A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond
Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …
registration over the past decade. The initial developments, such as regression-based and U …
SADIR: shape-aware diffusion models for 3D image reconstruction
Abstract 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 …
standing challenge in computer vision and image analysis. While deep learning-based …
Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections
This paper explicates a solution to building correspondences between molecular-scale
transcriptomics and tissue-scale atlases. This problem arises in atlas construction and cross …
transcriptomics and tissue-scale atlases. This problem arises in atlas construction and cross …
MetaMorph: learning metamorphic image transformation with appearance changes
This paper presents a novel predictive model, MetaMorph, for metamorphic registration of
images with appearance changes (ie, caused by brain tumors). In contrast to previous …
images with appearance changes (ie, caused by brain tumors). In contrast to previous …
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 …
spaces generated by a well known Euler-Poincaré differential equation (EPDiff). To achieve …
DeepSeeded: Volumetric segmentation of dense cell populations with a cascade of deep neural networks in bacterial biofilm applications
Accurate and automatic segmentation of individual cell instances in microscopy images is a
vital step for quantifying the cellular attributes, which can subsequently lead to new …
vital step for quantifying the cellular attributes, which can subsequently lead to new …
SpaER: Learning Spatio-temporal Equivariant Representations for Fetal Brain Motion Tracking
In this paper, we introduce SpaER, a pioneering method for fetal motion tracking that
leverages equivariant filters and self-attention mechanisms to effectively learn spatio …
leverages equivariant filters and self-attention mechanisms to effectively learn spatio …
Invariant Shape Representation Learning For Image Classification
Geometric shape features have been widely used as strong predictors for image
classification. Nevertheless, most existing classifiers such as deep neural networks (DNNs) …
classification. Nevertheless, most existing classifiers such as deep neural networks (DNNs) …
MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image Deformations
Geometric transformations have been widely used to augment the size of training images.
Existing methods often assume a unimodal distribution of the underlying transformations …
Existing methods often assume a unimodal distribution of the underlying transformations …
Activation From Sparse 2D Cardiac MRIs
Identifying regions of late mechanical activation (LMA) of the left ventricular (LV)
myocardium is critical in determining the optimal pacing site for cardiac resynchronization …
myocardium is critical in determining the optimal pacing site for cardiac resynchronization …