Abstract

The purpose of this project is to develop computer-aided diagnosis/detection (CAD) for a wide variety of radiologic images and disease types. This project uses existing NIH radiology images. We are developing techniques for segmentation of abdominal CT images to accurately locate the boundaries of the major abdominal organs such as the liver, spleen, adrenal glands, kidneys and pancreas. We made further progress on this project, providing accurate localization and measurement of the pancreas. We made further progress on a project to develop computer-aided detection of prostate cancer on endorectal coil MRI scans. We are developing convolutional neural networks based methods (deep learning) on big data to train computers to detect diseases on radiology images like X-Ray, CT and MRI scans.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
Clinical Center (CLC)
Type
Investigator-Initiated Intramural Research Projects (ZIA)
Project #
1ZIACL040004-16
Application #
9776056
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
16
Fiscal Year
2018
Total Cost
Indirect Cost

  Institution

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

  Publications

Summers, Ronald M (2018) Deep Learning Lends a Hand to Pediatric Radiology. Radiology 287:323-325
Greer, Matthew D; Lay, Nathan; Shih, Joanna H et al. (2018) Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: an international multi-reader study. Eur Radiol 28:4407-4417
Roth, Holger R; Lu, Le; Lay, Nathan et al. (2018) Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation. Med Image Anal 45:94-107
Summers, Ronald M (2018) Are we at a crossroads or a plateau? Radiomics and machine learning in abdominal oncology imaging. Abdom Radiol (NY) :
Zhang, Ling; Lu, Le; Summers, Ronald M et al. (2018) Convolutional Invasion and Expansion Networks for Tumor Growth Prediction. IEEE Trans Med Imaging 37:638-648
Gao, Mingchen; Bagci, Ulas; Lu, Le et al. (2018) Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput Methods Biomech Biomed Eng Imaging Vis 6:1-6
Peng, Yifan; Wang, Xiaosong; Lu, Le et al. (2018) NegBio: a high-performance tool for negation and uncertainty detection in radiology reports. AMIA Jt Summits Transl Sci Proc 2017:188-196
Yan, Ke; Wang, Xiaosong; Lu, Le et al. (2018) DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J Med Imaging (Bellingham) 5:036501
Summers, Ronald M (2017) Texture analysis in radiology: Does the emperor have no clothes? Abdom Radiol (NY) 42:342-345
Greer, Matthew D; Shih, Joanna H; Lay, Nathan et al. (2017) Validation of the Dominant Sequence Paradigm and Role of Dynamic Contrast-enhanced Imaging in PI-RADS Version 2. Radiology 285:859-869

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