—— 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. “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]
  2. “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
  3. “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]
  4. “Mapping Hemispheric Asymmetries of the Macaque Cerebral Cortex during Early Brain Development”, Human Brain Mapping, 2019. [Jing Xia, Fan Wang, Zhengwang Wu, Li Wang, Caiming Zhang, Dinggang Shen, Gang Li]
  5. “Treatment-Naïve First Episode Depression Classification Based on High-order Brain Functional Network”, Journal of Affective Disorders, 2019. [Yanting Zheng, Xiaobo Chen, Danian Li, Yujie Liu, Xin Tan, Yi Liang, Han Zhang, Shijun Qiu, Dinggang Shen]
  6. “Dual-Domain Convolutional Neural Networks for Improving Structural Information in 3T MRI”, Magnetic Resonance Imaging, 2019. [Yongqin Zhang, Pew-Thian Yap, Liangqiong Qu, Jie-Zhi Cheng, Dinggang Shen]
  7. “Topological Correction of Infant White Matter Surfaces Using Anatomically Constrained Convolutional Neural Network”, NeuroImage, 2019. [Liang Sun, Daoqiang Zhang, Chunfeng Lian, Li Wang, Zhengwang Wu, Wei Shao, Weili Lin, Dinggang Shen, Gang Li]
  8. “Construction of 4D Infant Cortical Surface Atlases with Sharp Folding Patterns via Spherical Patch-based Group-wise Sparse Representation”, Human Brain Mapping, 2019. [Zhengwang Wu, Li Wang, Weili Lin, John H. Gilmore, Gang Li*, Dinggang Shen*] *Co-corresponding authors
  9. “Learning longitudinal classification-regression model for infant hippocampus segmentation”,Neurocomputing, 2019. [Yanrong Guo, Zhengwang Wu, Dinggang Shen]
  10. “Deep Feature Descriptor Based Hierarchical Dense Matching for X-ray Angiographic Images”, Computer Methods and Programs in Biomedicine, April 209. [Jingfan Fan, Jian Yang, Yachen Wang, SiyuanYang, Danni Ai, Yong Huang, Hong Song, Yongtian Wang, Dinggang Shen]
  11. “Fetal Cortical Surface Atlas Parcellation Based on Growth Patterns”, Human Brain Mapping, 2019. [Jing Xia, Fan Wang, Oualid M. Benkarim, Gerard Sanroma, Gemma Piella, Miguel A. González Ballester, Nadine Hahner, Elisenda Eixarch, Caiming Zhang, Dinggang Shen, Gang Li]
  12. “Denoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space”, IEEE Transactions on Medical Imaging, 2019. [Geng Chen, Bin Dong, Yong Zhang, Dinggang Shen, Pew-Thian  Yap]
  13. “Deep Learning Deformation Initialization for Rapid Groupwise Registration of Inhomogeneous Image Populations”, Frontiers in Neuroinformatics, 2019. [Sahar Ahmad, Jingfan Fan, Pei Dong, Xiaohuan Cao, Pew-Thian Yap, Dinggang Shen]
  14. “Latent Representation Learning for Alzheimer’s Disease Diagnosis with Incomplete Multi-modality Neuroimaging and Genetic Data”, IEEE Transactions on Medical Imaging, 2019. [Tao Zhou, Mingxia Liu, Kim-Han Thung, Dinggang Shen]
  15. “Super-Resolution Reconstruction of Neonatal Brain Magnetic Resonance Images via Residual Structured Sparse Representation”, Medical Image Analysis, 2019. [Yongqin Zhang, Pew-Thian Yap, Geng Chen, Weili Lin, Li Wang, Dinggang Shen]
  16. “Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks”, IEEE Transactions on Medical Imaging, 2019. [Yoonmi Hong, Jaeil Kim, Geng Chen, Weili Lin, Pew-Thian Yap, Dinggang Shen]
  17. “Dilated Dense U-net for Infant Hippocampus Subfield Segmentation”, Frontiers in Neuroinformatics, 2019. [Hancan Zhu, Feng Shi, Li Wang, Sheng-Che Hung, Meng-Hsiang Chen, Shuai Wang, Weili Lin,Dinggang Shen]
  18. “BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks”, Medical Image Analysis, 2019. [Jingfan Fan, Xiaohuan Cao, Pew-Thian Yap, Dinggang Shen]
  19. “CT Male Pelvic Organ Segmentation Using Fully Convolutional Networks with Boundary Sensitive Representation”, Medical Image Analysis, 2019. [Shuai Wang, Kelei He, Dong Nie, Sihang Zhou, Yaozong Gao, Dinggang Shen]
  20. “Foreground Fisher Vector: Encoding Class-Relevant Foreground to Improve Image Classification”, IEEE Transactions on Image Processing, 2019. [Yongsheng Pan, Yong Xia, Dinggang Shen]
  21. “Weakly-supervised Deep Learning for Brain Disease Prognosis using MRI and Incomplete Clinical Scores”, IEEE Transactions on Cybernetics, 2019. [Mingxia Liu, Jun Zhang, Chunfeng Lian,Dinggang Shen]
  22. “Hippocampal Segmentation from Longitudinal Infant Brain MR Images via Classification-guided Boundary Regression”, IEEE Access, 2019. [Yeqin Shao, Jaeil Kim, Yaozong Gao, Qian Wang, Weili Lin, Dinggang Shen]