Ongoing Research

(Saurabh J. Shigwan and Suyash P. Awate):
Over the last few decades, clinical healthcare decisions relating to diagnosis, treatment, and therapy have continued to increasingly rely on noninvasive technologies like medical imaging. Medical imaging technologies evolve over time, leading to newer techniques that interrogate the anatomy to extract more complex information.
This entails advances in algorithms for image processing and visualizing the processed image information.
Medical imaging is also an important scientific tool in collecting structural and functional data to better understand several complex and debilitating disorders,
such as those affecting the brain, heart, or lungs, including cancer. Scientific studies are increasingly relying on imaging and quantitative image analysis in large populations as
a less-subjective, more-reproducible, and more- scalable way of understanding the effects of (i) a disorder or (ii) a treatment strategy.

Shape analyses are important in a range of applications in medicine, biology, and computer vision. Shape analysis entails learning statistical models of shapes of anatomical structures from population data and subsequent statistical analyses, e.g., hypothesis testing or classification.
They provide information on the mean shape underlying a population group, the principles modes of variation of shape within the group,
and the differences between the mean shapes and variabilities between groups.
Examples of anatomical structures whose shape analysis holds important clues about the brain function include the subcortical structures.