A new 3D MRI segmentation method based on Generative Adversarial Network and Atrous Convolution

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dc.authoridTalu, Muhammed Fatih/0000-0003-1166-8404
dc.authoridCelik, Gaffari/0000-0001-5658-9529
dc.authorwosidTalu, Muhammed Fatih/W-2834-2017
dc.contributor.authorCelik, Gaffari
dc.contributor.authorTalu, Muhammed Fatih
dc.date.accessioned2024-08-04T20:50:42Z
dc.date.available2024-08-04T20:50:42Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground and aim: Although many algorithms have been proposed to segment brain structures in MRI scans, comparison of different algorithms in the same data set is rarely done. Many methods still run on privately held data and include comparisons with previous versions. The purpose of this study is to introduce a new Generative Adversarial Network (GAN) based segmentation architecture. Brain tissues on 3D-MRI scans with T1w modality were segmented into three (GM, WM, CSF) and eight (CGM, BG, WM, WMH, CF, VE, CE ve BS) parts. Methods: The proposed approach (Vol2SegGAN) consists of two parts: preprocessing and segmentation. The preprocessing includes extraction of the brain region, editing of labels in the datasets, MNI152 registration, clipping/sampling. The segmentation part is carried out by the collaboration of a generator (includes ACFP and PAM modules) and a discriminator (distinguishes real/fake) architectures. Conclusions: In the three part segmentation process, the proposed method showed the best segmentation success in CSF (Dice = 0.739, VS = 0.967), GM (Dice = 0.878, HD = 2.378) and WM (HD = 2.105) tissues, and the second best segmentation success in WM (Dice = 0.793, VS = 0.972) tissue according to Dice and VS metrics. Similarly, in the segmentation of eight parts, it is seen that it has the best success according to VS and HD metrics and the second best according to Dice metric. Vol2SegGAN, which has fewer parameters (6,883 mil.) than existing architectures, has an average of 11-12 s (CPU) or 0-1 s (GPU) to segment a sample 3D-MRI. Implementation codes of the proposed architecture are available on the github page1.en_US
dc.identifier.doi10.1016/j.bspc.2021.103155
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85115887581en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2021.103155
dc.identifier.urihttps://hdl.handle.net/11616/100228
dc.identifier.volume71en_US
dc.identifier.wosWOS:000704940200005en_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 segmentationen_US
dc.subject3D MRI scansen_US
dc.subjectGANen_US
dc.subjectACFPen_US
dc.subjectPAMen_US
dc.titleA new 3D MRI segmentation method based on Generative Adversarial Network and Atrous Convolutionen_US
dc.typeArticleen_US

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