Generating Synthetic Images from Real MR Images Using Deep Learning Methods
Ercüment Güvenç, Gürcan Çetin, Mevlüt Ersoy · Gazi Journal of Engineering Sciences · 2023
One of the most important technological developments in the field of medicine is Computed Tomography and Magnetic Resonance imaging techniques.This technique allows the size and shape of tumor areas in body tissues to be determined, making it easier for specialists to determine the type of tumor as well as whether it is benign or malignant.Various deep learning-based computer software have been developed to accurately detect tumor areas in tissue.Due to the lack of image data used in deep learning studies, a limitation naturally arises in studies in this field.In order to eliminate the lack of image data in these studies, image augmentation can be performed using deep learning methods as well as data augmentation methods using various image processing techniques.In this study, Generative Adversarial Networks, a deep learning technique, were employed to duplicate brain MR images and generate synthetic images.After the resulting MR images were made usable by undergoing various pre-processing, similarity rates to real images were calculated using metrics such as Peak Signal-to-Noise Ratio, Structural similarity index and Mean Square Error, and by looking at these rates, realistic images were added to the data set and the data set was expanded.