DeepMQ: A deep learning approach based myelin quantification in microscopic fluorescence images

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dc.authorid0000-0003-1106-3288
dc.contributor.authorÇimen, Sibel
dc.contributor.authorÇapar, Abdülkerim
dc.contributor.authorEkinci, Dursun Ali
dc.contributor.authorAyten, Umut Engin
dc.contributor.authorKerman, Bilal Ersen
dc.contributor.authorTöreyin, Behçet Uğur
dc.date.accessioned10.07.201910:49:13
dc.date.accessioned2019-07-10T19:37:11Z
dc.date.available10.07.201910:49:14
dc.date.available2019-07-10T19:37:11Z
dc.date.issued2018
dc.departmentİstanbul Medipol Üniversitesi, Uluslararası Tıp Fakültesi, Temel Tıp Bilimleri Bölümü, Histoloji ve Embriyoloji Ana Bilim Dalı
dc.description.abstractOligodendrocytes wrap around the axons and form the myelin. Myelin facilitates rapid neural signal transmission. Any damage to myelin disrupts neuronal communication leading to neurological diseases such as multiple sclerosis (MS). There is no cure for MS. This is, in part, due to lack of an efficient method for myelin quantification during drug screening. In this study, an image analysis based myelin sheath detection method, DeepMQ, is developed. The method consists of a feature extraction step followed by a deep learning based binary classification module. The images, which were acquired on a confocal microscope contain three channels and multiple z-sections. Each channel represents either oligodendroyctes, neurons, or nuclei. During feature extraction, 26-neighbours of each voxel is mapped onto a 2D feature image. This image is, then, fed to the deep learning classifier, in order to detect myelin. Results indicate that 93.38% accuracy is achieved in a set of fluorescence microscope images of mouse stem cell-derived oligodendroyctes and neurons. To the best of authors' knowledge, this is the first study utilizing image analysis along with machine learning techniques to quantify myelination.
dc.description.sponsorship316S026en_US
dc.description.sponsorshipEuropean Association for Signal Processingen_US
dc.description.sponsorshipIEEE Signal Processing Societyen_US
dc.description.sponsorshipMathWorksen_US
dc.description.sponsorshipAmazon Devicesen_US
dc.identifier.citationÇimen, S., Çapar, A., Ekinci, D. A., Ayten, U. E., Kerman, B. E. ve Töreyin, B. U. (2018). DeepMQ: A deep learning approach based myelin quantification in microscopic fluorescence images. European Signal Processing Conference (EUSIPCO) içinde (61-65. ss.). Rome, Italy, 3-7 September, 2018. https://dx.doi.org/10.23919/EUSIPCO.2018.8553438
dc.identifier.doi10.23919/EUSIPCO.2018.8553438
dc.identifier.endpage65
dc.identifier.isbn9789082797015
dc.identifier.issn2219-5491
dc.identifier.issn2076-1465
dc.identifier.scopusqualityN/A
dc.identifier.startpage61
dc.identifier.urihttps://hdl.handle.net/20.500.12511/1348
dc.identifier.urihttps://dx.doi.org/10.23919/EUSIPCO.2018.8553438
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE Computer Society
dc.relation.ecinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/316S026
dc.relation.ispartofEuropean Signal Processing Conference (EUSIPCO)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDeep Learning
dc.subjectLeNet
dc.subjectMicroscopic Fluorescence Imaging
dc.subjectMyelin
dc.subjectNeural Network
dc.titleDeepMQ: A deep learning approach based myelin quantification in microscopic fluorescence images
dc.typeConference Object

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