This article is intended to share with the community the breadth and depth of these key issues. Copyright RSNA, 2004

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Year:  2004        PMID: 15333795     DOI: 10.1148/radiol.2323032035

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  60 in total

1.  Consensus versus disagreement in imaging research: a case study using the LIDC database.

Authors:  Dmitriy Zinovev; Yujie Duo; Daniela S Raicu; Jacob Furst; Samuel G Armato
Journal:  J Digit Imaging       Date:  2012-06       Impact factor: 4.056

2.  Computerized lung nodule detection using 3D feature extraction and learning based algorithms.

Authors:  Serhat Ozekes; Onur Osman
Journal:  J Med Syst       Date:  2010-04       Impact factor: 4.460

3.  Mapping LIDC, RadLex™, and lung nodule image features.

Authors:  Pia Opulencia; David S Channin; Daniela S Raicu; Jacob D Furst
Journal:  J Digit Imaging       Date:  2011-04       Impact factor: 4.056

4.  Value of axial and coronal maximum intensity projection (MIP) images in the detection of pulmonary nodules by multislice spiral CT: comparison with axial 1-mm and 5-mm slices.

Authors:  Ray Valencia; Timm Denecke; Lukas Lehmkuhl; Frank Fischbach; Roland Felix; Friedrich Knollmann
Journal:  Eur Radiol       Date:  2005-08-16       Impact factor: 5.315

5.  Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.

Authors:  Ted W Way; Lubomir M Hadjiiski; Berkman Sahiner; Heang-Ping Chan; Philip N Cascade; Ella A Kazerooni; Naama Bogot; Chuan Zhou
Journal:  Med Phys       Date:  2006-07       Impact factor: 4.071

6.  Evaluation of lung MDCT nodule annotation across radiologists and methods.

Authors:  Charles R Meyer; Timothy D Johnson; Geoffrey McLennan; Denise R Aberle; Ella A Kazerooni; Heber Macmahon; Brian F Mullan; David F Yankelevitz; Edwin J R van Beek; Samuel G Armato; Michael F McNitt-Gray; Anthony P Reeves; David Gur; Claudia I Henschke; Eric A Hoffman; Peyton H Bland; Gary Laderach; Richie Pais; David Qing; Chris Piker; Junfeng Guo; Adam Starkey; Daniel Max; Barbara Y Croft; Laurence P Clarke
Journal:  Acad Radiol       Date:  2006-10       Impact factor: 3.173

7.  A knowledge-anchored integrative image search and retrieval system.

Authors:  Selnur Erdal; Umit V Catalyurek; Philip R O Payne; Joel Saltz; Jyoti Kamal; Metin N Gurcan
Journal:  J Digit Imaging       Date:  2007-11-27       Impact factor: 4.056

8.  A review of computer-aided diagnosis in thoracic and colonic imaging.

Authors:  Kenji Suzuki
Journal:  Quant Imaging Med Surg       Date:  2012-09

9.  Computed tomography assessment of response to therapy: tumor volume change measurement, truth data, and error.

Authors:  Michael F McNitt-Gray; Luc M Bidaut; Samuel G Armato; Charles R Meyer; Marios A Gavrielides; Charles Fenimore; Geoffrey McLennan; Nicholas Petrick; Binsheng Zhao; Anthony P Reeves; Reinhard Beichel; Hyun-Jung Grace Kim; Lisa Kinnard
Journal:  Transl Oncol       Date:  2009-12       Impact factor: 4.243

Review 10.  The importance of the regimen of screening in maximizing the benefit and minimizing the harms.

Authors:  Claudia I Henschke; Kunwei Li; Rowena Yip; Mary Salvatore; David F Yankelevitz
Journal:  Ann Transl Med       Date:  2016-04
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