Research and Publications

Medical Image Computing (MIC)

The major research goal of MIC is to extract clinically relevant information or knowledge from medical images. We have expertise and strong interests in applying advanced machine learning, deep learning, shape modeling, spectral graph theory, etc, to study anatomical structures of human organs, and analyze brain connectivity patterns in various population groups.

Hippocampus in the brain.
  • Kevin H. Hobbs, Pin Zhang, Bibo Shi, Charles D. Smith and Jundong Liu: Quad-mesh based radial distance biomarkers for Alzheimer's Disease, ISBI 2016: 19-23 pdf.
  • Chen, Y., Shi, B., Zhang, P., Smith, C., Wang, Z., Liu, J. (2017). Hippocampus Segmentation through Multi-view Ensemble ConvNets, 2017 IEEE International Symposium on Biomedical Imaging (ISBI).

Machine Learning and Deep Learning

U-Net Architecture.
  • Bibo Shi, Yani Chen, Kevin Hobbs, Charles D. Smith and Jundong Liu (2015). Nonlinear Metric Laerning for Alzheimer's Disease Diagnosis with Integration of Longitudinal Neuroimaging Features, BMVC 2015: 138.1-138.13, [pdf].

Shape Modeling

Computer Vision