Nineteen MICCAI papers accepted from IDEA group
19 MICCAI papers accepted from IDEA group
19 MICCAI papers accepted from IDEA group
The paper information is below: “4D Segmentation of Brain MR Images with Constrained Cortical Thickness Variation”, accepted for PLOS ONE, 2013. [Li Wang, Feng Shi, Gang Li, Dinggang Shen]
The paper information is as follows: “Resolution Enhancement of Lung 4D-CT Data Using Multi-Scale Inter-Phase Iterative Non-Local Means”, accepted for Medical Physics 2013. [Yu Zhang, Pew-Thian Yap, Guorong Wu, Qianjin Feng, Jun Lian, Wufan Chen, Dinggang Shen]
The paper information is below: “Altered Modular Organization of Structural Cortical Networks in Children with Autism”, accepted for PLOS ONE, 2013. [Feng Shi, Li Wang, Ziwen Peng, Chong-Yaw Wee, and Dinggang Shen]
The paper information is below: “The Non-Local Bootstrap – Estimation of Uncertainty in Diffusion MRI”, IPMI 2013, Asilomar, California, USA, Jun. 29 – Jul. 3, 2013. [Pew-Thian Yap, Hongyu An, Yasheng Chen, Dinggang Shen] “Exploring High-Order Functional Interactions via Structurally-Weighted LASSO Models”, IPMI 2013, Asilomar, California, USA, Jun. 29 – Jul. 3, 2013. [Dajiang Zhu, … Read more
The paper information is as follows: “Development of cortical anatomical properties from early childhood to early adulthood”, accepted for Neuroimage, 2013. [Jingxin Nie, Gang Li, Dinggang Shen]
The paper information is as follows: “aBEAT: A Toolbox for Consistent Analysis of Longitudinal Adult Brain MRI”, accepted for PLOS ONE, 2013. [Yakang Dai, Yaping Wang, Li Wang, Guorong Wu, Feng Shi, Dinggang Shen]
The paper information is below: “Groupwise Registration via Graph Shrinkage on the Image Manifold”, CVPR, June 25-27, 2013, Oregon, USA. [Shihui Ying, Guorong Wu, Qian Wang, Dinggang Shen] “Prostate Segmentation in CT Images via Spatial-Constrained Transductive Lasso (SCOTO)”, CVPR, June 25-27, 2013, Oregon, USA. [Yinghuan Shi, Shu Liao, Yaozong Gao, Daoqiang Zhang, Yang Gao, and … Read more
The paper information is below: “Semi-supervised Multimodal Relevance Vector Regression Improves Cognitive Performance Estimation from Imaging and Biological Biomarkers”, accepted Neuroinformatics, 2013. [Bo Cheng, Daoqiang Zhang, Songcan Chen, Daniel Kaufer, Dinggang Shen]