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.
- "Deep Ensemble Learning of Sparse Regression Models for Brain Disease Diagnosis”, Medical Image Analysis, 2017. [Heung-Il Suk, Seong-Whan Lee, Dinggang Shen]
- "Associations between Tumor Vascularity, Vascular Endothelial Growth Factor Expression and PET/MRI Radiomic Signatures in Primary Clear-Cell–Renal-Cell-Carcinoma: Proof-of-Concept Study”, Scientific Reports, 2017. [Qingbo YIN, Sheng-Che Hung, Li Wang, Weili Lin, Julia R. Fielding, W. Kimryn Rathmell, Amir H. Khandani, Michael E. Woods, Matthew I. Milowsky, Samira A. Brooks, Eric. M. Wallen, Dinggang Shen]
- "Reduced White Matter Integrity in Antisocial Personality Disorder: A Diffusion Tensor Imaging Study”, Scientific Reports, 2017. [Weixiong Jiang*, Feng Shi*, Huasheng Liu, Gang Li, Zhongxiang Ding, Hui Shen, Celina Shen, Seong-Whan Lee, Dewen Hu, Wei Wang, Dinggang Shen] *Co-first authors.
- "Connectivity Strength-Weighted Sparse Group Representation-based Brain Network Construction for MCI Classification”, Human Brain Mapping, 2017. [Renping Yu, Han Zhang, Le An, Xiaobo Chen, Zhihui Wei, Dinggang Shen]
- "MRI Based Prostate Cancer Detection with High-level Representation and Hierarchical Classification”, Medical Physics, 2016. [Yulian Zhu, Li Wang, Mingxia Liu, Chunjun Qian, Ambereen Yousuf, Aytekin Oto, Dinggang Shen]