3 accepted papers
We present Point2SSM, a novel unsupervised learning approach for constructing correspondence-based statistical shape models (SSMs) directly from raw point clouds. SSM is crucial in clinical research, enabling population-level analysis of morphological variation in bones and organs. Traditional metho…
The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe. However, observations of the CMB are contaminated by foreground emissions, obscuring the CMB signal and reducing its efficacy in constraining cosmological parameters. We employ…
Statistical shape modeling (SSM) is an enabling tool in medical image analysis as it allows for population-based quantitative analysis. The traditional pipeline for landmark-based SSM from images requires painstaking and cost-prohibitive steps. My thesis aims to leverage probabilistic deep learning…