I develop open-source computational methods for extracting meaningful, quantitative information from medical images—spanning computational anatomy, neuroimaging, pulmonary imaging, harmonization, and deep learning.
118 journal publications
20+ years in biomedical imaging
7 ANTsX open-source projects
3 major challenge wins & awards
Image registration, segmentation, cortical thickness, spatial normalization, and statistically principled phenotyping across scales and species.
Quantitative neuroimaging and pulmonary MRI methods designed around real biological and clinical questions.
Research software that makes advanced methodology available, inspectable, and reusable by the wider imaging community.
The ANTsX ecosystem
A family of open-source tools for quantitative biological and medical
imaging, from classical computational anatomy to deep-learning
workflows.
N4 bias-field correction
A widely adopted method for correcting intensity non-uniformity in MRI
and other medical images.
Large-scale cortical thickness evaluation
Benchmarking ANTs and FreeSurfer measurements to improve confidence in
quantitative neuroimaging studies.
Cross-site harmonization and reproducibility
Methods and evaluations addressing scanner effects, longitudinal data,
and scientific software bias.
Professor · Department of Radiology and Medical
Imaging
University of Virginia · Charlottesville, Virginia