My research interests lie in the field of mathematical biology and machine learning for biomedical imaging. Mathematical biology uses mathematics and statistical techniques to explain and predict biological phenomena. My mathematical interests include formulation, analysis, parameter estimation, and uncertainty quantification of ordinary and partial differential equation models. My biological interests lie in heterogeneity in cellular biology (cancer modeling), physiological modeling, and ecotoxicological modeling. I create novel machine learning algorithms to perform cell segmentation is phase-contrast biomedical images.
Cell segmentation is an important task in the biomedical field, but it can be laborious and time consuming. Machine learning has emerged as a powerful tool that can automate image segmentation tasks with accuracy. We recently proposed a convolutional neural network that traces the cell boundary, ensuring a contiguously segmented region. The video below displays the cell tracer in action. The white represents the ground truth segmentation of the cell, the green is the machine learning predicted cell trace. The 'predicted moves' shows the next 20 pixels predicted by our network and the direction choice shows the direction the tracer moves in next.
Collaborators: Kevin Flores (North Carolina State Univeristy) and John Lagergren (Oak Ridge National Lab)
- Rutter EM, Lagergren JH, Flores KB. A convolutional neural network method for boundary optimization enables few-shot learning for biomedical image segmentation. In: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. Springer ; 2019. pp. 190–198. (pdf) (link)
- Rutter EM, Lagergren JH, Flores KB. Automated object tracing for biomedical image segmentation using a deep convolutional neural network, in International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer ; 2018 :686–694. (pdf) (link)
Modeling Spread of COVID-19 on Campus
How does COVID-19 spread through a population? Recently, we were tasked with hypothetically modeling the spread of COVID-19 across UC Merced's campus. This work is based on the SIR (susceptible, infectious, recovered) epidemiological model and investigates effectiveness of non-pharmaceutical interventions (NPIs) such as wearing masks, transitioning to online learning, and self-isolating if experiencing symptoms. Which interventions are most important and how does that change as the population begins to become vaccinated?
Collaborators: Suzanne Sindi, Shilpa Khatri, Lihong Zhao, and Fabian Santiago