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. “Large-scale Dynamic Causal Modeling of Major Depressive Disorder based on Resting-state fMRI”, Human Brain Mapping, 2019. [Guoshi Li, Yujie Liu, Yanting Zheng, Danian Li, Xinyu Liang, Yaoping Chen, Ying Cui, Pew-Thian Yap, Shijun Qiu, Han Zhang, Dinggang Shen]
  2. “Population-Guided Large Margin Classifier for High-Dimension Low-Sample-Size Problems”, Pattern Recognition, 2019. [Qingbo Yin, Ehsan Adeli, Liran Shen, Dinggang Shen]
  3. “Adversarial Learning for Mono- or Multi-Modal Registration”, Medical Image Analysis, 2019. [Jingfan Fan, Xiaohuan Cao, Qian Wang, Pew-Thian Yap, Dinggang Shen]
  4. “One-Shot Generative Adversarial Learning for MRI Segmentation of Craniomaxillofacial Bony Structures”, IEEE Transactions on Medical Imaging, 2019. [Xu Chen, Chunfeng Lian, Li Wang, Hannah Deng, Steve H. Fung, Dong Nie, Kim-Han Thung, Pew-Thian Yap, Jaime Gateno, James J. Xia*, Dinggang Shen*]  * Co-corresponding authors
  5. “Mitigating Gyral Bias in Cortical Tractography via Asymmetric Fiber Orientation Distributions”, Medical Image Analysis, 2019. [Ye Wu, Yoonmi Hong, Yuanjing Feng*, Dinggang Shen*, Pew-Thian Yap*] *Co-corresponding authors
  6. “Fast Groupwise Registration Using Multi-Level and Multi-Resolution Graph Shrinkage”, Scientific Reports, 2019. [Pei Dong, Xiaohuan Cao, Pew-Thian Yap, Dinggang Shen]
  7. “Surface-Constrained Volumetric Registration for the Early Developing Brain”, Medical Image Analysis, 2019. [Sahar Ahmad, Zhengwang Wu, Gang Li, Li Wang, Weili Lin, Pew-Thian Yap*, Dinggang Shen*] *Co-corresponding authors
  8. “Identifying Autism Spectrum Disorder with Multi-Site fMRI via Low-Rank Domain Adaptation”, IEEE Transactions on Medical Imaging, 2019. [Mingliang Wang, Daoqiang Zhang*, Jiashuang Huang, Pew-Thian Yap, Dinggang Shen*, Mingxia Liu*] * Co-corresponding authors
  9. “Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection”, IEEE Transactions on Medical Imaging, 2019. [Tae-Eui Kam, Han Zhang, Zhicheng Jiao, Dinggang Shen]
  10. “Developmental Topography of Cortical Thickness during Infancy”, PNAS, 2019. [Fan Wang, Chunfeng Lian, Zhengwang Wu, Han Zhang, Tengfei Li, Yu Meng, Li Wang, Weili Lin, Dinggang Shen*, Gang Li*]  *Co-corresponding authors
  11. “XQ-SR: Joint x-q Space Super-Resolution with Application to Infant Diffusion MRI”, Medical Image Analysis, 2019. [Geng Chen, Bin Dong, Yong Zhang, Weili Lin, Dinggang Shen, Pew-Thian Yap]