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 made a number of advances over the past year. We showed that pericolonic visceral fat is a more useful predictive risk factor for colonic polyps compared with the more routinely used pan-abdominal visceral fat measurements. 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 #
1ZIACL040003-13
Application #
9154104
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
13
Fiscal Year
2015
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