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Found 13 papers, 8 papers with code
Towards Automatic Abdominal MRI Organ Segmentation: Leveraging Synthesized Data Generated From CT Labels
Our approach shows comparable results compared to fully-supervised segmentation methods trained on MR data.
SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in Brain MRI
Here we propose EquiTrack, the first method that uses recent steerable SE(3)-equivariant CNNs (E-CNN) for motion tracking.
Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis.
AnyStar: Domain randomized universal star-convex 3D instance segmentation
Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units.
Domain-agnostic segmentation of thalamic nuclei from joint structural and diffusion MRI
Some tools have attempted to incorporate information from diffusion MRI in the segmentation to refine these boundaries, but do not generalise well across diffusion MRI acquisitions.
Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets
Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset.
Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining
We show that this method is considerably more robust than SynthSeg, while also outperforming cascaded networks and state-of-the-art segmentation denoising methods.
Accurate super-resolution low-field brain MRI
To address this challenge, recent advances in machine learning facilitate the synthesis of higher resolution images derived from one or multiple lower resolution scans.
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining
Here we introduce SynthSeg, the first segmentation CNN robust against changes in contrast and resolution.
Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well - typically requiring T1 scans (e. g., MP-RAGE).
SynthMorph: learning contrast-invariant registration without acquired images
This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts.
Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast
Partial voluming (PV) is arguably the last crucial unsolved problem in Bayesian segmentation of brain MRI with probabilistic atlases.
A Learning Strategy for Contrast-agnostic MRI Segmentation
These samples are produced using the generative model of the classical Bayesian segmentation framework, with randomly sampled parameters for appearance, deformation, noise, and bias field.
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