Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks

Kwon, Gihyun; Han, Chihye; Kim, Dae-shik

doi:10.48550/arxiv.1908.02498

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…In conclusion, it can be asserted that the proposed 3D GAN model has the potential to be employed for both image segmentation and image data augmentation. The proposed model has the potential to be employed in conjunction with a 3D noise-to-image GAN model, such as those referenced in citations [38][39][40]. This combination allows for the integration of authentic segmentation outputs, which may then be transformed into realistic MR volumes.…”

Section: Discussionmentioning

confidence: 99%

Segmentation of Brain Tumor Using a 3D Generative Adversarial Network

Kalejahi,

Meshgini,

Danishvar

2023

Diagnostics

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Images of brain tumors may only show up in a small subset of scans, so important details may be missed. Further, because labeling is typically a labor-intensive and time-consuming task, there are typically only a small number of medical imaging datasets available for analysis. The focus of this research is on the MRI images of the human brain, and an attempt has been made to propose a method for the accurate segmentation of these images to identify the correct location of tumors. In this study, GAN is utilized as a classification network to detect and segment of 3D MRI images. The 3D GAN network model provides dense connectivity, followed by rapid network convergence and improved information extraction. Mutual training in a generative adversarial network can bring the segmentation results closer to the labeled data to improve image segmentation. The BraTS 2021 dataset of 3D images was used to compare two experimental models.

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Section: Discussionmentioning

confidence: 99%

Segmentation of Brain Tumor Using a 3D Generative Adversarial Network

Kalejahi,

Meshgini,

Danishvar

2023

Diagnostics

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“…Three dimensional brain images, from healthy data but also with tumors and strokes, could be generated with different GAN architectures. [15] A different paper employed a Vector-Quantised Variational Autoencoders (VQ-VAE) on 3 dimensional MRI data. [23] They achieved impressive results in image compression and reconstruction, but didn't do an analysis of the latent space nor did they show newly sampled brains only reconstructed ones.…”

Section: Related Workmentioning

confidence: 99%

Latent Space Analysis of VAE and Intro-VAE applied to 3-dimensional MR Brain Volumes of Multiple Sclerosis, Leukoencephalopathy, and Healthy Patients

Christopher¹,

Federau²

2021

Preprint

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Multiple Sclerosis (MS) and microvascular leukoencephalopathy are two distinct neurological conditions, the first caused by focal autoimmune inflammation in the central nervous system, the second caused by chronic white matter damage from atherosclerotic microvascular disease. Both conditions lead to signal anomalies on Fluid Attenuated Inversion Recovery (FLAIR) magnetic resonance (MR) images, which can be distinguished by an expert neuroradiologist, but which can look very similar to the untrained eye as well as in the early stage of both diseases. In this paper, we attempt to train a 3-dimensional deep neural network to learn the specific features of both diseases in an unsupervised manner. For this manner, in a first step we train a generative neural network to create artificial MR images of both conditions with approximate explicit density, using a mixed dataset of multiple sclerosis, leukoencephalopathy and healthy patients containing in total 5404 volumes of 3096 patients. In a second step, we distinguish features between the different diseases in the latent space of this network, and use them to classify new data.

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Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks

Cited by 2 publications

References 11 publications

Segmentation of Brain Tumor Using a 3D Generative Adversarial Network

Segmentation of Brain Tumor Using a 3D Generative Adversarial Network

Latent Space Analysis of VAE and Intro-VAE applied to 3-dimensional MR Brain Volumes of Multiple Sclerosis, Leukoencephalopathy, and Healthy Patients

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