Event
BIOE Seminar: Image-Based Computational Models to Improve Diagnostics and Treatments
Friday, November 14, 2025
9:00 a.m.
A. James Clark Hall, Room #2121
Ian White
ianwhite@umd.edu
Eleonora Tubaldi
The University of Maryland
Department of Mechanical Engineering
From Computed Tomography Scans to Multiplex Immunofluorescence Slides: Image-Based Computational Models to Improve Diagnostics and Treatments
Abstract
Medical images, such as computed tomography (CT) scans and multiplex immunofluorescence (mIF) slides, play a critical role in the prevention, diagnosis, and treatment of diseases. CT scans, accompanied with echocardiograms, offer the ability to simulate blood flow through arteries. On the other hand, mIF slides are cellular-level images used to detect multiple biomarkers within a single tissue section. In this talk, we will discuss how new computational models can leverage both types of images to improve diagnostics and patients’ stratification. First, we investigate the hemodynamics of Type B aortic dissection (TBAD) through fluid-structure interaction (FSI) models that simulate blood flow in the aorta of three patient-specific geometries. These models are created by (i) segmenting CT scans, (ii) modeling and meshing the fluid and the solid components, (iii) identifying the dissection flap within the solid mesh, (iv) running FSI simulations, and (v) analyzing the FSI results. To study multiple patients simultaneously and achieve large-scale parallelization in FSI results, we developed a new pipeline that automates parts of steps (ii) to (v). Second, we discuss the generation of meaningful deep learning features from mIF slides of patients with cancer. Due to their large size, the analysis of mIF slides requires that clinicians select regions of interest (ROI), which themselves contain millions of pixels. Therefore, we focus on generating a smaller, but powerful representation of each ROI using a visual transformer, which allows the analysis of the whole slide. Finally, we demonstrate how extracting features that keep relevant information can be used to classify the tumor immune microenvironment (TIME) as hot or cold.
Speaker Bio
Dr. Tubaldi is Assistant Professor in the Department of Mechanical Engineering at the University of Maryland, College Park. She received her Ph.D. degree at McGill University in Mechanical Engineering. Her research interests sit at the interface of computational modelling, fluid-structure interaction, and machine learning algorithms for imaging techniques. In biomedical engineering, research applications span from cardiovascular biomechanics to cancer immunology. Recently, she has been awarded the 2024 ASME Henry Hess Early Career Publication Award, 2023 NSF CAREER Award, and the 2020 Haythornthwaite Young Investigator Award from the ASME Applied Mechanics Division.
