Literature DB >> 12511699

Lung micronodules: automated method for detection at thin-section CT--initial experience.

Matthew S Brown1, Jonathan G Goldin, Robert D Suh, Michael F McNitt-Gray, James W Sayre, Denise R Aberle.   

Abstract

An automated system was developed for detecting lung micronodules on thin-section computed tomographic images and was applied to data from 15 subjects with 77 lung nodules. The automated system, without user interaction, achieved a sensitivity of 100% for nodules (>3 mm in diameter) and 70% for micronodules (<or=3 mm). With the same images, a radiologist detected nodules and micronodules with sensitivities of 91% and 51%, respectively, without system input. With assistance from the automated system, these sensitivities increased to 95% and 74%, respectively. Preliminary results indicate that the automated system considerably improved the radiologist's performance in micronodule detection. Copyright RSNA, 2002

Mesh:

Year:  2003        PMID: 12511699     DOI: 10.1148/radiol.2261011708

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


  22 in total

1.  Computer-assisted detection of pulmonary nodules: preliminary observations using a prototype system with multidetector-row CT data sets.

Authors:  Leo P Lawler; Susan A Wood; Harpreet K Pannu; Elliot K Fishman; Harpreet S Pannu
Journal:  J Digit Imaging       Date:  2003-12-15       Impact factor: 4.056

2.  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

Review 3.  Recent progress in computer-aided diagnosis of lung nodules on thin-section CT.

Authors:  Qiang Li
Journal:  Comput Med Imaging Graph       Date:  2007-03-21       Impact factor: 4.790

4.  Automated classification of lung bronchovascular anatomy in CT using AdaBoost.

Authors:  Robert A Ochs; Jonathan G Goldin; Fereidoun Abtin; Hyun J Kim; Kathleen Brown; Poonam Batra; Donald Roback; Michael F McNitt-Gray; Matthew S Brown
Journal:  Med Image Anal       Date:  2007-03-30       Impact factor: 8.545

5.  Pulmonary nodule detection on MDCT images: evaluation of diagnostic performance using thin axial images, maximum intensity projections, and computer-assisted detection.

Authors:  A Jankowski; T Martinelli; J F Timsit; C Brambilla; F Thony; M Coulomb; G Ferretti
Journal:  Eur Radiol       Date:  2007-09-01       Impact factor: 5.315

6.  Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier.

Authors:  Qiang Li; Feng Li; Kunio Doi
Journal:  Acad Radiol       Date:  2008-02       Impact factor: 3.173

7.  Multi slice computed tomography in the study of pulmonary metastases.

Authors:  G Angelelli; V Grimaldi; F Spinelli; A Scardapane; A Sardaro
Journal:  Radiol Med       Date:  2008-09-08       Impact factor: 3.469

Review 8.  Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography-a review.

Authors:  Heang-Ping Chan; Lubomir Hadjiiski; Chuan Zhou; Berkman Sahiner
Journal:  Acad Radiol       Date:  2008-05       Impact factor: 3.173

9.  Toward clinically usable CAD for lung cancer screening with computed tomography.

Authors:  Matthew S Brown; Pechin Lo; Jonathan G Goldin; Eran Barnoy; Grace Hyun J Kim; Michael F McNitt-Gray; Denise R Aberle
Journal:  Eur Radiol       Date:  2014-07-24       Impact factor: 5.315

10.  Automated lung nodule detection at low-dose CT: preliminary experience.

Authors:  Jin Mo Goo; Jeong Won Lee; Hyun Ju Lee; Seunghwan Kim; Jong Hyo Kim; Jung-Gi Im
Journal:  Korean J Radiol       Date:  2003 Oct-Dec       Impact factor: 3.500

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