Literature DB >> 20451814

Improving the accuracy of CTC interpretation: computer-aided detection.

Ronald M Summers1.   

Abstract

Computer-aided polyp detection aims to improve the accuracy of the colonography interpretation. The computer searches the colonic wall to look for polyplike protrusions and presents a list of suspicious areas to a physician for further analysis. Computer-aided polyp detection has developed rapidly in the past decade in the laboratory setting and has sensitivities comparable with those of experts. Computer-aided polyp detection tends to help inexperienced readers more than experienced ones and may also lead to small reductions in specificity. In its currently proposed use as an adjunct to standard image interpretation, computer-aided polyp detection serves as a spellchecker rather than an efficiency enhancer. Copyright 2010 Elsevier Inc. All rights reserved.

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Year:  2010        PMID: 20451814      PMCID: PMC2868270          DOI: 10.1016/j.giec.2010.02.004

Source DB:  PubMed          Journal:  Gastrointest Endosc Clin N Am        ISSN: 1052-5157


  72 in total

1.  Reduction of false positives on the rectal tube in computer-aided detection for CT colonography.

Authors:  Gheorghe Lordanescu; Ronald M Summers
Journal:  Med Phys       Date:  2004-10       Impact factor: 4.071

2.  Computer-assisted reader software versus expert reviewers for polyp detection on CT colonography.

Authors:  Stuart A Taylor; Steve Halligan; David Burling; Mary E Roddie; Lesley Honeyfield; Justine McQuillan; Hamdam Amin; Jamshid Dehmeshki
Journal:  AJR Am J Roentgenol       Date:  2006-03       Impact factor: 3.959

3.  Extracolonic findings identified in asymptomatic adults at screening CT colonography.

Authors:  Perry J Pickhardt; Andrew J Taylor
Journal:  AJR Am J Roentgenol       Date:  2006-03       Impact factor: 3.959

4.  Oral contrast adherence to polyps on CT colonography.

Authors:  Stacy D O'Connor; Ronald M Summers; J Richard Choi; Perry J Pickhardt
Journal:  J Comput Assist Tomogr       Date:  2006 Jan-Feb       Impact factor: 1.826

5.  Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population.

Authors:  Ronald M Summers; Jianhua Yao; Perry J Pickhardt; Marek Franaszek; Ingmar Bitter; Daniel Brickman; Vamsi Krishna; J Richard Choi
Journal:  Gastroenterology       Date:  2005-12       Impact factor: 22.682

6.  Region-based supine-prone correspondence for the reduction of false-positive CAD polyp candidates in CT colonography.

Authors:  Janne Näppi; Akihiko Okamura; Hans Frimmel; Abraham Dachman; Hiroyuki Yoshida
Journal:  Acad Radiol       Date:  2005-06       Impact factor: 3.173

7.  Automated insufflation of carbon dioxide for MDCT colonography: distension and patient experience compared with manual insufflation.

Authors:  David Burling; Stuart A Taylor; Steve Halligan; Louise Gartner; Mehjabeen Paliwalla; Chandani Peiris; Leanne Singh; Paul Bassett; Clive Bartram
Journal:  AJR Am J Roentgenol       Date:  2006-01       Impact factor: 3.959

Review 8.  Computer-aided detection (CAD) for CT colonography: a tool to address a growing need.

Authors:  L Bogoni; P Cathier; M Dundar; A Jerebko; S Lakare; J Liang; S Periaswamy; M E Baker; M Macari
Journal:  Br J Radiol       Date:  2005       Impact factor: 3.039

9.  Reduction of false positives by internal features for polyp detection in CT-based virtual colonoscopy.

Authors:  Zigang Wang; Zhengrong Liang; Lihong Li; Xiang Li; Bin Li; Joseph Anderson; Donald Harrington
Journal:  Med Phys       Date:  2005-12       Impact factor: 4.071

10.  Hybrid segmentation of colon filled with air and opacified fluid for CT colonography.

Authors:  Marek Franaszek; Ronald M Summers; Perry J Pickhardt; J Richard Choi
Journal:  IEEE Trans Med Imaging       Date:  2006-03       Impact factor: 10.048

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  9 in total

1.  Optimizing area under the ROC curve using semi-supervised learning.

Authors:  Shijun Wang; Diana Li; Nicholas Petrick; Berkman Sahiner; Marius George Linguraru; Ronald M Summers
Journal:  Pattern Recognit       Date:  2015-01-01       Impact factor: 7.740

2.  Distributed human intelligence for colonic polyp classification in computer-aided detection for CT colonography.

Authors:  Tan B Nguyen; Shijun Wang; Vishal Anugu; Natalie Rose; Matthew McKenna; Nicholas Petrick; Joseph E Burns; Ronald M Summers
Journal:  Radiology       Date:  2012-01-24       Impact factor: 11.105

Review 3.  Machine Learning for Medical Imaging.

Authors:  Bradley J Erickson; Panagiotis Korfiatis; Zeynettin Akkus; Timothy L Kline
Journal:  Radiographics       Date:  2017-02-17       Impact factor: 5.333

Review 4.  Progress in Fully Automated Abdominal CT Interpretation.

Authors:  Ronald M Summers
Journal:  AJR Am J Roentgenol       Date:  2016-04-21       Impact factor: 3.959

5.  Machine Learning for the Prediction of Cervical Spondylotic Myelopathy: A Post Hoc Pilot Study of 28 Participants.

Authors:  Benjamin S Hopkins; Kenneth A Weber; Kartik Kesavabhotla; Monica Paliwal; Donald R Cantrell; Zachary A Smith
Journal:  World Neurosurg       Date:  2019-03-25       Impact factor: 2.104

Review 6.  Machine learning and radiology.

Authors:  Shijun Wang; Ronald M Summers
Journal:  Med Image Anal       Date:  2012-02-23       Impact factor: 8.545

7.  New Approach for Risk Estimation Algorithms of BRCA1/2 Negativeness Detection with Modelling Supervised Machine Learning Techniques.

Authors:  Hulya Yazici; Demet Akdeniz Odemis; Dogukan Aksu; Ozge Sukruoglu Erdogan; Seref Bugra Tuncer; Mukaddes Avsar; Seda Kilic; Gozde Kuru Turkcan; Betul Celik; Muhammed Ali Aydin
Journal:  Dis Markers       Date:  2020-12-09       Impact factor: 3.434

8.  Automatic Pancreatic Ductal Adenocarcinoma Detection in Whole Slide Images Using Deep Convolutional Neural Networks.

Authors:  Hao Fu; Weiming Mi; Boju Pan; Yucheng Guo; Junjie Li; Rongyan Xu; Jie Zheng; Chunli Zou; Tao Zhang; Zhiyong Liang; Junzhong Zou; Hao Zou
Journal:  Front Oncol       Date:  2021-06-25       Impact factor: 6.244

9.  Automatic classification of cervical cancer from cytological images by using convolutional neural network.

Authors:  Miao Wu; Chuanbo Yan; Huiqiang Liu; Qian Liu; Yi Yin
Journal:  Biosci Rep       Date:  2018-11-28       Impact factor: 3.840

  9 in total

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