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.
- "Large Deformation Diffeomorphic Registration of Diffusion-Weighted Imaging Data”, accepted for Medical Image Analysis, 2014. [Pei Zhang, Marc Niethammer, Dinggang Shen, Pew-Thian Yap]
- “Subclass-based Multi-task Learning for Alzheimer's Disease Diagnosis”, Frontiers in Aging Neuroscience, 2014. [Heung-Il Suk, Seong-Whan Lee, Dinggang Shen]
- "Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis", accepted for NeuroImage, 2014. [Heung-Il Suk, Seong-Whan Lee, Dinggang Shen]
- "Simultaneous and Consistent Labeling of Longitudinal Dynamic Developing Cortical Surfaces in Infants”, accepted for Medical Image Analysis, 2014. [Gang Li, Li Wang, Feng Shi, Weili Lin, Dinggang Shen]
- "Spatial Distribution and Longitudinal Development of Deep Cortical Sulcal Landmarks in Infants”, accepted for Neuroimage, 2014. [Yu Meng, Gang Li, Weili Lin, John H Gilmore, Dinggang Shen]
- "Deformable Segmentation of 3-D MR Prostate Images via Distributed Discriminative Dictionary and Ensemble Learning”, accepted for Medical Physics, 2014. [Yanrong Guo, Yaozong Gao, Yeqin Shao, True Price, Aytekin Oto, Dinggang Shen]
“Subclass-based Multi-task Learning for Alzheimer's Disease Diagnosis”, Frontiers in Aging Neuroscience, 2014. [Heung-Il Suk, Seong-Whan Lee, Dinggang Shen]