Streamlined Biomedical Image Processing Pipelines

PhD Thesis Defense by Jenna Kim

Committee Members: Prof. Daniel Haehn (Chair), Prof. Jae W. Song, Prof. Tales Imbiriba, Prof. Nurit Haspel

GPD: Prof. Dan Simovici

When: 11:00 AM, April 08, 2026 (Wednesday)

Where: Pomplun Lab

Zoom Link: https://umassboston.zoom.us/my/jennajkim

This dissertation focuses on advancing carotid artery analysis through a series of visualizations and deep learning tools for calcified plaque assessment and related biomedical imaging tasks. Accurate plaque evaluation is essential, but current workflows depend on slow, clinician-dependent manual review. To address these limitations, this work introduces the CACTAS framework, a set of tools and methods that enable fast and reliable plaque segmentation for clinicians.

The first study, the CACTAS-Tool, provides a web-based labeling tool that enables clinicians to label plaque directly in three dimensions through a streamlined one-click interface. This tool significantly reduces the effort required to generate high-quality annotations by replacing slice-by-slice labeling with a more intuitive 3D interaction model, offering a faster alternative to existing manual workflows.

The second project, CACTAS-AI, automates plaque segmentation through a two-step deep learning approach. The system first segments the carotid artery to focus the search space and then segments calcified plaque within this anatomically relevant region.

The third study, CACTAS-UQ, investigates beyond segmentation by showing how confident the AI is in its predictions. To make this information accessible, the study introduces an integrated visualization that combines plaque composition, prediction probability, and uncertainty into a single unified view. Uncertain regions are visually flagged, giving clinicians a clearer picture of prediction reliability and supporting more informed decision-making.

This dissertation extends these visualization concepts to a microscopy-based biomedical setting through an ongoing collaboration with MYOTWIN, which serves as an extension of a DAAD-funded research exchange in Germany, where I was a visiting researcher in Summer 2025. This project focuses on the interactive visualization and analysis of calcium transients in engineered heart tissue, leveraging fluorescence imaging to study cardiac behavior.

Together, these studies advance the role of visualization and machine learning in medical image analysis, presenting practical, interpretable, and clinician-centered tools that enable more transparent and efficient assessment of vascular and cardiac imaging data. These contributions lay the foundation for future applications in clinical decision support.