activity
20192025
most citedLearning Conditional Deformable Templates with Convolutional Networks

78 citations · 83 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2025

Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains

Marianne Rakic, Siyu Gai, Etienne Chollet +2

A single biomedical image can be meaningfully segmented in multiple ways, depending on the desired application. For instance, a brain MRI can be segmented according to tissue types…

cs.CV2025

AtlasMorph: Learning conditional deformable templates for brain MRI

Marianne Rakic, Andrew Hoopes, S. Mazdak Abulnaga +3

Deformable templates, or atlases, are images that represent a prototypical anatomy for a population, and are often enhanced with probabilistic anatomical label maps. They are commo…

cs.CV2025

MultiMorph: On-demand Atlas Construction

S. Mazdak Abulnaga, Andrew Hoopes, Neel Dey +5

We present MultiMorph, a fast and efficient method for constructing anatomical atlases on the fly. Atlases capture the canonical structure of a collection of images and are essenti…

eess.IV2024

Tyche: Stochastic In-Context Learning for Medical Image Segmentation

Marianne Rakic, Hallee E. Wong, Jose Javier Gonzalez Ortiz +3

Existing learning-based solutions to medical image segmentation have two important shortcomings. First, for most new segmentation task, a new model has to be trained or fine-tuned.…

cs.CV2023★ 5 cited

ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image

Hallee E. Wong, Marianne Rakic, John Guttag +1

Biomedical image segmentation is a crucial part of both scientific research and clinical care. With enough labelled data, deep learning models can be trained to accurately automate…

cs.CV2020

Anatomical Predictions using Subject-Specific Medical Data

Marianne Rakic, John Guttag, Adrian V. Dalca

Changes over time in brain anatomy can provide important insight for treatment design or scientific analyses. We present a method that predicts how a brain MRI for an individual wi…

cs.CV2019★ 78 cited

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…