MRI Augmentation via Elastic Registration for Brain Lesions Segmentation

By Egor Krivov, Maxim Pisov, Mikhail Belyaev. 2017

13 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Neurology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Daniel Rueckert
2. Alexander Hammers
3. Paul Bentley
4. Ricardo Guerrero
5. Roger N. Gunn
6. Jason A. Dowling
7. Liang Chen
8. Evgin Göçeri

In Neurology

1. Joanna M. Wardlaw
2. Daniel Rueckert
3. Roger N. Gunn
4. Evgin Göçeri

Citations in the corpus

• GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks (2018, background): “Augmentation: As discussed in Section [11] extensive augmentation, beyond simple reflection, is rarely used in brain imaging due to the variety of preprocessing options available and anatomical constraints of the brain.”
• A review of medical image data augmentation techniques for deep learning applications (2021, methodology): “26 developed a method wherein brain lesions could be mapped onto healthy patient scans, by performing deformable image registration between the original patient and a healthy one.”