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Found 13 papers, 1 papers with code
From Registration Uncertainty to Segmentation Uncertainty
Here, we propose a novel framework to concurrently estimate both the epistemic and aleatoric segmentation uncertainties for image registration.
Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of Deep Learning?
This is a factor that has been overlooked in prior research on tMRI post-processing.
Efficient Annotation for Medical Image Analysis: A One-Pass Selective Annotation Approach
Annotating biomedical images for supervised learning is a complex and labor-intensive task due to data diversity and its intricate nature.
MomentaMorph: Unsupervised Spatial-Temporal Registration with Momenta, Shooting, and Correction
We introduce a novel "momenta, shooting, and correction" framework for Lagrangian motion estimation in the presence of repetitive patterns and large motion.
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.
LitCall: Learning Implicit Topology for CNN-based Aortic Landmark Localization
Given that the thoracic aorta has a relatively conserved topology across the population and that a human annotator with minimal training can estimate the location of unseen landmarks from limited examples, we proposed an auxiliary learning task to learn the implicit topology of aortic landmarks through a CNN-based network.
DrDisco: Deep Registration for Distortion Correction of Diffusion MRI with single phase-encoding
Since a great amount of diffusion data are only acquired with a single phase-encoding direction, the application of existing approaches is limited.
Label Propagation via Random Walk for Training Robust Thalamus Nuclei Parcellation Model from Noisy Annotations
Data-driven thalamic nuclei parcellation depends on high-quality manual annotations.
FastCod: Fast Brain Connectivity in Diffusion Imaging
Connectivity information derived from diffusion-weighted magnetic resonance images~(DW-MRIs) plays an important role in studying human subcortical gray matter structures.
New starting point registration method for tagged MRI tongue motion estimation
Accurate tongue motion estimation is essential for tongue function evaluation.
DRIMET: Deep Registration for 3D Incompressible Motion Estimation in Tagged-MRI with Application to the Tongue
We show that the method outperforms existing approaches, and also exhibits improvements in speed, robustness to tag fading, and large tongue motion.
Segmenting thalamic nuclei from manifold projections of multi-contrast MRI
After registration of these contrasts and isolation of the thalamus, we use the uniform manifold approximation and projection (UMAP) method for dimensionality reduction to produce a low-dimensional representation of the data within the thalamus.
Learning Pixel Trajectories with Multiscale Contrastive Random Walks
A range of video modeling tasks, from optical flow to multiple object tracking, share the same fundamental challenge: establishing space-time correspondence.
