Version 1
: Received: 31 January 2022 / Approved: 1 February 2022 / Online: 1 February 2022 (13:34:28 CET)
How to cite:
Khaled, A. MRI-GAN: MRI and Tissues Transfer Using Generative Adversarial Networks. Preprints2022, 2022020015. https://doi.org/10.20944/preprints202202.0015.v1
Khaled, A. MRI-GAN: MRI and Tissues Transfer Using Generative Adversarial Networks . Preprints 2022, 2022020015. https://doi.org/10.20944/preprints202202.0015.v1
Khaled, A. MRI-GAN: MRI and Tissues Transfer Using Generative Adversarial Networks. Preprints2022, 2022020015. https://doi.org/10.20944/preprints202202.0015.v1
APA Style
Khaled, A. (2022). MRI-GAN: MRI and Tissues Transfer Using Generative Adversarial Networks<strong> </strong>. Preprints. https://doi.org/10.20944/preprints202202.0015.v1
Chicago/Turabian Style
Khaled, A. 2022 "MRI-GAN: MRI and Tissues Transfer Using Generative Adversarial Networks<strong> </strong>" Preprints. https://doi.org/10.20944/preprints202202.0015.v1
Abstract
We study the brain segmentation by dividing the brain into multiple tissues. Given possible brain segmentation by deep, machine learning can be efficiently exploited to expedite the segmentation process in the clinical practice. To accomplish segmentation process, a MRI and tissues transfer using generative adversarial networks is proposed. Given the better result, we propose the transfer model using GAN. For the case of the brain tissues, white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) are segmented. Empirical results show that this proposed model significantly improved segmentation results compared to the stat-of-the-art results. Furthermore, a dice coefficient (DC) metric is used to evaluate the model performance.
Keywords
Deep learning; Machine learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
