Literature DB >> 20573538

Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study.

Bram van Ginneken1, Samuel G Armato, Bartjan de Hoop, Saskia van Amelsvoort-van de Vorst, Thomas Duindam, Meindert Niemeijer, Keelin Murphy, Arnold Schilham, Alessandra Retico, Maria Evelina Fantacci, Niccolò Camarlinghi, Francesco Bagagli, Ilaria Gori, Takeshi Hara, Hiroshi Fujita, Gianfranco Gargano, Roberto Bellotti, Sabina Tangaro, Lourdes Bolaños, Francesco De Carlo, Piergiorgio Cerello, Sorin Cristian Cheran, Ernesto Lopez Torres, Mathias Prokop.   

Abstract

Numerous publications and commercial systems are available that deal with automatic detection of pulmonary nodules in thoracic computed tomography scans, but a comparative study where many systems are applied to the same data set has not yet been performed. This paper introduces ANODE09 ( http://anode09.isi.uu.nl), a database of 55 scans from a lung cancer screening program and a web-based framework for objective evaluation of nodule detection algorithms. Any team can upload results to facilitate benchmarking. The performance of six algorithms for which results are available are compared; five from academic groups and one commercially available system. A method to combine the output of multiple systems is proposed. Results show a substantial performance difference between algorithms, and demonstrate that combining the output of algorithms leads to marked performance improvements. Copyright 2010 Elsevier B.V. All rights reserved.

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Year:  2010        PMID: 20573538     DOI: 10.1016/j.media.2010.05.005

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  38 in total

1.  LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned.

Authors:  Samuel G Armato; Lubomir Hadjiiski; Georgia D Tourassi; Karen Drukker; Maryellen L Giger; Feng Li; George Redmond; Keyvan Farahani; Justin S Kirby; Laurence P Clarke
Journal:  J Med Imaging (Bellingham)       Date:  2015-04

Review 2.  Challenges in diffusion MRI tractography - Lessons learned from international benchmark competitions.

Authors:  Kurt G Schilling; Alessandro Daducci; Klaus Maier-Hein; Cyril Poupon; Jean-Christophe Houde; Vishwesh Nath; Adam W Anderson; Bennett A Landman; Maxime Descoteaux
Journal:  Magn Reson Imaging       Date:  2018-11-29       Impact factor: 2.546

3.  Feasibility Study of a Generalized Framework for Developing Computer-Aided Detection Systems-a New Paradigm.

Authors:  Mitsutaka Nemoto; Naoto Hayashi; Shouhei Hanaoka; Yukihiro Nomura; Soichiro Miki; Takeharu Yoshikawa
Journal:  J Digit Imaging       Date:  2017-10       Impact factor: 4.056

4.  Pulmonary nodule classification in lung cancer screening with three-dimensional convolutional neural networks.

Authors:  Shuang Liu; Yiting Xie; Artit Jirapatnakul; Anthony P Reeves
Journal:  J Med Imaging (Bellingham)       Date:  2017-11-14

5.  Automatic segmentation of pulmonary blood vessels and nodules based on local intensity structure analysis and surface propagation in 3D chest CT images.

Authors:  Bin Chen; Takayuki Kitasaka; Hirotoshi Honma; Hirotsugu Takabatake; Masaki Mori; Hiroshi Natori; Kensaku Mori
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-07-08       Impact factor: 2.924

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

Authors:  Niccolò Camarlinghi; Ilaria Gori; Alessandra Retico; Roberto Bellotti; Paolo Bosco; Piergiorgio Cerello; Gianfranco Gargano; Ernesto Lopez Torres; Rosario Megna; Marco Peccarisi; Maria Evelina Fantacci
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-07-08       Impact factor: 2.924

7.  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

8.  Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme.

Authors:  Hao Han; Lihong Li; Fangfang Han; Bowen Song; William Moore; Zhengrong Liang
Journal:  IEEE J Biomed Health Inform       Date:  2014-06-04       Impact factor: 5.772

9.  A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images.

Authors:  Avan Suinesiaputra; Brett R Cowan; Ahmed O Al-Agamy; Mustafa A Elattar; Nicholas Ayache; Ahmed S Fahmy; Ayman M Khalifa; Pau Medrano-Gracia; Marie-Pierre Jolly; Alan H Kadish; Daniel C Lee; Ján Margeta; Simon K Warfield; Alistair A Young
Journal:  Med Image Anal       Date:  2013-09-13       Impact factor: 8.545

10.  Multi-path x-D Recurrent Neural Networks for Collaborative Image Classification.

Authors:  Riqiang Gao; Yuankai Huo; Shunxing Bao; Yucheng Tang; Sanja L Antic; Emily S Epstein; Steve Deppen; Alexis B Paulson; Kim L Sandler; Pierre P Massion; Bennett A Landman
Journal:  Neurocomputing       Date:  2020-02-15       Impact factor: 5.719

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