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Found 36 papers, 9 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.
Revisiting registration-based synthesis: A focus on unsupervised MR image synthesis
Subsequently, this estimated deformation is applied to align the paired WMn counterpart of the moving CSFn image, yielding a synthetic WMn image for the fixed CSFn image.
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.
AniRes2D: Anisotropic Residual-enhanced Diffusion for 2D MR Super-Resolution
Our findings contribute valuable insights to the application of DDPMs for SR of anisotropic MR images.
Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion
Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual delineation.
Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation
Our experiments illustrate that the amalgamation of one-shot adaptation data with harmonized training data surpasses the performance of utilizing either data source in isolation.
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.
Rapid Brain Meninges Surface Reconstruction with Layer Topology Guarantee
The meninges, located between the skull and brain, are composed of three membrane layers: the pia, the arachnoid, and the dura.
Optimal operating MR contrast for brain ventricle parcellation
Development of MR harmonization has enabled different contrast MRIs to be synthesized while preserving the underlying anatomy.
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.
Automated Ventricle Parcellation and Evan's Ratio Computation in Pre- and Post-Surgical Ventriculomegaly
Normal pressure hydrocephalus~(NPH) is a brain disorder associated with enlarged ventricles and multiple cognitive and motor symptoms.
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.
A latent space for unsupervised MR image quality control via artifact assessment
Image quality control (IQC) can be used in automated magnetic resonance (MR) image analysis to exclude erroneous results caused by poorly acquired or artifact-laden images.
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.
On Finite Difference Jacobian Computation in Deformable Image Registration
For digital transformations, |J| is commonly approximated using a central difference, but this strategy can yield positive |J|'s for transformations that are clearly not diffeomorphic -- even at the voxel resolution level.
HACA3: A Unified Approach for Multi-site MR Image Harmonization
Furthermore, HACA3 is also robust to imaging artifacts and can be trained and applied to any set of MR contrasts.
Deep filter bank regression for super-resolution of anisotropic MR brain images
In 2D multi-slice magnetic resonance (MR) acquisition, the through-plane signals are typically of lower resolution than the in-plane signals.
Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions
This work showcases, for the first time, the potential of DL in automating the process of effusion assessment from ultrasound in LMIC settings where there is often a lack of experienced radiologists to perform such tasks.
Disentangling A Single MR Modality
Disentangling anatomical and contrast information from medical images has gained attention recently, demonstrating benefits for various image analysis tasks.
Coordinate Translator for Learning Deformable Medical Image Registration
The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images.
Data-driven Uncertainty Quantification in Computational Human Head Models
This framework is demonstrated on a 2D subject-specific head model, where the goal is to quantify uncertainty in the simulated strain fields (i. e., output), given variability in the material properties of different brain substructures (i. e., input).
MR Slice Profile Estimation by Learning to Match Internal Patch Distributions
To super-resolve the through-plane direction of a multi-slice 2D magnetic resonance (MR) image, its slice selection profile can be used as the degeneration model from high resolution (HR) to low resolution (LR) to create paired data when training a supervised algorithm.
Information-based Disentangled Representation Learning for Unsupervised MR Harmonization
Accuracy and consistency are two key factors in computer-assisted magnetic resonance (MR) image analysis.
A Structural Causal Model for MR Images of Multiple Sclerosis
Precision medicine involves answering counterfactual questions such as "Would this patient respond better to treatment A or treatment B?"
Dual-cycle Constrained Bijective VAE-GAN For Tagged-to-Cine Magnetic Resonance Image Synthesis
Tagged magnetic resonance imaging (MRI) is a widely used imaging technique for measuring tissue deformation in moving organs.
Self domain adapted network
However, training a model for each target domain is time consuming and computationally expensive, even infeasible when target domain data are scarce or source data are unavailable due to data privacy.
Finding novelty with uncertainty
Medical images are often used to detect and characterize pathology and disease; however, automatically identifying and segmenting pathology in medical images is challenging because the appearance of pathology across diseases varies widely.
Validating uncertainty in medical image translation
Medical images are increasingly used as input to deep neural networks to produce quantitative values that aid researchers and clinicians.
Evaluating the Impact of Intensity Normalization on MR Image Synthesis
Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image.
Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN
The cycleGAN is becoming an influential method in medical image synthesis.
Why rankings of biomedical image analysis competitions should be interpreted with care
International challenges have become the standard for validation of biomedical image analysis methods.
Topology guaranteed segmentation of the human retina from OCT using convolutional neural networks
Optical coherence tomography (OCT) is a noninvasive imaging modality which can be used to obtain depth images of the retina.
Self Super-Resolution for Magnetic Resonance Images using Deep Networks
This paper presents a self super-resolution~(SSR) algorithm, which does not use any external atlas images, yet can still resolve HR images only reliant on the acquired LR image.
