Version 1
: Received: 10 August 2022 / Approved: 10 August 2022 / Online: 10 August 2022 (05:04:02 CEST)
How to cite:
Khaled, A. Transfer Learning using Generative Adversarial Networks for MRI Brain Image Segmentation. Preprints2022, 2022080192. https://doi.org/10.20944/preprints202208.0192.v1
Khaled, A. Transfer Learning using Generative Adversarial Networks for MRI Brain Image Segmentation. Preprints 2022, 2022080192. https://doi.org/10.20944/preprints202208.0192.v1
Khaled, A. Transfer Learning using Generative Adversarial Networks for MRI Brain Image Segmentation. Preprints2022, 2022080192. https://doi.org/10.20944/preprints202208.0192.v1
APA Style
Khaled, A. (2022). Transfer Learning using Generative Adversarial Networks for MRI Brain Image Segmentation. Preprints. https://doi.org/10.20944/preprints202208.0192.v1
Chicago/Turabian Style
Khaled, A. 2022 "Transfer Learning using Generative Adversarial Networks for MRI Brain Image Segmentation" Preprints. https://doi.org/10.20944/preprints202208.0192.v1
Abstract
Segmentation is an important step in medical imaging. In particular, machine learning, especially deep learning, has been widely used to efficiently improve and speed up the segmentation process in clinical practice. Despite the acceptable segmentation results of multi-stage models, little attention was paid to the use of deep learning algorithms for brain image segmentation, which could be due to the lack of training data. Therefore, in this paper, we propose a Generative Adversarial Network (GAN) model that performs transfer learning to segment MRI brain images.Our model enables the generation of more labeled brain images from existing labeled and unlabeled images. Our segmentation targets brain tissue images, including white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). We evaluate the performance of our GAN model using a commonly used evaluation metric, which is Dice Coefficient (DC). Our experimental results reveal that our proposed model significantly improves segmentation results compared to the standard GAN model. We observe that our model is 2.1–10.83 minutes faster than stat-of-the-art-models.
Keywords
Transfer Learning; Generative Adversarial Networks; MRI Brain Images
Subject
Engineering, Automotive Engineering
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.
