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.

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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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.

Adversarial Data Augmentation via Deformation Statistics

Deep learning models have been successful in computer vision and medical image analysis. However, training these models frequently requires large labeled image sets whose creation is often very time and labor intensive, for example, in the context of 3D segmentations. Approaches capable of training deep segmentation networks with a limited number of labeled samples are therefore highly desirable. Data augmentation or semi-supervised approaches are commonly used to cope with limited labeled training data. However, the augmentation strategies for many existing approaches are either hand-engineered or require computationally demanding searches. To that end, we explore an augmentation strategy which builds statistical deformation models from unlabeled data via principal component analysis and uses the resulting statistical deformation space to augment the labeled training samples. Specifically, we obtain transformations via deep registration models. This allows for an intuitive control over plausible deformation magnitudes via the statistical model and, if combined with an appropriate deformation model, yields spatially regular transformations. To optimally augment a dataset we use an adversarial strategy integrated into our statistical deformation model. We demonstrate the effectiveness of our approach for the segmentation of knee cartilage from 3D magnetic resonance images. We show favorable performance to state-of-the-art augmentation approaches.