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.

Follow our new articles automatically by clicking Follow new articles in this web and providing your email address.

New Papers:

  1. “Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages”, Scientific Reports, 2018. [Dong Nie, Junfeng Lu, Han Zhang, Ehsan Adeli, Jun Wang, Zhengda Yu, LuYan Liu, Qian Wang*, Jinsong Wu*, and Dinggang Shen*]  * Corresponding authors
  2. “Automatic Brain Labeling via Multi-Atlas Guided Fully Convolutional Networks”, Medical Image Analysis, 2018.  [Longwei Fang, Lichi Zhang, Dong Nie, Xiaohuan Cao, Islem Rekik, Seong-Whan Lee, Huiguang He, Dinggang Shen]
  3. “Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment from Hand Radiograph”, IEEE Journal of Biomedical and Health Informatics, 2018. [Xuhua Ren, Tingting Li, Xiujun Yang, Shuai Wang, Sahar Ahmad, Lei Xiang, Shaun Richard Stone, Lihong Li, Yiqiang Zhan, Dinggang Shen*, Qian Wang*]  * Corresponding authors
  4. “First-Year Development of Modules and Hubs in Infant Brain Functional Networks”, Neuroimage, 2018. [Xuyun Wen, Han Zhang, Gang Li, Mingxia Liu, Weiyan Yin, Weili Lin, Jun Zhang, Dinggang Shen]
  5. “Effective Feature Learning and Fusion of Multimodality Data using Stage-wise Deep Neural Network for Dementia Diagnosis”, Human Brain Mapping, 2018. [Tao Zhou, Kim-Han Thung, Xiaofeng Zhu, Dinggang Shen]
  6. “Infant Brain Development Prediction with Latent Partial Multi-View Representation Learning”, IEEE Transactions on Medical Imaging, 2018. [Changqing Zhang, Ehsan Adeli, Zhengwang Wu, Gang Li, Weili Lin, Dinggang Shen]
  7. “Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis”, Human Brain Mapping, 2018. [Huifang Huang, Xingdan Liu, Yan Jin, Seong-Whan Lee, Chong-Yaw Wee, Dinggang Shen]
  8. “Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer’s Disease Diagnosis”, IEEE Transactions on Biomedical Engineering, 2018. [Mingxia Liu, Jun Zhang, Ehsan Adeli, Dinggang Shen]