—— 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. “Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks”, IEEE Trans. Image Processing, 2020. [Siyuan Liu, Kim-Han Thung, Weili Lin, Pew-Thian Yap, Dinggang Shen]
  2. “Development of Dynamic Functional Architecture during Early Infancy”, Cerebral Cortex, 2020. [Xuyun Wen, Rifeng Wang, Weiyan Yin, Weili Lin, Han Zhang*, Dinggang Shen*]
  3. “Estimating Reference Shape Model for Personalized Surgical Reconstruction of Craniomaxillofacial Defects”, IEEE Transactions on Biomedical Engineering, 2020. [Deqiang Xiao, Chunfeng Lian, Li Wang, Hannah Deng, Kim-Han Thung, Jihua Zhu, Peng Yuan, Leonel Perez, Jr. Jaime Gateno, Pew-Thian Yap, James J. Xia*, Dinggang Shen*]
  4. “Designing Weighted Correlation Kernels in Convolutional Neural Networks for Functional Connectivity based Brain Disease Diagnosis”, Medical Image Analysis, 2020. [Biao Jie, Mingxia Liu, Chunfeng Lian, Feng Shi, Dinggang Shen]
  5. “Multi-Class ASD Classification Based on Functional Connectivity and Functional Correlation Tensor via Multi-Source Domain Adaptation and Multi-View Sparse Representation”, IEEE Transactions on Medical Imaging, 2020. [Jun Wang, Lichi Zhang, Qian Wang, Lei Chen, Jun Shi, Xiaobo Chen, Zuoyong Li, Dinggang Shen]
  6. “Spatially-Constrained Fisher Representation for Brain Disease Identification with Incomplete Multi-Modal Neuroimages”, IEEE Transactions on Medical Imaging, 2020. [Yongsheng Pan, Mingxia Liu, Chunfeng Lian, Yong Xia, Dinggang Shen]
  7. “Adversarial Confidence Learning for Medical Image Segmentation and Synthesis”, International Journal of Computer Vision, 2020. [Dong Nie, Dinggang Shen]
  8. “Brain Network Construction and Classification Toolbox (BrainNetClass)”, Human Brain Mapping, 2020. [Zhen Zhou, Xiaobo Chen, Yu Zhang, Dan Hu, Lishan Qiao, Renping Yu, Pew-Thian Yap, Gang Pan, Han Zhang, Dinggang Shen]
  9. “Multi-view Spatial Aggregation Framework for Joint Localization and Segmentation of Organs at risk in Head and Neck CT Images”, IEEE Transactions on Medical Imaging, 2020. [Shujun Liang, Kim-Han Thung, Dong Nie, Yu Zhang, Dinggang Shen]
  10. “Deep Multi-Scale Mesh Feature Learning for Automated Labeling of Raw Dental Surfaces from 3D Intraoral Scanners”, IEEE Transactions on Medical Imaging, 2020. [Chunfeng Lian, Li Wang, Tai-Hsien Wu, Fan Wang, Pew-Thian Yap, Ching-Chang Ko, Dinggang Shen]
  11. “Synthesized 7T MRI from 3T MRI via Deep Learning in Spatial and Wavelet Domains”, Medical Image Analysis, 2020. [Liangqiong Qu, Yongqin Zhang, Shuai Wang, Pew-Thian Yap, Dinggang Shen]
  12. “Iterative Label Denoising Network: Segmenting Male Pelvic Organs in CT from 3D Bounding Box Annotations”, IEEE Transactions on Biomedical Engineering, 2020. [Shuai Wang, Qian Wang, Yeqin Shao, Liangqiong Qu, Chunfeng Lian, Jun Lian, Dinggang Shen]