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Segmentation of brain tumours in MRI images using deep learning
Ustsinau, Usevalad ; Odstrčilík, Jan (referee) ; Chmelík, Jiří (advisor)
The following master's thesis paper equipped with a short description of CT scans and MR images and the main differences between them, explanation of the structure of convolutional neural networks and how they implemented into biomedical image analysis, besides it was taken a popular modification of U-Net and tested on two loss-functions. As far as segmentation quality plays a highly important role for doctors, in experiment part it was paid significant attention to training quality and prediction results of the model. The experiment has shown the effectiveness of the provided algorithm and performed 100 training cases with the following analysis through the similarity. The proposed outcome gives us certain ideas for future improving the quality of image segmentation via deep learning techniques.
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Image data shape analysis
Kylián, Jakub ; Harabiš, Vratislav (referee) ; Chmelík, Jiří (advisor)
Bachelor thesis is focused on shape analysis of image data. This thesis clarifies usage of methods, techniques and procedures that lead to such analysis. The content of this thesis is divided into three chapters – theory, analysis solution and conclusion. In the chapter theory, methods and general theory of the topic are introduced. The rule is, that every method used in subsequent sections is explained in detail, but this doesn't necessarily mean that it applies in reverse. Next chapter, following up the theory section is analysis solution. In this section, a problem is presented. This problem is then, with knowledge presented in chapter theory solved. The solution consists of many processes and preparations that are also explained in detail in this chapter.
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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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Application of optimisation methods for MRI data segmentation
Olešová, Kristína ; Mézl, Martin (referee) ; Chmelík, Jiří (advisor)
This thesis deals with a segmentation of brain tissues from MRI image data and its implementation in MATLAB. Segmentation problematic is described with attention to formulating segmentation as optimization problem and segmentation of given images with different metaheuristic algorithm consequently. This approach was chosen due to information from last specialized publications, where it was accentuated for its fast computational speed and universality. This thesis tries to prove this statement with segmentation of brain images with brain tumours that have different types, number, stage of illness and phase of therapy.
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Using advanced segmentation methods for images from TEM microscopes
Mocko, Štefan ; Chmelík, Jiří (referee) ; Potočňák, Tomáš (advisor)
Tato magisterská práce se zabývá využitím konvolučních neuronových sítí pro segmentační účely v oblasti transmisní elektronové mikroskopie. Také popisuje zvolenou topologii neuronové sítě - U-NET, použíté augmentační techniky a programové prostředí. Firma Thermo Fisher Scientific (dříve FEI Czech Republic s.r.o) poskytla obrazová data pro účely této práce. Získané segmentační výsledky jsou prezentovány ve formě křivek (ROC, PRC) a ve formě numerických hodnot (ARI, DSC, Chybová matice). Zvolená UNET topologie dosáhla excelentních výsledků v oblasti pixelové segmentace. S největší pravděpodobností, budou tyto výsledky sloužit jako odrazový můstek pro interní firemní výzkum.
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Pre-registration of CT pulmonary volumetric image data
Šiška, Branislav ; Walek, Petr (referee) ; Chmelík, Jiří (advisor)
This bachelor thesis is dealing with pre-registration of CT pulmonary volumetric image data. Pre-registration is solved by phase correlation method, which decomposes 3D images into 2D slices arranged in a row. It further describes the geometric transformations, interpolation, calculations of similarity criteria, optimization of registration of images and the image registration process itself. The pre-registration software runs in MATLAB^®, which works with 3D images of real CT image data with an emphasis on process speed.
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Image data shape analysis
Kylián, Jakub ; Harabiš, Vratislav (referee) ; Chmelík, Jiří (advisor)
Bachelor thesis is focused on shape analysis of image data. This thesis clarifies usage of methods, techniques and procedures that lead to such analysis. The content of this thesis is divided into three chapters – theory, analysis solution and conclusion. In the chapter theory, methods and general theory of the topic are introduced. The rule is, that every method used in subsequent sections is explained in detail, but this doesn't necessarily mean that it applies in reverse. Next chapter, following up the theory section is analysis solution. In this section, a problem is presented. This problem is then, with knowledge presented in chapter theory solved. The solution consists of many processes and preparations that are also explained in detail in this chapter.
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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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Detection of biological structures in TEM microscope images
Cikánek, Martin ; Chmelík, Jiří (referee) ; Potočňák, Tomáš (advisor)
The aim of the first part of this thesis is to explain the theoretical basis of transmission electron microscopy and to mention fundamental parts of transmission electron microscopes. The next part of this work is focused on possible methods of image segmentation, the use of neural networks in the detection of objects in an image and the subsequent clustering of results. The theoretical part of the thesis is concluded with an explanation of some already published methods of automatic detection of biological structures in microscopic images and theoretical design of the algorithm, which will be subsequently developed. The process of training neural networks in order to automatically detect biological structures in an image is described at the beginning of the practical part. This is followed by an evaluation of the results achieved by these networks. Subsequently, cluster analysis methods are applied to these results, the products of which are compared with each other and also with the results obtained by already published methods.
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Processing of high-resolution retinal images
Vraňáková, Sofia ; Chmelík, Jiří (referee) ; Valterová, Eva (advisor)
Diplomová práca je zameraná na spracovávanie obrazov sietnice s vysokým rozlíšením. Cieľom práce je zlepšiť výslednú kvalitu výsledných snímkov sietnice získaných zo sekvencie snímkov nižšej kvality. Jednotlivé snímky sú najskôr spracované pomocou bilaterálnej filtrácie a zlepšenia kontrastu. v ďalšom kroku sú odstránené rozmazané snímky a snímky zobrazujúce iné časti sietnice. Posun medzi jednotlivými snímkami v sekvencii sa odhaduje pomocou fázovej korelácie, a tieto obrazy sú potom fúzované do výsledného snímku s vysokým rozlíšením pomocou priemerovania a využitia superrozlišovacej techniky, presnejšie regularizácie pomocou bilaterálneho celkového rozptylu. Výsledné mediánové hodnoty skóre kvality získaných obrazov sú PIQUE 0.2600, NIQE 0.0701, a BRISQUE 0.3936 pre techniku priemerovania, a PIQUE 0.1063, NIQE 0.0507, and BRISQUE 0.1570 pre superrozlišovaciu techniku.
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