About Us

UNC Biomedical Image Analysis Group

The Biomedical Image Analysis Group at the University of North Carolina at Chapel Hill (UNC-biag) focuses on the design of computational algorithms to extract quantitative measures from biomedical data. While our emphasis is on image data (e.g., obtained via magnetic resonance imaging, computed tomography, or microscopy) our analyses also include clinical measures and genomics. The group is led by Marc Niethammer.

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Our work is highly interdisciplinary and includes collaborators from a wide range of disciplines such as statistics, applied mathematics, radiology, surgery, and epidemiology. Consequently, we also publish in venues ranging from clinical journals, to medical conferences (such as MICCAI and IPMI) to computer vision conferences (such as CVPR and ECCV), to machine learning conferences (such as NeurIPS, ICML, and AAAI).

Recent Publications

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Discovering Hidden Physics Behind Transport Dynamics

Transport processes are ubiquitous. They are, for example, at the heart of optical flow approaches; or of perfusion imaging, where blood transport is assessed, most commonly by injecting a tracer. An advection-diffusion equation is widely used to describe these transport phenomena. Our goal is estimating the underlying physics of advection-diffusion equations, expressed as velocity and diffusion tensor fields. We propose a learning framework (YETI) building on an auto-encoder structure between 2D and 3D image time-series, which incorporates the advection-diffusion model. To help with identifiability, we develop an advection-diffusion simulator which allows pre-training of our model by supervised learning using the velocity and diffusion tensor fields. Instead of directly learning these velocity and diffusion tensor fields, we introduce representations that assure incompressible flow and symmetric positive semi-definite diffusion fields and demonstrate the additional benefits of these representations on improving estimation accuracy. We further use transfer learning to apply YETI on a public brain magnetic resonance (MR) perfusion dataset of stroke patients and show its ability to successfully distinguish stroke lesions from normal brain regions via the estimated velocity and diffusion tensor fields.

PIANO: Perfusion Imaging via Advection-diffusion

Perfusion imaging (PI) is clinically used to assess strokes and brain tumors. Commonly used PI approaches based on magnetic resonance imaging (MRI) or computed tomography (CT) image the effect of a contrast agent moving through blood vessels and into tissue. Contrast-agent free approaches, for example, based on intravoxel incoherent motion, also exist, but are so far not routinely used clinically. MR or CT perfusion imaging based on contrast agents relies on the estimation of the arterial input function (AIF) to approximately model tissue perfusion, neglecting spatial dependencies. Reliably estimating the AIF is also non-trivial, leading to difficulties with standardizing perfusion measures. In this work we therefore propose a data-assimilation approach (PIANO) which estimates the velocity and diffusion fields of an advection-diffusion model best explaining the contrast dynamics. PIANO accounts for spatial dependencies and neither requires estimating the AIF nor relies on a particular contrast agent bolus shape. Specifically, we propose a convenient parameterization of the estimation problem, a numerical estimation approach, and extensively evaluate PIANO. We demonstrate that PIANO can successfully resolve velocity and diffusion field ambiguities and results in sensitive measures for the assessment of stroke, comparing favorably to conventional measures of perfusion.