SegSRGAN: Super-resolution and segmentation using generative adversarial networks — Application to neonatal brain MRI

Delannoy, Quentin; Pham, Chi-Hieu; Cazorla, Clément; Tor-Díez, Carlos; Dollé, Guillaume; Meunier, Hélène; Bednarek, Nathalie; Fablet, Ronan; Passat, Nicolas; Rousseau, François

doi:10.1016/j.compbiomed.2020.103755

“…Since its introduction, there has been a surge of interest in the application of GAN frameworks related to the brain. Some of the applications include image generation with improved properties such as achieving super resolution or better quality [6][7][8][9][10][11], data augmentation [12][13][14], segmentation [9,[13][14][15][16], image reconstruction [17][18][19][20], image-to-image translation [21][22][23][24], and motion correction [25,26]. While these important studies have demonstrated the exciting prospect of using GAN architectures, there is a limited amount of work that has focused on utilizing the generated images for subsequent tasks such as disease classification [27].…”

Section: Introductionmentioning

confidence: 99%

Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning

Zhou

¹

,

Qiu

²

,

Joshi

³

et al. 2021

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Background Generative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn from magnetic resonance imaging (MRI) scans of multiple magnetic field strengths to enhance Alzheimer’s disease (AD) classification performance. Methods T1-weighted brain MRI scans from 151 participants of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), who underwent both 1.5-Tesla (1.5-T) and 3-Tesla imaging at the same time were selected to construct a GAN model. This model was trained along with a three-dimensional fully convolutional network (FCN) using the generated images (3T*) as inputs to predict AD status. Quality of the generated images was evaluated using signal to noise ratio (SNR), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE). Cases from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n = 107) and the National Alzheimer’s Coordinating Center (NACC, n = 565) were used for model validation. Results The 3T*-based FCN classifier performed better than the FCN model trained using the 1.5-T scans. Specifically, the mean area under curve increased from 0.907 to 0.932, from 0.934 to 0.940, and from 0.870 to 0.907 on the ADNI test, AIBL, and NACC datasets, respectively. Additionally, we found that the mean quality of the generated (3T*) images was consistently higher than the 1.5-T images, as measured using SNR, BRISQUE, and NIQE on the validation datasets. Conclusion This study demonstrates a proof of principle that GAN frameworks can be constructed to augment AD classification performance and improve image quality.

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“…Their models outperformed previous models with an overall 0.66%. A Super resolution and segmentation using generative adversarial networks is a framework introduced by [30] to neonatal brain M RI. It consists training a generating network that estimates for a given input image to its corresponding HR, and a discriminator network D is designed to distinguish real HR and segmentation images.…”

Section: B Generative Adversarial Networkmentioning

confidence: 99%

Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Network

Khaled¹,

Han²

2021

Preprint

1

0

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Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

show abstract

“…Their models outperformed previous models with an overall 0.66%. A Super resolution and segmentation using generative adversarial networks is a framework introduced by [31] to neonatal brain M RI. It consists training a generating network that estimates for a given input image to its corresponding HR, and a discriminator network D is designed to distinguish real HR and segmentation images.…”

Section: B Generative Adversarial Networkmentioning

confidence: 99%

Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Network

Khaled¹,

Han²

2021

Preprint

1

0

View full text Add to dashboard Cite

Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

show abstract

SegSRGAN: Super-resolution and segmentation using generative adversarial networks — Application to neonatal brain MRI

Cited by 71 publications

References 38 publications

Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning

Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning

Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Network

Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Network

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