UNC IDEA Group

------ Image Display, Enhancement, and Analysis (IDEA) Group

UNC IDEA group consists of the IDEA Lab in the Department of Radiology and the Image Analysis Core Lab in the Biomedical Research Imaging Center (BRIC). The IDEA lab is devoted to the development of novel image analysis methods and tools, and their applications to various clinical research and trials. The developed methods include deformable registration (HAMMER), deformable segmentation (AFDM), and multivariate pattern classification algorithms. These methods have been applied to various studies on brain diseases and development (including MCI, AD, Schizophrenia, and Neonate Development Study), heart, breast cancer, and prostate cancer. The image analysis core in BRIC supports the image storage and analysis needs of scientists in UNC. It also provides services for brain structural and functional analysis, small animal imaging analysis, visualization, and others.

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New Papers:

  1. “Discovering Cortical Sulcal Folding Patterns in Neonates Using Large-scale Dataset”, Human Brain Mapping, 2018. [Yu Meng, Gang Li, Li Wang, Weili Lin, John H Gilmore, Dinggang Shen]
  2. “Diagnosis of Autism Spectrum Disorders Using Multi-level High-order Functional Networks Derived from Resting-State Functional MRI”, Frontiers in Human Neuroscience, 2018. [Feng Zhao, Han Zhang, Islem Rekik, Zhiyong An, Dinggang Shen]
  3. “Integrative Radiomics Expression Predicts Molecular Subtypes of Primary Clear Cell Renal Cell Carcinoma”, Clinical Radiology, 2018. [Qingbo Yin, Sheng-Che Hung, W. Kimryn Rathmell, Liran Shen, Li Wang, Weili Lin, Julia R. Fielding, Amir H. Khandani, Michael E. Woods, Matthew I. Milowsky, Samira A. Brooks, Eric. M. Wallen, Dinggang Shen]
  4. “Multi-Atlas Segmentation of MR Tumor Brain Images Using Low-Rank Based Image Recovery”, IEEE Transactions on Medical Imaging, 2018. [Zhenyu Tang, Sahar Ahmad, Pew-Thian Yap, Dinggang Shen]
  5. “Brain-wide Genome-wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model”, IEEE Transactions on Biomedical Engineering, 2018. [Tao Zhou, Kim-Han Thung, Mingxia Liu, Dinggang Shen]