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Detection of cells in confocal microscopy images
Hubálek, Michal ; Štursa, Dominik (referee) ; Škrabánek, Pavel (advisor)
The goal of the thesis was to create an application that automatically detects healthy cardiomyocytes from images captured by a confocal microscope. The thesis was created based on the specific needs of researchers from the Slovak Academy of Sciences.The application will facilitate and increase the efficiency of their research,because until now they have to evaluate the images and search for suitable cells manually. The RetinaNet convolutional neural network is used for detection and has been implemented in a user-friendly desktop application. The application also automatically records and stores coordinates of detected cells which can be used for capturing cells in higher image quality. Another advantage of the developed application is its versatility, which allows to train detection on other data, making it applicable to other projects. The result of this work is a functional, standalone and intuitive application that is ready to be used by researchers.
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Classification of marked cells migration in tissue
Solař, Jan ; Skopalík, Josef (referee) ; Čmiel, Vratislav (advisor)
This diploma thesis deals with analysing of modern methods for cell detection, visualization and quantification in 3D space. The first section deals with optical methods for cells detection. There is detailed discussion about cell labeling and detection on confocal microscopy. There is also description about developed algorithm for whole cell volume quantification from microscopy images. This could made a comparsion of fluorescence signal according to time of cell labeling and according to cell shapes. There was also optimalization of handmade tissue phantoms visualization. It could be compared a possibilities of cell detections in these phantoms by confocal microscopy and OCT. It was also implemented algorithm for quantification of cells from OCT images. Besides confocal microscopy and OCT cells are also analyzed by other methods. The last part is the Conclusion of results and comparison of used methods.
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Cell segmentation from wide-field light microscopy images using CNNs
GHAZNAVI, Ali
Image object segmentation allows localising the region of interest in the image (ROI) and separating the foreground from the background. Cell detection and segmentation are the primary and critical steps in microscopy image analysis. Analysing microscopy images allows us to extract vital information about the cells, including their morphology, size, and life cycle. On the other hand, living cell segmentation is challenging due to the complexity of these datasets. This research focused on developing Artificial Intelligence/Machine Learning methods of single- and multi-class segmentation of living cells. For this study, the Negroid cervical epithelioid carcinoma HeLa line was chosen as the oldest, immortal, and most widely used model cell line. Several time-lapse image series of living HeLa cells were captured using a high-resolved wide-field transmitted/reflected light microscope (custom-made for the Institute of Complex System, Nové Hrady, Czech Republic) to observe micro-objects and cells. Employing a telecentric objective with a high-resolution camera with a large sensor size allows us to achieve a high level of detail and sharper borders in large microscopy images. The collected time-lapse images were calibrated and denoised in the pre-processing step. The data sets collected under the transmission microscope setup were analyzed using a simple U-Net, Attention U-Net, and Residual Attention U-Net to achieve the best single-class semantic segmentation result. The data sets collected under the reflection microscope setup were analyzed using hybrid U-Net methods, including Vgg19-Unet, Inception-Unet, and ResNet34-Unet, to achieve the most precise multi-class segmentation result.
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Detection of cells in confocal microscopy images
Hubálek, Michal ; Štursa, Dominik (referee) ; Škrabánek, Pavel (advisor)
The goal of the thesis was to create an application that automatically detects healthy cardiomyocytes from images captured by a confocal microscope. The thesis was created based on the specific needs of researchers from the Slovak Academy of Sciences.The application will facilitate and increase the efficiency of their research,because until now they have to evaluate the images and search for suitable cells manually. The RetinaNet convolutional neural network is used for detection and has been implemented in a user-friendly desktop application. The application also automatically records and stores coordinates of detected cells which can be used for capturing cells in higher image quality. Another advantage of the developed application is its versatility, which allows to train detection on other data, making it applicable to other projects. The result of this work is a functional, standalone and intuitive application that is ready to be used by researchers.
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Cell Detection Methods For The Images From Holographic Microscope
Vičar, Tomáš
Microscopical cell image analysis is widely used for cell behavior and morphology study. In dense cell cultures precise detection (separation) of a single cell is challenging task and it is important step for automatic cell analysis methods. There are a variety of methods, but most of them are less accurate for non-circular cells. This paper describes the common approaches for cell detection applied on images from holographic microscope. Linear discriminant analysis is used for combining results of these methods to obtain new more precise and robust approach.
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Classification of marked cells migration in tissue
Solař, Jan ; Skopalík, Josef (referee) ; Čmiel, Vratislav (advisor)
This diploma thesis deals with analysing of modern methods for cell detection, visualization and quantification in 3D space. The first section deals with optical methods for cells detection. There is detailed discussion about cell labeling and detection on confocal microscopy. There is also description about developed algorithm for whole cell volume quantification from microscopy images. This could made a comparsion of fluorescence signal according to time of cell labeling and according to cell shapes. There was also optimalization of handmade tissue phantoms visualization. It could be compared a possibilities of cell detections in these phantoms by confocal microscopy and OCT. It was also implemented algorithm for quantification of cells from OCT images. Besides confocal microscopy and OCT cells are also analyzed by other methods. The last part is the Conclusion of results and comparison of used methods.
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