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Found 27 papers, 10 papers with code
Boosting Skull-Stripping Performance for Pediatric Brain Images
With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing.
Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI
Here, we propose a SVR method that overcomes the shortcomings of previous work and produces state-of-the-art reconstructions in the presence of extreme inter-slice motion.
JOSA: Joint surface-based registration and atlas construction of brain geometry and function
By recognizing the mismatch between geometry and function, JOSA provides new insights into the future development of registration methods using joint analysis of the brain structure and function.
Multi-Head Graph Convolutional Network for Structural Connectome Classification
To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification.
Joint cortical registration of geometry and function using semi-supervised learning
Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces.
Anatomy-aware and acquisition-agnostic joint registration with SynthMorph
Most affine methods are agnostic to anatomy, meaning the registration will be inaccurate if algorithms consider all structures in the image.
Data Consistent Deep Rigid MRI Motion Correction
Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies.
SuperWarp: Supervised Learning and Warping on U-Net for Invariant Subvoxel-Precise Registration
In recent years, learning-based image registration methods have gradually moved away from direct supervision with target warps to instead use self-supervision, with excellent results in several registration benchmarks.
Learning the Effect of Registration Hyperparameters with HyperMorph
We design a meta network, or hypernetwork, that predicts the parameters of a registration network for input hyperparameters, thereby comprising a single model that generates the optimal deformation field corresponding to given hyperparameter values.
SynthStrip: Skull-Stripping for Any Brain Image
The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams.
Supervision by Denoising for Medical Image Segmentation
SUD unifies stochastic averaging and spatial denoising techniques under a spatio-temporal denoising framework and alternates denoising and model weight update steps in an optimization framework for semi-supervision.
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.
Unsupervised learning of MRI tissue properties using MRI physics models
In this work we propose an unsupervised deep-learning strategy that employs MRI physics to estimate all three tissue properties from a single multiecho MRI scan session, and generalizes across varying acquisition parameters.
Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas
The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images.
HyperMorph: Amortized Hyperparameter Learning for Image Registration
We present HyperMorph, a learning-based strategy for deformable image registration that removes the need to tune important registration hyperparameters during training.
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).
3D Reconstruction and Segmentation of Dissection Photographs for MRI-free Neuropathology
NTNC traditionally requires a volumetric MRI scan, acquired either ex vivo or a short time prior to death.
SynthMorph: learning contrast-invariant registration without acquired images
This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts.
Cortical surface registration using unsupervised learning
The experiments show that the proposed SphereMorph is capable of modeling the geometric registration problem in a CNN framework and demonstrate superior registration accuracy and computational efficiency.
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.
Ranked #1 on
Brain Segmentation
on Brain MRI segmentation
FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline
In this work we propose a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer's anatomical segmentation including surface reconstruction and cortical parcellation.
Fast Infant MRI Skullstripping with Multiview 2D Convolutional Neural Networks
The three probabilistic segmentations in the three views are linearly fused and thresholded to produce a final brain mask.
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
To develop a deep learning-based segmentation model for a new image dataset (e. g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches.
PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation
In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input acquisition.
Pulse Sequence Resilient Fast Brain Segmentation
We use the forward models to augment the training data with test data specific training examples.
A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology
In this study, we present a probabilistic atlas of the thalamic nuclei built using ex vivo brain MRI scans and histological data, as well as the application of the atlas to in vivo MRI segmentation.
Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections
Here, we overcome this limitation with a probabilistic method that simultaneously solves for registration and synthesis directly on the target images, without any training data.
