Senior Investigator and Staff Radiologist
Chief, Clinical Image Processing Service
Chief, Imaging Biomarkers and Computer-Aided Diagnosis Laboratory
Specialty: Magnetic Resonance Imaging (MRI) and Computer Tomography (CT) Image Processing
- Virtual Colonoscopy
- Multiorgan multi-atlas image registration for abdominal CT
- Computer aided detection of abnormalities on abdominal CT
Education & Training
Degree / Year
MD & PhD, 1988
Institution and Field
University of Pennsylvania, Physics
University of Pennsylvania
Radiology Residency, University of Michigan
MRI Fellowship, Duke University
Please note: This software is provided as is and without warranty or guarantee of any kind. It is for research purposes only and not to be used for patient care. While we are interested in learning about bugs in the software, no software support of any kind will be provided. If it is used in research, a citation to the publication is required in any publications or presentations.
Persons with disabilities or using assistive technology may find some images which are not fully accessible. For assistance, contact us by sending an e-mail message to Dr. Tejas Mathai and identify documents/pages for which access is required. We will provide assistance in accessing the content of these files for those who need them. You may also find helpful information on Electronic Accessibility at the NIH Clinical Center.
Additional software may be downloaded at: https://github.com/rsummers11/CADLab
Annotated lymph node CT data: 90 mediastinal and 86 abdominal CT cases with annotated lymph node locations can be downloaded at: https://wiki.cancerimagingarchive.net/display/Public/CT+Lymph+Nodes
Annotated pancreas CT data: 82 abdominal contrast enhanced 3D CT scans cases with manual segmentations of the pancreas can be downloaded at: https://wiki.cancerimagingarchive.net/display/Public/Pancreas-CT
Chest radiograph dataset comprising 112,120 frontal-view X-ray images of 30,805 unique patients annotated with 14 text-mined disease labels can be downloaded at: https://nihcc.app.box.com/v/ChestXray-NIHCC
- Deep Lesion dataset comprising 32,000 annotated lesions identified on CT images of 4,400 unique patients with annotations that include the exact location and size of each lesion can be downloaded at: https://nihcc.box.com/v/DeepLesion.