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Found 10 papers, 4 papers with code
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.
An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation
In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans.
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.
Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels
However, multiple per-image annotations are often not available in a real-world application and the uncertainty does not provide full control on segmentation results to users.
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
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.
A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis.
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.
