Computer Aided Detection for Radiologic Images

Summers, Ronald M.

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 also made progress on automated detection of the thyroid, bone fractures and lymphadenopathy on CT scans. We made further progress on a project to develop computer-aided detection of prostate cancer on endorectal coil MRI scans. We are developing methods to identify frequently missed lesions such as epidural masses. We made progress on a new project that uses convolutional neural networks (deep learning) on big data to train computers to detect diseases on radiology images like CT and MRI scans.

Funding Agency

• Agency: National Institute of Health (NIH)
• Institute: Clinical Center (CLC)
• Type: Investigator-Initiated Intramural Research Projects (ZIA)
• Project #: 1ZIACL040004-14
• Application #: 9352017
• Support Year: 14
• Fiscal Year: 2016

Institution

• Name: Clinical Center

Publications

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 (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

Pattanayak, Puskar; Turkbey, Evrim B; Summers, Ronald M (2017) Comparative Evaluation of Three Software Packages for Liver and Spleen Segmentation and Volumetry. Acad Radiol 24:831-839

Zhang, Ling; Lu, Le; Nogues, Isabella et al. (2017) DeepPap: Deep Convolutional Networks for Cervical Cell Classification. IEEE J Biomed Health Inform :