Utilizing Deep Convolutional Generative Adversarial Networks for Automatic Segmentation of Gliomas: An Artificial Intelligence Study

dc.contributor.authorAydogan Duman, Ebru
dc.contributor.authorSagiroglu, Seref
dc.contributor.authorCeltikci, Pinar
dc.contributor.authorDemirezen, Mustafa Umut
dc.contributor.authorBorcek, Alp Ozgun
dc.contributor.authorEmmez, Hakan
dc.contributor.authorCeltikci, Emrah
dc.contributor.pubmedID34542897en_US
dc.date.accessioned2022-11-22T10:12:22Z
dc.date.available2022-11-22T10:12:22Z
dc.date.issued2022
dc.description.abstractAIM: To describe a deep convolutional generative adversarial networks (DCGAN) model which learns normal brain MRI from normal subjects than finds distortions such as a glioma from a test subject while performing a segmentation at the same time. MATERIAL and METHODS: MRIs of 300 healthy subjects were employed as training set. Additionally, test data were consisting anonymized T2-weigted MRIs of 27 healthy subjects and 27 HGG patients. Consecutive axial T2-weigted MRI slices of every subject were extracted and resized to 364x448 pixel resolution. The generative model produced random normal synthetic images and used these images for calculating residual loss to measure visual similarity between input MRIs and generated MRIs. RESULTS: The model correctly detected anomalies on 24 of 27 HGG patients' MRIs and marked them as abnormal. Besides, 25 of 27 healthy subjects' MRIs in the test dataset detected correctly as healthy MRI. The accuracy, precision, recall, and AUC were 0.907, 0.892, 0.923, and 0.907, respectively. CONCLUSION: Our proposed model demonstrates acceptable results can be achieved only by training with normal subject MRIs via using DCGAN model. This model is unique because it learns only from normal MRIs and it is able to find any abnormality which is different than the normal pattern.en_US
dc.identifier.endpage21en_US
dc.identifier.issn1019-5149en_US
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85123239625en_US
dc.identifier.startpage16en_US
dc.identifier.urihttp://hdl.handle.net/11727/8140
dc.identifier.volume32en_US
dc.identifier.wos000768200100001en_US
dc.language.isoengen_US
dc.relation.isversionof10.5137/1019-5149.JTN.29217-20.2en_US
dc.relation.journalTURKISH NEUROSURGERYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectDeep learningen_US
dc.subjectGliomaen_US
dc.subjectMachine learningen_US
dc.subjectSegmentationen_US
dc.titleUtilizing Deep Convolutional Generative Adversarial Networks for Automatic Segmentation of Gliomas: An Artificial Intelligence Studyen_US
dc.typearticleen_US

Files

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: