2.2k citations · 5.8k across the 101 of their papers we have counts for
6 papers · 2 filters
Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation
Hyeon Woo Lee, Mert R. Sabuncu, Adrian V. Dalca
We tackle biomedical image segmentation in the scenario of only a few labeled brain MR images. This is an important and challenging task in medical applications, where manual annot…
Learning Conditional Deformable Templates with Convolutional Networks
Adrian V. Dalca, Marianne Rakic, John Guttag +1
We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks. Conventional methods for te…
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
Adrian V. Dalca, Evan Yu, Polina Golland +3
Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these…
Unsupervised Data Imputation via Variational Inference of Deep Subspaces
Adrian V. Dalca, John Guttag, Mert R. Sabuncu
A wide range of systems exhibit high dimensional incomplete data. Accurate estimation of the missing data is often desired, and is crucial for many downstream analyses. Many state-…
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation
Adrian V. Dalca, John Guttag, Mert R. Sabuncu
We consider the problem of segmenting a biomedical image into anatomical regions of interest. We specifically address the frequent scenario where we have no paired training data th…
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Adrian V. Dalca, Guha Balakrishnan, John Guttag +1
Classical deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimizati…