Literature DB >> 15647868

CAD techniques, challenges, and controversies in computed tomographic colonography.

H Yoshida1, A H Dachman.   

Abstract

Computer-aided diagnosis (CAD) for computed tomographic colonography (CTC) automatically detects the locations of suspicious polyps and masses on CTC and provides radiologists with a second opinion. CAD has the potential to increase radiologists' diagnostic performance in the detection of polyps and masses and to decrease variability of the diagnostic accuracy among readers without significantly increasing the reading time. Technical developments have advanced CAD substantially during the past several years, and a fundamental scheme for the detection of polyps has been established. The most recent CAD systems based on this scheme produce a clinically acceptable high sensitivity and a low false-positive rate. However, CAD for CTC is still under active development, and the technology needs to be improved further. This report describes the expected benefits, the current fundamental scheme, the key techniques used for detection of polyps and masses on CTC, the current detection performance, as well as the pitfalls, challenges, controversies, and the future of CAD.

Entities:  

Mesh:

Year:  2005        PMID: 15647868     DOI: 10.1007/s00261-004-0244-x

Source DB:  PubMed          Journal:  Abdom Imaging        ISSN: 0942-8925


  20 in total

1.  The role of informatics in health care reform.

Authors:  Yueyi I Liu; Daniel L Rubin
Journal:  Acad Radiol       Date:  2012-07-06       Impact factor: 3.173

2.  Medical decision-making system of ultrasound carotid artery intima-media thickness using neural networks.

Authors:  N Santhiyakumari; P Rajendran; M Madheswaran
Journal:  J Digit Imaging       Date:  2011-12       Impact factor: 4.056

3.  Fully automated three-dimensional detection of polyps in fecal-tagging CT colonography.

Authors:  Janne Näppi; Hiroyuki Yoshida
Journal:  Acad Radiol       Date:  2007-03       Impact factor: 3.173

4.  Massive-training artificial neural network coupled with Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography.

Authors:  Kenji Suzuki; Jun Zhang; Jianwu Xu
Journal:  IEEE Trans Med Imaging       Date:  2010-06-21       Impact factor: 10.048

5.  Improving Polyp Detection Algorithms for CT Colonography: Pareto Front Approach.

Authors:  Adam Huang; Jiang Li; Ronald M Summers; Nicholas Petrick; Amy K Hara
Journal:  Pattern Recognit Lett       Date:  2010-03-21       Impact factor: 3.756

6.  Optimizing computer-aided colonic polyp detection for CT colonography by evolving the Pareto fronta.

Authors:  Jiang Li; Adam Huang; Jack Yao; Jiamin Liu; Robert L Van Uitert; Nicholas Petrick; Ronald M Summers
Journal:  Med Phys       Date:  2009-01       Impact factor: 4.071

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

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

8.  Comparative performance of a primary-reader and second-reader paradigm of computer-aided detection for CT colonography in a low-prevalence screening population.

Authors:  Mototaka Miyake; Gen Iinuma; Stuart A Taylor; Steve Halligan; Tsuyoshi Morimoto; Tamaki Ichikawa; Hideto Tomimatsu; Gareth Beddoe; Kazuro Sugimura; Yasuaki Arai
Journal:  Jpn J Radiol       Date:  2013-02-19       Impact factor: 2.374

9.  CT colonography: computer-aided detection of morphologically flat T1 colonic carcinoma.

Authors:  Stuart A Taylor; Gen Iinuma; Yutaka Saito; Jie Zhang; Steve Halligan
Journal:  Eur Radiol       Date:  2008-04-04       Impact factor: 5.315

10.  Time-efficient CT colonography interpretation using an advanced image-gallery-based, computer-aided "first-reader" workflow for the detection of colorectal adenomas.

Authors:  Thomas Mang; Gerardo Hermosillo; Matthias Wolf; Luca Bogoni; Marcos Salganicoff; Vikas Raykar; Helmut Ringl; Michael Weber; Christina Mueller-Mang; Anno Graser
Journal:  Eur Radiol       Date:  2012-08-18       Impact factor: 5.315

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