Keywords: Super-Resolution, Ultra High-Field MRI, Generative Adversarial Network, Data Augmentation
TL;DR: GAN based super-resolution as an way of augmenting data to improve segmentation accuracy at 9Tesla MRI
Abstract: Segmenting ultra high-field MR images is an important first step in many applications. Segmentation methods based on machine learning have been shown to be valuable tools for this purpose. However, for ultra high-field MR images ($>$ 7 Tesla), a lack of training data is a problem. Therefore, in this work, we propose to use super-resolution for augmenting the training set. Specifically, we describe an efficient super-resolution model based on Generative Adversarial Network(GAN). It produces synthetic images that simulate MR data at ultra high isotropic resolutions of $0.6$ mm. We present the first results that show an improvement in segmentation accuracy of imaging data acquired at a 9.4 Tesla MRI scanner.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Image Synthesis
Secondary Subject Area: Unsupervised Learning and Representation Learning
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