A review of multimodal image matching: Methods and applications
Multimodal image matching, which refers to identifying and then corresponding the same or
similar structure/content from two or more images that are of significant modalities or …
similar structure/content from two or more images that are of significant modalities or …
MRI segmentation of the human brain: challenges, methods, and applications
I Despotović, B Goossens… - … mathematical methods in …, 2015 - Wiley Online Library
Image segmentation is one of the most important tasks in medical image analysis and is
often the first and the most critical step in many clinical applications. In brain MRI analysis …
often the first and the most critical step in many clinical applications. In brain MRI analysis …
[HTML][HTML] A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease
Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder. Mild
cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can …
cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can …
Voxelmorph: a learning framework for deformable medical image registration
We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical
image registration. Traditional registration methods optimize an objective function for each …
image registration. Traditional registration methods optimize an objective function for each …
A deep learning framework for unsupervised affine and deformable image registration
Image registration, the process of aligning two or more images, is the core technique of
many (semi-) automatic medical image analysis tasks. Recent studies have shown that deep …
many (semi-) automatic medical image analysis tasks. Recent studies have shown that deep …
Breaking the dilemma of medical image-to-image translation
L Kong, C Lian, D Huang, Y Hu… - Advances in Neural …, 2021 - proceedings.neurips.cc
Abstract Supervised Pix2Pix and unsupervised Cycle-consistency are two modes that
dominate the field of medical image-to-image translation. However, neither modes are ideal …
dominate the field of medical image-to-image translation. However, neither modes are ideal …
Data augmentation using learned transformations for one-shot medical image segmentation
Image segmentation is an important task in many medical applications. Methods based on
convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on …
convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on …
Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces
Classical deformable registration techniques achieve impressive results and offer a rigorous
theoretical treatment, but are computationally intensive since they solve an optimization …
theoretical treatment, but are computationally intensive since they solve an optimization …
An unsupervised learning model for deformable medical image registration
We present a fast learning-based algorithm for deformable, pairwise 3D medical image
registration. Current registration methods optimize an objective function independently for …
registration. Current registration methods optimize an objective function independently for …
Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided
diagnosis of neurodegenerative disorders, eg, Alzheimer's disease (AD), due to its …
diagnosis of neurodegenerative disorders, eg, Alzheimer's disease (AD), due to its …