—— 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. “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]
  2. “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]
  3. “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]
  4. “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]
  5. “CT Male Pelvic Organ Segmentation via Hybrid Loss Network with Incomplete Annotation”, IEEE Transactions on Medical Imaging, 2019. [Shuai Wang, Dong Nie, Liangqiong Qu, Yeqin Shao, Jun Lian, Qian Wang, Dinggang Shen]
  6. “Individual Identification and Individual Variability Analysis Based on Cortical Folding Features in Developing Infant Singletons and Twins”, Human Brain Mapping, 2020. [Dingna Duan, Shunren Xia, Islem Rekik, Zhengwang Wu, Li Wang, Weili Lin, John H Gilmore, Dinggang Shen, Gang Li]
  7. “Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients”, IEEE Transactions on Medical Imaging, 2020. [Zhenyu Tang, Yuyun Xu, Lei Jin, Abudumijiti Aibaidula, Junfeng Lu, Zhicheng Jiao, Jinsong Wu, Han Zhang, Dinggang Shen]
  8. “Multi-modal Latent Space Inducing Ensemble SVM Classifier for Early Dementia Diagnosis with Neuroimaging Data”, Medical Image Analysis, 2019. [Tao Zhou, Kim-Han Thung, Mingxia Liu, Feng Shi, Changqing Zhang, Dinggang Shen]
  9. “Deep Morphological Simplification Network (MS-Net) for Guided Registration of Brain Magnetic Resonance Images”, Pattern Recognition, 2019. [Dongming Wei, Lichi Zhang, Zhengwang Wu, Gang Li, Xiaohuan Cao, Dinggang Shen, Qian Wang]
  10. “Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network”. IEEE Transactions on Biomedical Engineering. [Mingliang Wang, Chunfeng Lian, Dongren Yao, Daoqiang Zhang*, Mingxia Liu*, Dinggang Shen*] * Co-corresponding authors
  11. “Sub-Millimeter MR Fingerprinting using Deep-Learning-Based Tissue Quantification”, Magnetic Resonance in Medicine, 2019. [Zhenghan Fang, Yong Chen, Sheng-Che Hung, XiaoXia Zhang, Weili Lin, Dinggang Shen]
  12. “Context-Guided Fully Convolutional Networks for Joint Craniomaxillofacial Bone Segmentation and Landmark Digitization”, Medical Image Analysis, 2019. [Jun Zhang, Mingxia Liu, Li Wang, Si Chen, Peng Yuan, Jianfu Li, Steve Guo-Fang Shen, Zhen Tang, Ken-Chung Chen, James J. Xia, Dinggang Shen]
  13. “Cascaded Multi-Task 3D Fully Convolutional Networks for Pancreas Segmentation”, IEEE Transactions on Cybernetics, 2019. [Jie Xue, Kelei He, Dong Nie, Ehsan Adeli, Zhenshan Shi, Seong-Whan Lee, Yuanjie Zheng, Xiyu Liu, Dengwang Li, Dinggang Shen]
  14. “FCN Based Label Correction for Multi-Atlas Guided Organ Segmentation”, Neuroinformatics, 2019. [Hancan Zhu, Ehsan Adeli, Feng Shi, Dinggang Shen]
  15. “Large-scale Dynamic Causal Modeling of Major Depressive Disorder based on Resting-state fMRI”, Human Brain Mapping, 2019. [Guoshi Li, Yujie Liu, Yanting Zheng, Danian Li, Xinyu Liang, Yaoping Chen, Ying Cui, Pew-Thian Yap, Shijun Qiu, Han Zhang, Dinggang Shen]
  16. “Population-Guided Large Margin Classifier for High-Dimension Low-Sample-Size Problems”, Pattern Recognition, 2019. [Qingbo Yin, Ehsan Adeli, Liran Shen, Dinggang Shen]
  17. “Adversarial Learning for Mono- or Multi-Modal Registration”, Medical Image Analysis, 2019. [Jingfan Fan, Xiaohuan Cao, Qian Wang, Pew-Thian Yap, Dinggang Shen]
  18. “One-Shot Generative Adversarial Learning for MRI Segmentation of Craniomaxillofacial Bony Structures”, IEEE Transactions on Medical Imaging, 2019. [Xu Chen, Chunfeng Lian, Li Wang, Hannah Deng, Steve H. Fung, Dong Nie, Kim-Han Thung, Pew-Thian Yap, Jaime Gateno, James J. Xia*, Dinggang Shen*]  * Co-corresponding authors
  19. “Mitigating Gyral Bias in Cortical Tractography via Asymmetric Fiber Orientation Distributions”, Medical Image Analysis, 2019. [Ye Wu, Yoonmi Hong, Yuanjing Feng*, Dinggang Shen*, Pew-Thian Yap*] *Co-corresponding authors
  20. “Fast Groupwise Registration Using Multi-Level and Multi-Resolution Graph Shrinkage”, Scientific Reports, 2019. [Pei Dong, Xiaohuan Cao, Pew-Thian Yap, Dinggang Shen]
  21. “Surface-Constrained Volumetric Registration for the Early Developing Brain”, Medical Image Analysis, 2019. [Sahar Ahmad, Zhengwang Wu, Gang Li, Li Wang, Weili Lin, Pew-Thian Yap*, Dinggang Shen*] *Co-corresponding authors
  22. “Identifying Autism Spectrum Disorder with Multi-Site fMRI via Low-Rank Domain Adaptation”, IEEE Transactions on Medical Imaging, 2019. [Mingliang Wang, Daoqiang Zhang*, Jiashuang Huang, Pew-Thian Yap, Dinggang Shen*, Mingxia Liu*] * Co-corresponding authors
  23. “Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection”, IEEE Transactions on Medical Imaging, 2019. [Tae-Eui Kam, Han Zhang, Zhicheng Jiao, Dinggang Shen]
  24. “Developmental Topography of Cortical Thickness during Infancy”, PNAS, 2019. [Fan Wang, Chunfeng Lian, Zhengwang Wu, Han Zhang, Tengfei Li, Yu Meng, Li Wang, Weili Lin, Dinggang Shen*, Gang Li*]  *Co-corresponding authors
  25. “XQ-SR: Joint x-q Space Super-Resolution with Application to Infant Diffusion MRI”, Medical Image Analysis, 2019. [Geng Chen, Bin Dong, Yong Zhang, Weili Lin, Dinggang Shen, Pew-Thian Yap]