21 Result(s)
-
Chapter and Conference Paper
MetaMorph: Learning Metamorphic Image Transformation with Appearance Changes
This paper presents a novel predictive model, MetaMorph, for metamorphic registration of images with appearance changes (i.e., caused by brain tumors). In contrast to previous learning-based registration metho...
-
Chapter and Conference Paper
NeurEPDiff: Neural Operators to Predict Geodesics in Deformation Spaces
This paper presents NeurEPDiff, a novel network to fast predict the geodesics in deformation spaces generated by a well known Euler-Poincaré differential equation (EPDiff). To achieve this, we develop a neural...
-
Chapter and Conference Paper
SADIR: Shape-Aware Diffusion Models for 3D Image Reconstruction
3D image reconstruction from a limited number of 2D images has been a long-standing challenge in computer vision and image analysis. While deep learning-based approaches have achieved impressive performance in...
-
Chapter and Conference Paper
Defending Medical Image Diagnostics Against Privacy Attacks Using Generative Methods: Application to Retinal Diagnostics
Machine learning (ML) models used in medical imaging diagnostics can be vulnerable to a variety of privacy attacks, including membership inference attacks, that lead to violations of regulations governing the use...
-
Chapter and Conference Paper
Bayesian Atlas Building with Hierarchical Priors for Subject-Specific Regularization
This paper presents a novel hierarchical Bayesian model for unbiased atlas building with subject-specific regularizations of image registration. We develop an atlas construction process that automatically sele...
-
Chapter and Conference Paper
Mixture Probabilistic Principal Geodesic Analysis
Dimensionality reduction on Riemannian manifolds is challenging due to the complex nonlinear data structures. While probabilistic principal geodesic analysis (PPGA) has been proposed to generalize conventiona...
-
Chapter and Conference Paper
Data-Driven Model Order Reduction for Diffeomorphic Image Registration
This paper presents a data-driven model reduction algorithm to reduce the computational complexity of diffeomorphic image registration in the context of large deformation diffeomorphic metric map** (LDDMM). ...
-
Chapter and Conference Paper
On the Applicability of Registration Uncertainty
Estimating the uncertainty in (probabilistic) image registration enables, e.g., surgeons to assess the operative risk based on the trustworthiness of the registered image data. If surgeons receive inaccurately...
-
Article
Fast Diffeomorphic Image Registration via Fourier-Approximated Lie Algebras
This paper introduces Fourier-approximated Lie algebras for shooting (FLASH), a fast geodesic shooting algorithm for diffeomorphic image registration. We approximate the infinite-dimensional Lie algebra of smo...
-
Chapter and Conference Paper
Efficient Laplace Approximation for Bayesian Registration Uncertainty Quantification
This paper presents a novel approach to modeling the posterior distribution in image registration that is computationally efficient for large deformation diffeomorphic metric map** (LDDMM). We develop a Lapl...
-
Chapter and Conference Paper
A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation
A reliable Ultrasound (US)-to-US registration method to compensate for brain shift would substantially improve Image-Guided Neurological Surgery. Develo** such a registration method is very challenging, due ...
-
Chapter and Conference Paper
Frequency Diffeomorphisms for Efficient Image Registration
This paper presents an efficient algorithm for large deformation diffeomorphic metric map** (LDDMM) with geodesic shooting for image registration. We introduce a novel finite dimensional Fourier representati...
-
Chapter and Conference Paper
Fast Geodesic Regression for Population-Based Image Analysis
Geodesic regression on images enables studies of brain development and degeneration, disease progression, and tumor growth. The high-dimensional nature of image data presents significant computational challeng...
-
Chapter and Conference Paper
Temporal Registration in In-Utero Volumetric MRI Time Series
We present a robust method to correct for motion and deformations in in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI requires robust alignment across time in the presence of substa...
-
Chapter and Conference Paper
Low-Dimensional Statistics of Anatomical Variability via Compact Representation of Image Deformations
Using image-based descriptors to investigate clinical hypotheses and therapeutic implications is challenging due to the notorious “curse of dimensionality” coupled with a small sample size. In this paper, we p...
-
Chapter
Probabilistic Geodesic Models for Regression and Dimensionality Reduction on Riemannian Manifolds
We present a probabilistic formulation for two closely related statistical models for Riemannian manifold data: geodesic regression and principal geodesic analysis. These models generalize linear regression an...
-
Chapter and Conference Paper
SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation
Region-based methods have proven necessary for improving segmentation accuracy of neuronal structures in electron microscopy (EM) images. Most region-based segmentation methods use a scoring function to determ...
-
Chapter and Conference Paper
Finite-Dimensional Lie Algebras for Fast Diffeomorphic Image Registration
This paper presents a fast geodesic shooting algorithm for diffeomorphic image registration. We first introduce a novel finite-dimensional Lie algebra structure on the space of bandlimited velocity fields. We ...
-
Chapter and Conference Paper
A Hierarchical Bayesian Model for Multi-Site Diffeomorphic Image Atlases
Image templates, or atlases, play a critical role in imaging studies by providing a common anatomical coordinate system for analysis of shape and function. It is now common to estimate an atlas as a deformable...
-
Chapter and Conference Paper
Bayesian Principal Geodesic Analysis in Diffeomorphic Image Registration
Computing a concise representation of the anatomical variability found in large sets of images is an important first step in many statistical shape analyses. In this paper, we present a generative Bayesian app...
