6 papers
PRISM: A 3D Probabilistic Neural Representation for Interpretable Shape Modeling
Yining Jiao, Sreekalyani Bhamidi, Carlton Jude Zdanski +8
Understanding how anatomical shapes evolve in response to developmental covariates - and quantifying their spatially varying uncertainties - is critical in healthcare research. Exi…
Investigating Demographic Bias in Brain MRI Segmentation: A Comparative Study of Deep-Learning and Non-Deep-Learning Methods
Ghazal Danaee, Marc Niethammer, Jarrett Rushmore +1
Deep-learning-based segmentation algorithms have substantially advanced the field of medical image analysis, particularly in structural delineations in MRIs. However, an important…
Guiding Registration with Emergent Similarity from Pre-Trained Diffusion Models
Nurislam Tursynbek, Hastings Greer, Basar Demir +1
Diffusion models, while trained for image generation, have emerged as powerful foundational feature extractors for downstream tasks. We find that off-the-shelf diffusion models, tr…
CARL: A Framework for Equivariant Image Registration
Hastings Greer, Lin Tian, Francois-Xavier Vialard +3
Image registration estimates spatial correspondences between a pair of images. These estimates are typically obtained via numerical optimization or regression by a deep network. A…
LucidAtlas: Learning Uncertainty-Aware, Covariate-Disentangled, Individualized Atlas Representations
Yining Jiao, Sreekalyani Bhamidi, Huaizhi Qu +11
The goal of this work is to develop principled techniques to extract information from high dimensional data sets with complex dependencies in areas such as medicine that can provid…
multiGradICON: A Foundation Model for Multimodal Medical Image Registration
Basar Demir, Lin Tian, Thomas Hastings Greer +7
Modern medical image registration approaches predict deformations using deep networks. These approaches achieve state-of-the-art (SOTA) registration accuracy and are generally fast…