MedGAN: Medical image translation using GANs
Image-to-image translation is considered a new frontier in the field of medical image
analysis, with numerous potential applications. However, a large portion of recent …
analysis, with numerous potential applications. However, a large portion of recent …
Multiscale brain MRI super-resolution using deep 3D convolutional networks
CH Pham, C Tor-Díez, H Meunier, N Bednarek… - … Medical Imaging and …, 2019 - Elsevier
The purpose of super-resolution approaches is to overcome the hardware limitations and
the clinical requirements of imaging procedures by reconstructing high-resolution images …
the clinical requirements of imaging procedures by reconstructing high-resolution images …
Image super-resolution using progressive generative adversarial networks for medical image analysis
D Mahapatra, B Bozorgtabar, R Garnavi - Computerized Medical Imaging …, 2019 - Elsevier
Anatomical landmark segmentation and pathology localisation are important steps in
automated analysis of medical images. They are particularly challenging when the anatomy …
automated analysis of medical images. They are particularly challenging when the anatomy …
[HTML][HTML] FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution
High-resolution magnetic resonance images can provide fine-grained anatomical
information, but acquiring such data requires a long scanning time. In this paper, a …
information, but acquiring such data requires a long scanning time. In this paper, a …
Multi-input cardiac image super-resolution using convolutional neural networks
Abstract 3D cardiac MR imaging enables accurate analysis of cardiac morphology and
physiology. However, due to the requirements for long acquisition and breath-hold, the …
physiology. However, due to the requirements for long acquisition and breath-hold, the …
Simultaneous super-resolution and cross-modality synthesis of 3D medical images using weakly-supervised joint convolutional sparse coding
Abstract Magnetic Resonance Imaging (MRI) offers high-resolution in vivo imaging and rich
functional and anatomical multimodality tissue contrast. In practice, however, there are …
functional and anatomical multimodality tissue contrast. In practice, however, there are …
An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine
Super-resolution, which is one of the trend issues of recent times, increases the resolution of
the images to higher levels. Increasing the resolution of a vital image in terms of the …
the images to higher levels. Increasing the resolution of a vital image in terms of the …
Channel splitting network for single MR image super-resolution
High resolution magnetic resonance (MR) imaging is desirable in many clinical applications
due to its contribution to more accurate subsequent analyses and early clinical diagnoses …
due to its contribution to more accurate subsequent analyses and early clinical diagnoses …
Simultaneous single-and multi-contrast super-resolution for brain MRI images based on a convolutional neural network
In magnetic resonance imaging (MRI), the acquired images are usually not of high enough
resolution due to constraints such as long sampling times and patient comfort. High …
resolution due to constraints such as long sampling times and patient comfort. High …
[HTML][HTML] 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 …
designed for data with near-isotropic voxels at approximately 1 mm resolution, and …