Brain MRI high resolution image creation and segmentation with the new GAN method

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dc.authoridALTUN GUVEN, SARA/0000-0003-2877-7105
dc.contributor.authorGuven, Sara Altun
dc.contributor.authorTalu, Muhammed Fatih
dc.date.accessioned2024-10-04T18:48:27Z
dc.date.available2024-10-04T18:48:27Z
dc.date.issued2023
dc.departmentBayburt Üniversitesien_US
dc.description.abstractBrain magnetic resonance imaging segmentation is a recent and still popular research area. Good and accurate segmentation results play an important role in the diagnosis of cancer or other brain diseases. In this article, a novel Generative Adversarial Network architecture is proposed for brain magnetic resonance imaging segmentation. The proposed method is called SSimDCL (Supervised SimDCL). Four studies were conducted in this article. In the first study, the SSimDCL method on the two-dimensional brain magnetic resonance imaging dataset was compared with the current state-of-art architectures CycleGAN, CUT, FastCUT, DCLGAN, and SimDCL. In the second study, the dataset resolution was improved. In the third study, being measured the efficiency of the newly created dataset. And the SSimDCL is trained for both the dataset with increased resolution and the normal dataset, and the results are obtained. In the fourth study, the results of the SSimDCL and the VolBrain brain magnetic resonance imaging segmentation results, which are widely used today, are included. When VolBrain segmentation and SSimDCL segmentation are compared. The results were compared both visually and metrically. Fre ' chet Inception Distance (FID), Kernel Inception Distance (KID), Peak Signal to Noise Ratio (PSNR) and Learned Perceptual Image Patch Similarity (LPIPS) were used as measurement metrics. The Jaccard and Dice similarity metrics were also used in the analysis. It was observed that the SSimDCL give satisfactory results in all four studies. This method can be used as an automatic brain MRI image segmentation system.en_US
dc.description.sponsorshipScientific Research and Coordination Unit of Inonu University [FDK-2021-2675]en_US
dc.description.sponsorshipThis study was financed by the Scientific Research and Coordination Unit of Inonu University with the ?FDK-2021-2675? project. We would like to thank Inonu University.en_US
dc.identifier.doi10.1016/j.bspc.2022.104246
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85140052982en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2022.104246
dc.identifier.urihttp://hdl.handle.net/20.500.12403/3057
dc.identifier.volume80en_US
dc.identifier.wosWOS:000880754500003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing and Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBrain MRIen_US
dc.subjectImage generationen_US
dc.subjectImage segmentationen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectGenerative Adversarial Networksen_US
dc.titleBrain MRI high resolution image creation and segmentation with the new GAN methoden_US
dc.typeArticleen_US

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