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. “3D Fully Convolutional Networks for Multi-Modal Isointense Infant Brain Image Segmentation”, IEEE Transactions on Cybernetics, 2018. [Dong Nie, Li Wang, Ehsan Adeli, Cuijing Lao, Weili Lin, Dinggang Shen]
  2. Conversion and Time-to-Conversion Predictions of Mild Cognitive Impairment using Low-Rank Affinity Pursuit Denoising and Matrix Completion, Medical Image Analysis, 2018. [Kim-Han Thung, Pew-Thian Yap, Ehsan Adeli, Seong-Whan Lee, Dinggang Shen]
  3. “Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises”, IEEE Trans. on Pattern Analysis and Machine Intelligence, 2018. [Ehsan Adeli, Kim-Han Thung, Le An, Guorong Wu, Feng Shi, Tao Wang, Dinggang Shen]
  4. "Anatomical Landmark based Deep Feature Representation for MR Images in Brain Disease Diagnosis", IEEE Journal of Biomedical and Health Informatics, 2018. [Mingxia Liu, Jun Zhang, Dong Nie, Pew-Thian Yap, Dinggang Shen]
  5. "Interleaved 3D-CNNs for Joint Segmentation of Small-Volume Structures in Head and Neck CT Images", Medical Physics, 2018. [Xuhua Ren, Lei Xiang, Dong Nie, Yeqin Shao, Dinggang Shen*, Qian Wang*]  * Co-Corresponding authors.