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Features for the analysis and classification of cells in holographic microscope images
Navrátilová, Markéta ; Kolář, Radim (referee) ; Vičar, Tomáš (advisor)
This thesis deals with features used for analysis and classification of cell images captured by holographic microscope. Distinctive features are described together with tools for their classification. Features are extracted on provided segmented cells with use of Matlab programming environment. Based on extracted features the cells are classified by SVM classificator. With use of clustering methods and dimensionality reduction different cell types are analyzed. Reliabity of each feature is tested.
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Detection of pathologies in retinal images
Mesíková, Klaudia ; Kolář, Radim (referee) ; Vičar, Tomáš (advisor)
The goal of this thesis is to design and implement software for the detection of diabetes mellitus symptoms from the image of the human eye retina. Diabetic retinopathy is the most common disease affecting the retina. Pathologies connected with this disease can lead to partial or complete blindness. For the detection of pathological symptoms is important to correctly detect some parts of the eye retina such as optic disc and blood vessels. These can cause a problem with the identification of disease. After removing the optic disc and blood vessels, the pathology object is being detected.
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Quantitative Digital Holographic Microscopy using machine learning
Duša, Martin ; Kolář, Radim (referee) ; Vičar, Tomáš (advisor)
This thesis presents machine learning methods for determining the parameters of micro and nano particles from digital holographic microscopy images. In the theoretical part the principles of hologram imaging, holographic microscopy and the similarity between Mie theory and hologram are presented. The second part of the theoretical review is devoted to machine learning methods used in determining the quantitative information of particles. The practical part is focused on the design of a procedure for determining the position, refractive index and radius using the U-Net architecture implemented in PyTorch and DeepTrack 2.1. The results of the proposed methodologies are discussed at the end of the paper.
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Cell segmentation using convolutional neural networks
Hrdličková, Alžběta ; Chmelík, Jiří (referee) ; Vičar, Tomáš (advisor)
This work examines the use of convolutional neural networks with a focus on semantic and instance segmentation of cells from microscopic images. The theoretical part contains a description of deep neural networks and a summary of widely used convolutional architectures for image segmentation. The practical part of the work is devoted to the creation of a convolutional neural network model based on the U-Net architecture. It also contains cell segmentation of predicted images using three methods, namely thresholding, the watershed and the random walker.
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Detection and measurement of electron beam in TEM images
Polcer, Simon ; Vičar, Tomáš (referee) ; Chmelík, Jiří (advisor)
This diploma thesis deals with automatic detection and measurement of the electron beam in the images from a transmission electron microscope (TEM). The introduction provides a description of the construction and the main parts of the electron microscope. In the theoretical part, there are summarized modes of illumination from the fluorescent screen. Machine learning, specifically convolution neural network U-Net is used for automatic detection of the electron beam in the image. The measurement of the beam is based on ellipse approximation, which defines the size and dimension of the beam. Neural network learning requires an extensive database of images. For this purpose, the own augmentation approach is proposed, which applies a specific combination of geometric transformations for each mode of illumination. In the conclusion of this thesis, the results are evaluated and summarized. This proposed algorithm achieves 0.815 of the DICE coefficient, which describes an overlap between two sets. The thesis was designed in Python programming language.
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Cell tracking in images from holographic microscope
Vičar, Tomáš ; Odstrčilík, Jan (referee) ; Kolář, Radim (advisor)
This thesis focuses on cell tracking in image sequences acquired using a multimodal holographic microscope (MHM). The principles of holographic microscopy are described together with the application in cells acquisition. The main part of the thesis describes a complete approach for segmentation and tracking of single cells in acquired in long-term sequences. The approach is designed based on parametric active contour models with specific modifications to achieve reasonable precision and robustness. The implemented method is described in detail, including the evaluation and demonstration of results.
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Blood vessel segmentation in retinal images using deep learning approaches
Serečunová, Stanislava ; Vičar, Tomáš (referee) ; Kolář, Radim (advisor)
This diploma thesis deals with the application of deep neural networks with focus on image segmentation. The theoretical part contains a description of deep neural networks and a summary of widely used convolutional architectures for segmentation of objects from the image. Practical part of the work was devoted to testing of an existing network architectures. For this purpose, an open-source software library Tensorflow, implemented in Python programming language, was used. A frequent problem incorporating the use of convolutional neural networks is the requirement on large amount of input data. In order to overcome this obstacle a new data set, consisting of a combination of five freely available databases was created. The selected U-net network architecture was tested by first modification of the newly created data set. Based on the test results, the chosen network architecture has been modified. By these means a new network has been created achieving better performance in comparison to the original network. The modified architecture is then trained on a newly created data set, that contains images of different types taken with various fundus cameras. As a result, the trained network is more robust and allows segmentation of retina blood vessels from images with different parameters. The modified architecture was tested on the STARE, CHASE, and HRF databases. Results were compared with published segmentation methods from literature, which are based on convolutional neural networks, as well as classical segmentation methods. The created network shows a high success rate of retina blood vessels segmentation comparable to state-of-the-art methods.
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Detection of persons and evaluation of gender and age in image data
Dobiš, Lukáš ; Vičar, Tomáš (referee) ; Kolář, Radim (advisor)
Táto diplomová práca sa venuje automatickému rozpoznávaniu ludí v obrazových dátach s využitím konvolučných neurónových sieti na určenie polohy tváre a následnej analýze získaných dát. Výsledkom analýzy tváre je určenie pohlavia, emócie a veku osoby. Práca obsahuje popis použitých architektúr konvolučných sietí pre každú podúlohu. Sieť na odhad veku má natrénované nové váhy, ktoré sú vzápätí zmrazené a majú do svojej architektúry vložené LSTM vrstvy. Tieto vrstvy sú samostatne dotrénované a testované na novom datasete vytvorenom pre tento účel. Výsledky testov ukazujú zlepšenie predikcie veku. Riešenie pre rýchlu, robustnú a modulárnu detekciu tváre a ďalších ludských rysov z jedného obrazu alebo videa je prezentované ako kombinácia prepojených konvolučných sietí. Tieto sú implementované v podobe skriptu a následne vysvetlené. Ich rýchlosť je dostatočná pre ďalšie dodatočné analýzy tváre na živých obrazových dátach.
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Analysis of Microscopic Images of Cancer Cells
Vičar, Tomáš ; Matula,, Petr (referee) ; Sladoje, Natasa (referee) ; Kolář, Radim (advisor)
Tato disertační práce je zaměřena na analýzu různých forem mikroskopických obrazových dat nádorových buněk (statické 2D snímky, statické 3D obrazy, 2D časosběrné zobrazování živých buněk). Hlavní pozornost je věnována datům získaným koherencí řízeným holografickým mikroskopem, který je relativně novou modalitou schopnou kotrastních záznamů živých buněk bez barvení (label-free) a poskytuje kvantitativní informaci (kvantitativní fázové zobrazení - QPI). V práci je popsán základní postup analýzy těchto snímků a jsou vytvářeny nové metody a zdokonalovány metody pro jednotlivé kroky této analýzy. Největší část práce je věnována segmentaci buněk, kde jsou shrnuty klasické metody i metody založené na hlubokém učení. Jsou také vyvinuty nové metody vhodné právě pro QPI data. Část práce je také věnována segmentaci 3D fluorescenční jader a detekci DNA zlomů pomocí hlubokého učení. Práce se zabývá i dalším zpracování v podobě sledování buněk, extrakce příznaků a následné analýze, kde je detekována buněčná smrt a jsou vytvořeny vhodné interpretovatelné příznaky pro klasifikaci buněčné smrti na apoptickou a lytickou. Celkově tato práce přispívá k rozvoji jednotlivých kroků analýzy obrazu nádorových buněk a odráží současný pokrok v oblasti analýzy obrazu, zejména přístupy hlubokého učení, což je také demonstrováno na několika výzkumných aplikacích.
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