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Öğe A multi-spectral myelin annotation tool for machine learning based myelin quantification [version 1; peer review: 1 not approved](F1000 Research Ltd, 2021) Çapar, Abdulkerim; Çimen Yetiş, Sibel; Aladağ, Zeynep; Ekinci, Dursun Ali; Ayten, Umut Engin; Kerman, Bilal Ersen; Töreyin, Behçet UğurMyelin is an essential component of the nervous system and myelin damage causes demyelination diseases. Myelin is a sheet of oligodendrocyte membrane wrapped around the neuronal axon. In the fluorescent images, experts manually identify myelin by colocalization of oligodendrocyte and axonal membranes that fit certain shape and size criteria. Because myelin wriggles along x-y-z axes, machine learning is ideal for its segmentation. However, machinelearning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitates expert labor. To facilitate myelin annotation, we developed a workflow and a software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, we shared a set of myelin ground truths annotated using this workflow.Öğe DeepMQ: A deep learning approach based myelin quantification in microscopic fluorescence images(IEEE Computer Society, 2018) Çimen, Sibel; Çapar, Abdülkerim; Ekinci, Dursun Ali; Ayten, Umut Engin; Kerman, Bilal Ersen; Töreyin, Behçet UğurOligodendrocytes 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.Öğe Floresan mikroskop görüntülerinde miyelin segmentasyonu(Institute of Electrical and Electronics Engineers Inc., 2019) Yetiş, Sibel Çimen; Ekinci, Dursun Ali; Çakır, Ertan; Ekşioglu, Ender Mete; Ayten, Umut Engin; Çapar, Abdülkerim; Töreyin, Behçet Uğur; Kerman, Bilal ErsenAksonların etrafına sarılmış¸ miyelin kılıf, hızlı bir şekilde sinyal iletimini sağlar ve deformasyonu, Multipl Skleroz (MS) gibi çeşitli nörodejeneratif hastalıklara neden olur. Aday ilaç geliştirilmesi için, miyelinizasyonun miktarının belirlenebiliyor olması gerekmektedir. Miyelin nicelleştirilmesi, genellikle konfokal mikroskoplar tarafından elde edilen mikroskopik ?oresan görüntülerinde bir uzman tarafından miyelin etiketleme temeline dayanan ve yoğun emek gerektiren bir iştir. Bu çalışmada, ?oresan mikroskopi görüntülerinde anlamsal bölütlemeye dayalı bir otomatik miyelin belirleme yöntemi geliştirilmiştir. Üç kanallı ve üç boyutlu olarak mikroskoptan alınan, fare kök hücresinden türetilmiş¸ nöron ve oligodendrosit ortak kültürlerinin görüntüleri bir uzman tarafından etiketlenmiştir. Alınan görüntüler e?gitim için yamalara ayrılmıs¸ ve etiketlerden de her yamanın karşılığı elde edilmiştir. Miyelin içeren ve içermeyen bögeleri tanımlamak üzere eğitim işlemi için 11552 yamadan olus¸an bir veri kümesi kullanılmıştır. Veri kümesinde çeşitli öğrenme algoritmaları kullanılarak anlamsal bölütleme tekniğinin miyelin tespit performansları değerlendirilmiştir. En yüksek doğruluk değeri olan yüzde 97.32, grup boyutu 8 ve devir sayısı 250 iken “RMSprop” öğrenme algoritması ile elde edilmiştir. Sonuçlarımız, önerilen otomatik segmentasyon yaklaşımının miyelin tespiti için uygun olduğunu göstermektedir. Burada açıklanan miyelin segmentasyon yaklaşımı, remiyelinizasyon ilaç taramalarının bir parçası haline gelecek potansiyele sahiptir.Öğe Myelin detection in fluorescence microscopy images using machine learning(Elsevier, 2020) Çimen Yetiş, Sibel; Çapar, Abdulkerim; Ekinci, Dursun Ali; Ayten, Umut Engin; Kerman, Bilal Ersan; Töreyin, Behçet UğurBackground: The myelin sheath produced by glial cells insulates the axons, and supports the function of the nervous system. Myelin sheath degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS). There are no therapies for MS that promote remyelination. Drug discovery frequently involves screening thousands of compounds. However, this is not feasible for remyelination drugs, since myelin quantification is a manual labor-intensive endeavor. Therefore, the development of assistive software for expedited myelin detection is instrumental for MS drug discovery by enabling high-content image-based drug screens.New method: In this study, we developed a machine learning based expedited myelin detection approach in fluorescence microscopy images. Multi-channel three-dimensional microscopy images of a mouse stem cell-based myelination assay were labeled by experts. A spectro-spatial feature extraction method was introduced to represent local dependencies of voxels both in spatial and spectral domains. Feature extraction yielded two data set of over forty-seven thousand annotated images in total.Results: Myelin detection performances of 23 different supervised machine learning techniques including a customized-convolutional neural network (CNN), were assessed using various train/test split ratios of the data sets. The highest accuracy values of 98.84 +/- 0.09% and 98.46 +/- 0.11% were achieved by Boosted Trees and customized-CNN, respectively.Comparison with existing methods: Our approach can detect myelin in a common experimental setup. Myelin extending in any orientation in 3 dimensions is segmented from 3 channel z-stack fluorescence images.Conclusions: Our results suggest that the proposed expedited myelin detection approach is a feasible and robust method for remyelination drug screening.
