Deep learning based unsupervised tissue segmentation in histopathological images

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buir.advisorDemir, Çiğdem Gündüz
dc.contributor.authorKöylü, Troya Çağıl
dc.date.accessioned2017-11-17T09:45:58Z
dc.date.available2017-11-17T09:45:58Z
dc.date.copyright2017-11
dc.date.issued2017-11
dc.date.submitted2017-11-16
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2017.en_US
dc.descriptionIncludes bibliographical references (leaves 58-61).en_US
dc.description.abstractIn the current practice of medicine, histopathological examination of tissues is essential for cancer diagnosis. However, this task is both subject to observer variability and time consuming for pathologists. Thus, it is important to develop automated objective tools, the first step of which usually comprises image segmentation. According to this need, in this thesis, we propose a novel approach for the segmentation of histopathological tissue images. Our proposed method, called deepSeg, is a two-tier method. The first tier transfers the knowledge from AlexNet, which is a convolutional neural network (CNN) trained for the non-medical domain of ImageNet, to the medical domain of histopathological tissue image characterization. The second tier uses this characterization in a seed-controlled region growing algorithm, for the unsupervised segmentation of heterogeneous tissue images into their homogeneous regions. To test the effectiveness of the segmentation, we conduct experiments on microscopic colon tissue images. Quantitative results reveal that the proposed method improves the performance of the previous methods that work on the same dataset. This study both illustrates one of the first successful demonstrations of using deep learning for tissue image segmentation, and shows the power of using deep learning features instead of handcrafted ones in the domain of histopathological image analysis.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2017-11-17T09:45:58Z No. of bitstreams: 1 mastersThesis_troyacagilkoylu_bilkent.pdf: 36435738 bytes, checksum: f955e7c988de6195b19541ebbc1b4dd5 (MD5)en
dc.description.provenanceMade available in DSpace on 2017-11-17T09:45:58Z (GMT). No. of bitstreams: 1 mastersThesis_troyacagilkoylu_bilkent.pdf: 36435738 bytes, checksum: f955e7c988de6195b19541ebbc1b4dd5 (MD5) Previous issue date: 2017-11en
dc.description.statementofresponsibilityby Troya Çağıl Köylü.en_US
dc.embargo.release2020-11-16
dc.format.extentx, 61 leaves : illustrations (some color), charts (some color) ; 30 cmen_US
dc.identifier.itemidB156816
dc.identifier.urihttp://hdl.handle.net/11693/33880
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep learningen_US
dc.subjectConvolutional neural networksen_US
dc.subjectTransfer learningen_US
dc.subjectHistopathological tissue image segmentationen_US
dc.subjectSeed-controlled region growingen_US
dc.titleDeep learning based unsupervised tissue segmentation in histopathological imagesen_US
dc.title.alternativeHistopatolojik görüntülerde derin öğrenme temelli öğreticisiz doku bölütlemesien_US
dc.typeThesisen_US
thesis.degree.disciplineComputer Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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