Dinggang Shen

PhD, Radiology, UNC-Chapel Hill, Clinical Research

Dinggang Shen

UNC-Chapel Hill
Clinical Research


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Area of interest

Dr. Shen is a Professor of Radiology, BRIC, Computer Science, and Biomedical Engineering in the University of North Carolina at Chapel Hill (UNC-CH). He is a director for the Image Display, Enhancement, and Analysis (IDEA) Lab in the Department of Radiology, and also a director for the medical image analysis core in the Biomedical Research Imaging Center (BRIC) at UNC-CH. Before joining UNC-CH, he was a tenure-track assistant professor in the University of Pennsylvania (UPenn).

With his colleagues, Dr. Shen has developed many innovative and practical methods for deformable segmentation (AFDM), registration (HAMMER, CLASSIC, ORBIT, RABBIT, TIMER, and ABSORB), and neuroimage classification (COMPARE and STEP). These methods have been applied for diagnosis of brain diseases (e.g., AD, MCI,  schizophrenia, and autism), cardiac disease, prostate cancer, breast cancer and lung cancer, with more than 400 papers published in the international journals and conference proceedings.

Currently, Dr. Shen's group is studying two cancer-related projects, respectively, on prostate cancer and lung cancer. For prostate cancer project, Dr. Shen's group is developing a novel method for online learning of patient-specific appearance and shape deformation information, as a way to significantly improve prostate segmentation in the daily CT images of a patient. Accurate segmentation of the prostate is critical for determining the day-to-day motions of the prostate during the external beam radiation therapy. In particular, with accurate estimation of motions for the prostate, the treatment plan can be adjusted, as is popularly done in adaptive radiation therapy (ART). Also, the best treatment for the disease could be achieved by maximizing the dose to the tumor and minimizing dose to healthy tissue. With successful development of the potentially more accurate segmentation algorithms planed in their project, the effectiveness of radiotherapy for prostate cancer treatment can be highly improved.

For the lung cancer project, Dr. Shen's group is mainly developing super-resolution technique for enhancement of 4D-CT, estimating lung motion with 4D registration, and also estimating full lung motion information from 3D free-breathing CT image acquired on the treatment day.

Awards and Honors

2001 Best paper award, IEEE Workshop on Mathematical Methods in Biomedical Image Analysis
(MMBIA), Kauai, Hawaii, December 9-10, 2001.
2006 Best Paper Award, IEEE Signal Processing Society.
2007 The Most Cited Paper Award for the journal Image and Vision Computing

Link to Publications on Reach NC site

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