Literature DB >> 23256078

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

Kenji Suzuki1.   

Abstract

Medical imaging has been indispensable in medicine since the discovery of x-rays. Medical imaging offers useful information on patients' medical conditions and on the causes of their symptoms and diseases. As imaging technologies advance, a large number of medical images are produced which physicians/radiologists must interpret. Thus, computer aids are demanded and become indispensable in physicians' decision making based on medical images. Consequently, computer-aided detection and diagnosis (CAD) has been investigated and has been an active research area in medical imaging. CAD is defined as detection and/or diagnosis made by a radiologist/physician who takes into account the computer output as a "second opinion". In CAD research, detection and diagnosis of lung and colorectal cancer in thoracic and colonic imaging constitute major areas, because lung and colorectal cancers are the leading and second leading causes, respectively, of cancer deaths in the U.S. and also in other countries. In this review, CAD of the thorax and colon, including CAD for detection and diagnosis of lung nodules in thoracic CT, and that for detection of polyps in CT colonography, are reviewed.

Entities:  

Keywords:  CT colonography; Computer-aided detection; classifier; colorectal polyps; computer-aided diagnosis; lung nodule; machine learning in medical imaging; pixel-based machine learning; screening; thoracic CT

Year:  2012        PMID: 23256078      PMCID: PMC3496503          DOI: 10.3978/j.issn.2223-4292.2012.09.02

Source DB:  PubMed          Journal:  Quant Imaging Med Surg        ISSN: 2223-4306


  108 in total

1.  Computerized detection of pulmonary nodules on CT scans.

Authors:  S G Armato; M L Giger; C J Moran; J T Blackburn; K Doi; H MacMahon
Journal:  Radiographics       Date:  1999 Sep-Oct       Impact factor: 5.333

2.  A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results.

Authors:  M F McNitt-Gray; E M Hart; N Wyckoff; J W Sayre; J G Goldin; D R Aberle
Journal:  Med Phys       Date:  1999-06       Impact factor: 4.071

3.  Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: evaluation with receiver operating characteristic analysis.

Authors:  Yuichi Matsuki; Katsumi Nakamura; Hideyuki Watanabe; Takatoshi Aoki; Hajime Nakata; Shigehiko Katsuragawa; Kunio Doi
Journal:  AJR Am J Roentgenol       Date:  2002-03       Impact factor: 3.959

4.  Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images.

Authors:  Masahito Aoyama; Qiang Li; Shigehiko Katsuragawa; Heber MacMahon; Kunio Doi
Journal:  Med Phys       Date:  2002-05       Impact factor: 4.071

Review 5.  Computer-aided diagnosis for CT colonography.

Authors:  Hiroyuki Yoshida; Abraham H Dachman
Journal:  Semin Ultrasound CT MR       Date:  2004-10       Impact factor: 1.875

6.  Improved classifier for computer-aided polyp detection in CT colonography by nonlinear dimensionality reduction.

Authors:  Shijun Wang; Jianhua Yao; Ronald M Summers
Journal:  Med Phys       Date:  2008-04       Impact factor: 4.071

7.  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

8.  A method of comparing the areas under receiver operating characteristic curves derived from the same cases.

Authors:  J A Hanley; B J McNeil
Journal:  Radiology       Date:  1983-09       Impact factor: 11.105

9.  Massive-training support vector regression and Gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography.

Authors:  Jian-Wu Xu; Kenji Suzuki
Journal:  Med Phys       Date:  2011-04       Impact factor: 4.071

10.  Pixel-based machine learning in medical imaging.

Authors:  Kenji Suzuki
Journal:  Int J Biomed Imaging       Date:  2012-02-28
View more
  16 in total

Review 1.  [Machine learning and multiparametric MRI for early diagnosis of prostate cancer].

Authors:  D Bonekamp; H-P Schlemmer
Journal:  Urologe A       Date:  2021-03-12       Impact factor: 0.639

2.  Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research.

Authors:  Spiros Kostopoulos; Panagiota Ravazoula; Pantelis Asvestas; Ioannis Kalatzis; George Xenogiannopoulos; Dionisis Cavouras; Dimitris Glotsos
Journal:  J Digit Imaging       Date:  2017-06       Impact factor: 4.056

3.  Evidence based imaging strategies for solitary pulmonary nodule.

Authors:  Yi-Xiang J Wang; Jing-Shan Gong; Kenji Suzuki; Sameh K Morcos
Journal:  J Thorac Dis       Date:  2014-07       Impact factor: 2.895

Review 4.  Magnetic resonance imaging for lung cancer screen.

Authors:  Yi-Xiang J Wang; Gladys G Lo; Jing Yuan; Peder E Z Larson; Xiaoliang Zhang
Journal:  J Thorac Dis       Date:  2014-09       Impact factor: 2.895

5.  Quantitative radiology: automated measurement of polyp volume in computed tomography colonography using Hessian matrix-based shape extraction and volume growing.

Authors:  Mark L Epstein; Piotr R Obara; Yisong Chen; Junchi Liu; Amin Zarshenas; Nazanin Makkinejad; Abraham H Dachman; Kenji Suzuki
Journal:  Quant Imaging Med Surg       Date:  2015-10

Review 6.  Deep learning: definition and perspectives for thoracic imaging.

Authors:  Guillaume Chassagnon; Maria Vakalopolou; Nikos Paragios; Marie-Pierre Revel
Journal:  Eur Radiol       Date:  2019-12-06       Impact factor: 5.315

7.  A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm.

Authors:  Liang Liang; Minliang Liu; Caitlin Martin; John A Elefteriades; Wei Sun
Journal:  Biomech Model Mechanobiol       Date:  2017-04-06

8.  Design Optimization of Spatial-Spectral Filters for Cone-Beam CT Material Decomposition.

Authors:  Matthew Tivnan; Wenying Wang; Grace Gang; J Webster Stayman
Journal:  IEEE Trans Med Imaging       Date:  2022-08-31       Impact factor: 11.037

9.  Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer.

Authors:  Wookjin Choi; Jung Hun Oh; Sadegh Riyahi; Chia-Ju Liu; Feng Jiang; Wengen Chen; Charles White; Andreas Rimner; James G Mechalakos; Joseph O Deasy; Wei Lu
Journal:  Med Phys       Date:  2018-03-12       Impact factor: 4.071

Review 10.  Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology.

Authors:  Martina Sollini; Francesco Bartoli; Andrea Marciano; Roberta Zanca; Riemer H J A Slart; Paola A Erba
Journal:  Eur J Hybrid Imaging       Date:  2020-12-09
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.