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. “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]
  2. “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]
  3. “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]
  4. “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]
  5. “BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks”, Medical Image Analysis, 2019. [Jingfan Fan, Xiaohuan Cao, Pew-Thian Yap, Dinggang Shen]
  6. “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]
  7. “Foreground Fisher Vector: Encoding Class-Relevant Foreground to Improve Image Classification”, IEEE Transactions on Image Processing, 2019. [Yongsheng Pan, Yong Xia, Dinggang Shen]
  8. “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]
  9. “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]