Literature DB >> 17376650

CAD in CT colonography without and with oral contrast agents: progress and challenges.

Hiroyuki Yoshida1, Janne Näppi.   

Abstract

Computed tomographic colonography (CTC), also known as virtual colonoscopy, is an emerging alternative technique for screening of colon cancers. CTC uses CT to provide a series of cross-sectional images of the colon for detection of polyps and masses. Fecal tagging is a means of labeling of residual feces by an oral contrast agent for improving the accuracy in the detection of polyps. Computer-aided diagnosis (CAD) for CTC automatically determines the locations of suspicious polyps and masses in CTC and presents them to radiologists, typically as a second opinion. Despite its relatively short history, CAD has become one of the mainstream techniques that could make CTC prime time for screening of colorectal cancer. Rapid technical developments have advanced CAD substantially during the last several years, and a fundamental scheme for the detection of polyps has been established, in which sophisticated 3D image processing, analysis, and display techniques play a pivotal role. The latest CAD systems indicate a clinically acceptable high sensitivity and a low false-positive rate, and observer studies have demonstrated the benefits of these systems in improving radiologists' detection performance. Some technical and clinical challenges, however, remain unresolved before CAD can become a truly useful tool for clinical practice. Also, new challenges are facing CAD as the methods for bowel preparation and image acquisition, such as tagging of fecal residue with oral contrast agents, and interpretation of CTC images evolve. This article reviews the current status and future challenges in CAD for CTC without and with fecal tagging.

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Year:  2007        PMID: 17376650     DOI: 10.1016/j.compmedimag.2007.02.011

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  17 in total

1.  Automated segmentation of hepatic vessels in non-contrast X-ray CT images.

Authors:  Suguru Kawajiri; Xiangrong Zhou; Xuejun Zhang; Takeshi Hara; Hiroshi Fujita; Ryujiro Yokoyama; Hiroshi Kondo; Masayuki Kanematsu; Hiroaki Hoshi
Journal:  Radiol Phys Technol       Date:  2008-07-01

2.  Deep learning-based image restoration algorithm for coronary CT angiography.

Authors:  Fuminari Tatsugami; Toru Higaki; Yuko Nakamura; Zhou Yu; Jian Zhou; Yujie Lu; Chikako Fujioka; Toshiro Kitagawa; Yasuki Kihara; Makoto Iida; Kazuo Awai
Journal:  Eur Radiol       Date:  2019-04-08       Impact factor: 5.315

3.  Adaptive correction of the pseudo-enhancement of CT attenuation for fecal-tagging CT colonography.

Authors:  Janne Näppi; Hiroyuki Yoshida
Journal:  Med Image Anal       Date:  2008-01-26       Impact factor: 8.545

Review 4.  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

5.  Efficacy of computer aided analysis in detection of significant coronary artery stenosis in cardiac using dual source computed tomography.

Authors:  Anja J Reimann; Ilias Tsiflikas; Harald Brodoefel; Michael Scheuering; Daniel Rinck; Andreas F Kopp; Claus D Claussen; Martin Heuschmid
Journal:  Int J Cardiovasc Imaging       Date:  2008-09-28       Impact factor: 2.357

Review 6.  Progress in Fully Automated Abdominal CT Interpretation.

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

Review 7.  CT colonography with computer-aided detection: recognizing the causes of false-positive reader results.

Authors:  Igor Trilisky; Kristen Wroblewski; Michael W Vannier; John M Horne; Abraham H Dachman
Journal:  Radiographics       Date:  2014 Nov-Dec       Impact factor: 5.333

8.  A comparison of material decomposition techniques for dual-energy CT colonography.

Authors:  Radin A Nasirudin; Rie Tachibana; Janne J Näppi; Kai Mei; Felix K Kopp; Ernst J Rummeny; Hiroyuki Yoshida; Peter B Noël
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2015-02-21

9.  Comparison of three different iodine-based bowel regimens for CT colonography.

Authors:  Delia Campanella; Lia Morra; Silvia Delsanto; Vincenzo Tartaglia; Roberto Asnaghi; Alberto Bert; Emanuele Neri; Daniele Regge
Journal:  Eur Radiol       Date:  2009-08-27       Impact factor: 5.315

10.  Analysis of scalability of high-performance 3D image processing platform for virtual colonoscopy.

Authors:  Hiroyuki Yoshida; Yin Wu; Wenli Cai
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2014-03-19
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