Computing PhD Research

Scientific Computing and Imaging Institute

Kalhert School of Computing

University of Utah

Overview

My research is in machine learning for medical image analysis, with a focus on Statistical Shape Modeling (SSM). SSM enables population-based quantitative analysis of anatomical shapes such as bones and organs, informing clinical diagnosis. However, traditional optimization-based SSM generation techniques have limitations that hinder their widespread adoption. My work aims to make SSM more feasible, trustworthy, and broadly applicable. I've established three research aims to this end.

Aim 1: Uncertainty Quantification in SSM from Images

Deep learning frameworks, such as DeepSSM, extract SSM directly from unsegmented images (such as CT or MRI scans) with little manual overhead. While such approaches alleviate the time-consuming and expert-driven workflow of traditional SSM generation techniques in inference,  they can produce overconfident estimates of shape that cannot be blindly assumed to be accurate. In my work, I have adapted such networks to convey what they do not know via granular estimates of uncertainty. This is critical in sensitive clinical applications. 

ShapeMI Workshop at MICCAI 2020 (Best Paper Runner-Up)

Uncertain-DeepSSM_Graphical_Abrstract-v3.pdf

MICCAI 2023 (Early Accept)

BVIB_poster.pdf

Aim 2: Learning Spatiotemporal SSM

Numerous clinical investigations require understanding changes in anatomical shape over time, such as in dynamic organ cycle characterization or longitudinal analyses (e.g., for disease progression). Spatiotemporal statistical shape modeling (SSM) allows for quantifying and evaluating dynamic shape variation with respect to a cohort or population of interest. Existing SSM generation approaches assume sample independence and thus are unsuitable for sequential dynamic shape observations. I've extended the optimization scheme to capture a statistically significant time dependency directly.

STACOM Workshop at MICCAI 2022

4D_STACOM22_Poster.pdf

Frontiers in Bioengineering and Biotechnology Journal 2023

Aim 3: SSM from Point Clouds

Traditional methods for creating SSMs need noise-free surface meshes or binary volumes, rely on assumptions or predefined templates, and simultaneously optimize across the entire cohort, leading to lengthy inference times given new data. My work overcomes these barriers by providing a data-driven solution that infers SSMs directly from raw point clouds, reducing inference burdens and increasing applicability as point clouds are more easily acquired. 

MICCAI 2023 (Early Accept)

PointCompletionSSM.pdf