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. "Learning Non-Linear Patch Embeddings with Neural Networks for Label Fusion", Medical Image Analysis, 2017. [Gerard Sanroma, Oualid M. Benkarim, Gemma Piella, Oscar Camara, Guorong Wu, Dinggang Shen, Juan D. Gispert, Jos ́e Luis Molinuevo, Miguel A. Gonz ́alez Ballester]
  2. "Robust Brain ROI Segmentation by Deformation Regression and Deformable Shape Model”, Medical Image Analysis, 2017. [Zhengwang Wu, Yanrong Guo, Sang Hyun Park, Yaozong Gao, Pei Dong, Seong-Whan Lee, Dinggang Shen]
  3. "Hierarchical Vertex Regression Based Segmentation of Head and Neck CT Images for Radiotherapy Planning", IEEE Transactions on Image Processing, 2017. [Zhensong Wang, Lifang Wei, Li Wang, Yaozong Gao, Wufan Chen, Dinggang Shen]
  4. "Landmark-based Deep Multi-Instance Learning for Brain Disease Diagnosis", Medical Image Analysis, 2017. [Mingxia Liu, Jun Zhang, Ehsan Adeli, Dinggang Shen]
  5. "Radiation-induced Brain Structural and Functional Abnormalities in Pre-symptomatic Phase and Outcome Prediction", Human Brain Mapping, 2017. [Zhongxiang Ding*, Han Zhang*, Xiaofei Lv, Fei Xie, Lizhi Liu, Shijun Qiu, Li Li, Dinggang Shen]  * Co-corresponding authors