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Analysis of Microscopic Images of Cancer Cells
Vičar, Tomáš ; Matula,, Petr (oponent) ; Sladoje, Natasa (oponent) ; Kolář, Radim (vedoucí práce)
This dissertation focuses on the analysis of various forms of microscopic image data of cancer cells (static 2D images, static 3D stacks, 2D timelapse live cell imaging). The main focus is on data acquired with a~coherence controlled holographic microscope, which is a~relatively new modality capable of contrast imaging of live cells without staining (label-free) and provide quantitative information (Quantitative Phase Imaging - QPI). In this thesis, the basic procedure for the analysis of cell images is described, where new methods for the individual steps are developed and refined. The largest part of the thesis is devoted to cell segmentation, where classical and deep learning-based methods are summarized. New methods suitable specifically for QPI data are also developed. A~part of the thesis is devoted to the segmentation of 3D fluorescence nuclei and the detection of DNA breaks using deep learning. The thesis also deals with further processing in the form of cell tracking, feature extraction and subsequent analysis, where cell death is detected and suitable interpretable features are developed to classify cell death into apoptotic and lytic. Overall, this thesis contributes to the development of different steps of image analysis of cancer cells and reflects current advances in the image analysis field, deep learning approaches in particular, which is also demonstrated in several research applications.
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Analysis of Microscopic Images of Cancer Cells
Vičar, Tomáš ; Matula,, Petr (oponent) ; Sladoje, Natasa (oponent) ; Kolář, Radim (vedoucí práce)
This dissertation focuses on the analysis of various forms of microscopic image data of cancer cells (static 2D images, static 3D stacks, 2D timelapse live cell imaging). The main focus is on data acquired with a~coherence controlled holographic microscope, which is a~relatively new modality capable of contrast imaging of live cells without staining (label-free) and provide quantitative information (Quantitative Phase Imaging - QPI). In this thesis, the basic procedure for the analysis of cell images is described, where new methods for the individual steps are developed and refined. The largest part of the thesis is devoted to cell segmentation, where classical and deep learning-based methods are summarized. New methods suitable specifically for QPI data are also developed. A~part of the thesis is devoted to the segmentation of 3D fluorescence nuclei and the detection of DNA breaks using deep learning. The thesis also deals with further processing in the form of cell tracking, feature extraction and subsequent analysis, where cell death is detected and suitable interpretable features are developed to classify cell death into apoptotic and lytic. Overall, this thesis contributes to the development of different steps of image analysis of cancer cells and reflects current advances in the image analysis field, deep learning approaches in particular, which is also demonstrated in several research applications.
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Segmentation of cells from microscopic images
Lašan, Michal ; Soukup, Jindřich (vedoucí práce) ; Blažek, Jan (oponent)
V této práci prezentujeme novou metodu na automatickou segmentaci savčích rakovinových buněk z časozběrných snímků pořízených mikroskopem na bázi fázového kontrastu. Tato metoda je sledem kroků založených na základních technikách z oblasti zpracování obrazu, matematické morfologie a teorie grafů. Její hlavní myšlenkou je využít přítomnosti halo artefaktů kolem buněk, díky nimž jsou hranice mezi buňkama světlejší než zbytek obrázku. Navazuje na metodu navrženou Jindřichem Soukupem, která umí oddělovat buňky od pozadí. Srovnáme tuto metodu s watershed - věřejně dostupným algoritmem z odvětví matematické morfologie. Jako referenci použijeme segmentaci lidským expertem. Prezentována metoda je implementována v MATLABu a Javě s jednoduchým a intuitivním rozhraním. Také připojujeme přímočarý editor segmentace, pomocí něhož užívatel může napravit nepřesnosti segmentace, nebo dokonce vytvořit svou vlastní manuální segmentaci. Powered by TCPDF (www.tcpdf.org)
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