Our lab conducts research in image analysis, statistical shape modeling, and machine learning to discover the underlying mechanisms of diseases. In particular, we develop novel methods for image registration, image segmentation, and population-based geometric shape analysis. Our research has potential applications in noninvasive disease diagnosis, screening, and treatment.
FLASH: Fast Image Registration
FLASH (Fourier-approximated Lie algebras for shooting) is a ultrafast implementation of LDDMM (large deformation diffeomorphic metric mapping) with geodesic shooting algorithm for image registration. Our algorithm dramatically speeds up the state-of-art registration methods with little to no loss of accuracy.
Code is publicly distributed on https://bitbucket.org/FlashC.
Registration Uncertainty Quantification
We develop efficient algorithms to quantify the uncertainty of registration results. This is critical to fair assessment on the final estimated transformations and subsequent improvement on the accuracy of predictive registration models. Our algorithm improves the reliability of registration in clinical applications, e.g., real-time image guided navigation system for neurosurgery.