Abstract

We are improving CT colonography (virtual colonoscopy) by developing computer-assisted diagnosis methods. These methods attempt to identify and characterize colonic polyps automatically, thereby increasing physician accuracy and efficiency and helping patients by finding their polyps. We are developing methods to detect extracolonic findings on CT colonography using fully-automated software. We improved the accuracy of computer-aided polyp detection using recent advances in machine learning.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
Clinical Center (CLC)
Type
Investigator-Initiated Intramural Research Projects (ZIA)
Project #
1ZIACL040003-14
Application #
9352016
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
14
Fiscal Year
2016
Total Cost
Indirect Cost

  Institution

Name
Clinical Center
Department
Type
DUNS #
City
State
Country
Zip Code

  Publications

Lee, Scott J; Liu, Jiamin; Yao, Jianhua et al. (2018) Fully automated segmentation and quantification of visceral and subcutaneous fat at abdominal CT: application to a longitudinal adult screening cohort. Br J Radiol 91:20170968
Summers, Ronald M (2016) Progress in Fully Automated Abdominal CT Interpretation. AJR Am J Roentgenol 207:67-79
Roth, Holger R; Lu, Le; Liu, Jiamin et al. (2016) Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation. IEEE Trans Med Imaging 35:1170-81
Wang, Shijun; Li, Diana; Petrick, Nicholas et al. (2015) Optimizing area under the ROC curve using semi-supervised learning. Pattern Recognit 48:276-287
Cherry, Kevin M; Peplinski, Brandon; Kim, Lauren et al. (2015) Sequential Monte Carlo tracking of the marginal artery by multiple cue fusion and random forest regression. Med Image Anal 19:164-75
Liu, Jiamin; Pattanaik, Sanket; Yao, Jianhua et al. (2015) Associations among pericolonic fat, visceral fat, and colorectal polyps on CT colonography. Obesity (Silver Spring) 23:408-14
Liu, Jianfei; Wang, Shijun; Linguraru, Marius George et al. (2015) Computer-aided detection of exophytic renal lesions on non-contrast CT images. Med Image Anal 19:15-29
Ryckman, Eva M; Summers, Ronald M; Liu, Jiamin et al. (2015) Visceral fat quantification in asymptomatic adults using abdominal CT: is it predictive of future cardiac events? Abdom Imaging 40:222-6
Wei, Zhuoshi; Yao, Jianhua; Wang, Shijun et al. (2014) Feasibility of using the marginal blood vessels as reference landmarks for CT colonography. AJR Am J Roentgenol 202:W50-8
Liu, Jianfei; Wang, Shijun; Yao, Jianhua et al. (2013) Manifold diffusion for exophytic kidney lesion detection on non-contrast CT images. Med Image Comput Comput Assist Interv 16:340-7

Showing the most recent 10 out of 41 publications