Literature DB >> 21739112

Combination of computer-aided detection algorithms for automatic lung nodule identification.

Niccolò Camarlinghi1, Ilaria Gori, Alessandra Retico, Roberto Bellotti, Paolo Bosco, Piergiorgio Cerello, Gianfranco Gargano, Ernesto Lopez Torres, Rosario Megna, Marco Peccarisi, Maria Evelina Fantacci.   

Abstract

PURPOSE: The aim of this work is to evaluate the potential of combining different computer-aided detection (CADe) methods to increase the actual support for radiologists of automated systems in the identification of pulmonary nodules in CT scans.
METHODS: The outputs of three different CADe systems developed by researchers of the Italian MAGIC-5 collaboration were combined. The systems are: the CAMCADe (based on a Channeler-Ant-Model which segments vessel tree and nodule candidates and a neural classifier), the RGVPCADe (a Region-Growing- Volume-Plateau algorithm detects nodule candidates and a neural network reduces false positives); the VBNACADe (two dedicated procedures, based respectively on a 3D dot-enhancement algorithm and on intersections of pleura surface normals, identifies internal and juxtapleural nodules, and a Voxel-Based-Neural-Approach reduces false positives. A dedicated OsiriX plugin implemented with the Cocoa environments of MacOSX allows annotating nodules and visualizing singles and combined CADe findings.
RESULTS: The combined CADe has been tested on thin slice (lower than 2 mm) CTs of the LIDC public research database and the results have been compared with those obtained by the single systems. The FROC (Free Receiver Operating Characteristic) curves show better results than the best of the single approaches.
CONCLUSIONS: Has been demonstrated that the combination of different approaches offers better results than each single CADe system. A clinical validation of the combined CADe as second reader is being addressed by means of the dedicated OsiriX plugin.

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Mesh:

Year:  2011        PMID: 21739112     DOI: 10.1007/s11548-011-0637-6

Source DB:  PubMed          Journal:  Int J Comput Assist Radiol Surg        ISSN: 1861-6410            Impact factor:   2.924


  16 in total

1.  Detection of pulmonary nodules at spiral CT: comparison of maximum intensity projection sliding slabs and single-image reporting.

Authors:  S Diederich; M G Lentschig; T R Overbeck; D Wormanns; W Heindel
Journal:  Eur Radiol       Date:  2001       Impact factor: 5.315

2.  On combining computer-aided detection systems.

Authors:  Meindert Niemeijer; Marco Loog; Michael David Abramoff; Max A Viergever; Mathias Prokop; Bram van Ginneken
Journal:  IEEE Trans Med Imaging       Date:  2010-09-02       Impact factor: 10.048

3.  Small pulmonary nodules: effect of two computer-aided detection systems on radiologist performance.

Authors:  Marco Das; Georg Mühlenbruch; Andreas H Mahnken; Thomas G Flohr; Lutz Gündel; Sven Stanzel; Thomas Kraus; Rolf W Günther; Joachim E Wildberger
Journal:  Radiology       Date:  2006-11       Impact factor: 11.105

4.  The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation.

Authors:  Michael F McNitt-Gray; Samuel G Armato; Charles R Meyer; Anthony P Reeves; Geoffrey McLennan; Richie C Pais; John Freymann; Matthew S Brown; Roger M Engelmann; Peyton H Bland; Gary E Laderach; Chris Piker; Junfeng Guo; Zaid Towfic; David P-Y Qing; David F Yankelevitz; Denise R Aberle; Edwin J R van Beek; Heber MacMahon; Ella A Kazerooni; Barbara Y Croft; Laurence P Clarke
Journal:  Acad Radiol       Date:  2007-12       Impact factor: 3.173

5.  Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans.

Authors:  Qiang Li; Shusuke Sone; Kunio Doi
Journal:  Med Phys       Date:  2003-08       Impact factor: 4.071

6.  Pleural nodule identification in low-dose and thin-slice lung computed tomography.

Authors:  A Retico; M E Fantacci; I Gori; P Kasae; B Golosio; A Piccioli; P Cerello; G De Nunzio; S Tangaro
Journal:  Comput Biol Med       Date:  2009-11-01       Impact factor: 4.589

7.  Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008.

Authors:  Jacques Ferlay; Hai-Rim Shin; Freddie Bray; David Forman; Colin Mathers; Donald Maxwell Parkin
Journal:  Int J Cancer       Date:  2010-12-15       Impact factor: 7.396

8.  Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT.

Authors:  David S Paik; Christopher F Beaulieu; Geoffrey D Rubin; Burak Acar; R Brooke Jeffrey; Judy Yee; Joyoni Dey; Sandy Napel
Journal:  IEEE Trans Med Imaging       Date:  2004-06       Impact factor: 10.048

9.  Automatic lung segmentation in CT images with accurate handling of the hilar region.

Authors:  Giorgio De Nunzio; Eleonora Tommasi; Antonella Agrusti; Rosella Cataldo; Ivan De Mitri; Marco Favetta; Silvio Maglio; Andrea Massafra; Maurizio Quarta; Massimo Torsello; Ilaria Zecca; Roberto Bellotti; Sabina Tangaro; Piero Calvini; Niccolò Camarlinghi; Fabio Falaschi; Piergiorgio Cerello; Piernicola Oliva
Journal:  J Digit Imaging       Date:  2009-10-14       Impact factor: 4.056

10.  Computer-aided detection of lung nodules on multidetector row computed tomography using three-dimensional analysis of nodule candidates and their surroundings.

Authors:  Sumiaki Matsumoto; Yoshiharu Ohno; Hitoshi Yamagata; Daisuke Takenaka; Kazuro Sugimura
Journal:  Radiat Med       Date:  2008-11-22
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  9 in total

1.  Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter.

Authors:  Atsushi Teramoto; Hiroshi Fujita
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-06-09       Impact factor: 2.924

2.  Anatomy of an Extensible Open Source PACS.

Authors:  Frederico Valente; Luís A Bastião Silva; Tiago Marques Godinho; Carlos Costa
Journal:  J Digit Imaging       Date:  2016-06       Impact factor: 4.056

3.  A comparison of axial versus coronal image viewing in computer-aided detection of lung nodules on CT.

Authors:  Tae Iwasawa; Sumiaki Matsumoto; Takatoshi Aoki; Fumito Okada; Yoshihiro Nishimura; Hitoshi Yamagata; Yoshiharu Ohno
Journal:  Jpn J Radiol       Date:  2014-12-23       Impact factor: 2.374

4.  Large scale validation of the M5L lung CAD on heterogeneous CT datasets.

Authors:  E Lopez Torres; E Fiorina; F Pennazio; C Peroni; M Saletta; N Camarlinghi; M E Fantacci; P Cerello
Journal:  Med Phys       Date:  2015-04       Impact factor: 4.071

5.  A unified methodology based on sparse field level sets and boosting algorithms for false positives reduction in lung nodules detection.

Authors:  Soudeh Saien; Hamid Abrishami Moghaddam; Mohsen Fathian
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-08-09       Impact factor: 2.924

6.  Fully automated detection of breast cancer in screening MRI using convolutional neural networks.

Authors:  Mehmet Ufuk Dalmış; Suzan Vreemann; Thijs Kooi; Ritse M Mann; Nico Karssemeijer; Albert Gubern-Mérida
Journal:  J Med Imaging (Bellingham)       Date:  2018-01-11

7.  Vascular decomposition using weighted approximate convex decomposition.

Authors:  Ashirwad Chowriappa; T Kesavadas; Maxim Mokin; Peter Kan; Sarthak Salunke; Sabareesh K Natarajan; Peter D Scott
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-06-13       Impact factor: 2.924

8.  Expert knowledge-infused deep learning for automatic lung nodule detection.

Authors:  Jiaxing Tan; Yumei Huo; Zhengrong Liang; Lihong Li
Journal:  J Xray Sci Technol       Date:  2019       Impact factor: 1.535

9.  Lung Cancer Detection Using Fuzzy Auto-Seed Cluster Means Morphological Segmentation and SVM Classifier.

Authors:  T Manikandan; N Bharathi
Journal:  J Med Syst       Date:  2016-06-14       Impact factor: 4.460

  9 in total

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