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Estimation of bone mineral density of cancellous vertebral bone in multi-energy CT data
Líška, Martin ; Jakubíček, Roman (referee) ; Chmelík, Jiří (advisor)
The principle of the BMD estimation method presented in this thesis consists in the tomographic scanning of the axial skeleton by a CT system with two different energies. The BMD estimation method was applied to acquisitions scanned by CT system IQon Spectral CT (Philips) on seven patients, two men and five women, in the lumbo-sacral region. For the functionality of the method, it is necessary to know the standardized amounts of selected elemental components contained in a given tissue, specifically in the cancellous bone of the vertebra. In the first part, the thesis deals with the theoretical part of solving the estimation of BMD from dual-energy CT data, two equations with several unknowns and their modification. The practical part deals with the program solution of the method of calculating the estimation of bone minerals in dual-energy CT data. The outputs of the presented BMD estimation method were processed and statistically compared with the other two phantom-less BMD estimation methods. The functionality of the method and statistical processing were solved in MATLAB and STATISTICA softwares.
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Cell detection using convolutional neural networks
Doskočil, Ondřej ; Chmelík, Jiří (referee) ; Vičar, Tomáš (advisor)
This bachelor thesis deals with the use of convolutional neural networks for cell detection in image data. The theoretical part contains a description of the functioning of these networks and their various architectures. In the practical part, these networks were implemented and trained on an available dataset. However, each of these networks uses a different approach to detection. Finally, the individual networks were statistically evaluated and a discussion was conducted.
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Utilisation of shape analysis methods for object classification in medical images
Karela, Jiří ; Odstrčilík, Jan (referee) ; Chmelík, Jiří (advisor)
Bachelor thesis deals with problems of shape analysis. It describes some procedures and methods related to this kind of analysis. The thesis is divided into theoretical part, practical part and conclusion. In the theoretical part we describe in greater detail some methods, with the help of which the practical part was solved. But other theories related to the topic are also described. The practical part then follows the given theory and solves the problem of shape analysis due to the knowledge gained in the theory. The algorithm is tested on medical data from CT of vertebrae. The conclusion serves as a summary and evaluation of the shape analysis solution. It also serves as a reflection on the realization of our method, ie how our solution and result could be improved.
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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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Airway analysis of prematurely born babies based on X-ray CT and MRI scans
Lázňovský, Jakub ; Harabiš, Vratislav (referee) ; Chmelík, Jiří (advisor)
Předkládaná Diplomová práce se zabývá analýzou a tvorbou modelů dýchacích cest předčasně narozených dětí. Nejprve je položen teoretický základ v oblasti vývoje dýchacího ústrojí a tvorby modelů dýchacích cest. Poté jsou představeny využité zobrazovací modality a popsány metody pro práci s obrazovými daty. Praktická část práce se zabývá vytvořením modelů dýchacích cest tří novorozenců. Všechny tyto modely jsou vytvořeny na základě klinických CT a MRI dat novorozenců narozených ve 30. týdnu gestačního věku. U těchto vytvořených modelů jsou dále analyzovány vybrané parametry související s anatomickou strukturou dýchacích cest. Na základě analýzy těchto parametrů byl následně navrhnut reprezentativní model, odpovídající dýchacím cestám novorozence daného gestačního věku.
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Detection and evaluation of distorted frames in retinal image data
Vašíčková, Zuzana ; Chmelík, Jiří (referee) ; Kolář, Radim (advisor)
Diplomová práca sa zaoberá detekciou a hodnotením skreslených snímok v retinálnych obrazových dátach. Teoretická časť obsahuje stručné zhrnutie anatómie oka a metód hodnotenia kvality obrazov všeobecne, ako aj konkrétne hodnotenie retinálnych obrazov. Praktická časť bola vypracovaná v programovacom jazyku Python. Obsahuje predspracovanie dostupných retinálnych obrazov za účelom vytvorenia vhodného datasetu. Ďalej je navrhnutá metóda hodnotenia troch typov šumu v skreslených retinálnych obrazoch, presnejšie pomocou Inception-ResNet-v2 modelu. Táto metóda nebola prijateľná a navrhnutá bola teda iná metóda pozostávajúca z dvoch krokov - klasifikácie typu šumu a následného hodnotenia úrovne daného šumu. Pre klasifikáciu typu šumu bolo využité filtrované Fourierove spektrum a na hodnotenie obrazu boli využité príznaky extrahované pomocou ResNet50, ktoré vstupovali do regresného modelu. Táto metóda bola ďalej rozšírená ešte o krok detekcie zašumených snímok v retinálnych sekvenciách.
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Classification of glioma grading in brain MRI
Olešová, Kristína ; Mézl, Martin (referee) ; Chmelík, Jiří (advisor)
This thesis deals with a classification of glioma grade in high and low aggressive tumours and overall survival prediction based on magnetic resonance imaging. Data used in this work is from BRATS challenge 2019 and each set contains information from 4 weighting sequences of MRI. Thesis is implemented in PYTHON programming language and Jupyter Notebooks environment. Software PyRadiomics is used for calculation of image features. Goal of this work is to determine best tumour region and weighting sequence for calculation of image features and consequently select set of features that are the best ones for classification of tumour grade and survival prediction. Part of thesis is dedicated to survival prediction using set of statistical tests, specifically Cox regression
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Segmentation of amyloid plaques in brains of trangenic rats based on microCT image data
Kačníková, Diana ; Kolář, Radim (referee) ; Chmelík, Jiří (advisor)
The presence of amyloid plaques in the hippocampus highlights the incidence of Alzheimer’s disease. Manual segmentation of amyloid plaques is very time consuming and increases the time that can be used to monitor the distribution of amyloid plaques. Distribution carries significant information about disease progression and the impact of potential therapy. The automatic or semi-automatic segmentation method can lead to significant savings in the time which are required when the disease has rapid progression. The description of amyloid plaques and the computed tomography are included in this work. In this diploma thesis are three implemented algorithms, two of them are based on published articles and one’s own methodological solution. The conclusion of the thesis is a quantitative evaluation of the accuracy of implemented segmentation procedures.
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Segmentation of cranial bone after craniectomy
Vavřinová, Pavlína ; Chmelík, Jiří (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the segmentation of cranial bone in CT patient’s data after craniectomy. The U-Net architecture in 2D and 3D variant were selected for the intention of solving this problem. Jaccard index for 2D U-Net was evaluate as 89,4 % and for 3D U-Net it was 67,1 %. In the area after surgical intervention evaluating index has smaller difference between both variant, the average success rate of skull classification was 98,4 % for 2D U-Net and 97,0 % for 3D U-Net.
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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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