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Segmentation of bone lesions in spinal CT data
Zaťko, Martin ; Chmelík, Jiří (oponent) ; Jakubíček, Roman (vedoucí práce)
The aim of the bachelor thesis was to get acquainted with the anatomy and oncological diseases of spine. Search for segmentation techniques and implement my chosen machine learning technique for the task of segmenting bone lesions of vertebral bodies. The U-net architecture of convolutional neural networks, which is generally widely used in the segmentation of biomedical images, was selected and implemented. The results obtained are high enough for the network to be used for initial rough detection and segmentation, but its use in the clinical world is not recommended.
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Estimation of bone mineral density of cancellous vertebral bone in multi-energy CT data
Líška, Martin ; Jakubíček, Roman (oponent) ; Chmelík, Jiří (vedoucí práce)
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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Detekce buněk pomocí konvolučních neuronových sítí
Doskočil, Ondřej ; Chmelík, Jiří (oponent) ; Vičar, Tomáš (vedoucí práce)
Tato bakalářská práce se zabývá využitím konvolučních neuronových sítí pro detekci buněk v obrazových datech. Teoretická část obsahuje popis fungování těchto sítí a jejich různých architektur. V praktické části byly tyto sítě implementovány a trénovány na dostupném datasetu. Každá z těchto sítí využívá však jiný přístup k detekci. Nakonec byly jednotlivé sítě statisticky vyhodnoceny a byl provedena diskuse.
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Využití metod tvarové analýzy pro klasifikaci objektů v medicínských obrazech
Karela, Jiří ; Odstrčilík, Jan (oponent) ; Chmelík, Jiří (vedoucí práce)
Tato bakalářská práce se zabývá problematikou tvarové analýzy. Popisuje některé postupy a metody, které s touto analýzou souvisí. Práce je rozdělena na teoretickou část, praktickou část a závěr. V teoretické části jsou popsané do většího detailu některé metody, s pomocí kterých poté byla řešena část praktická. Také je zde ale popsána i další teorie, která souvisí s tématem. V praktické části se poté navazuje na danou teorii a je řešen problém tvarové analýzy díky znalostem v teorii získaných. Algoritmus je otestován na medicínských datech z CT obratel. Závěr slouží jako shrnutí a zhodnocení řešení tvarové analýzy. Také slouží jako úvaha nad realizací naší metody, tedy jak by se mohlo naše řešení a výsledek zlepšit.
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Segmentation of brain tumours in MRI images using deep learning
Ustsinau, Usevalad ; Odstrčilík, Jan (oponent) ; Chmelík, Jiří (vedoucí práce)
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 (oponent) ; Chmelík, Jiří (vedoucí práce)
The proposed Master’s thesis deals with the analysis and creation of airway models of premature babies. Firstly, the theoretical basis is discussed in the field of development of the respiratory system and the creation of airway models. Then the used imaging modalities are introduced, and methods for working with image data are described. The practical part of the thesis deals with the creation of airway models of three newborns. All of these models are based on clinical CT and MRI data of neonates born at 30 weeks of gestational age. In these created models, selected parameters related to the anatomical structure of the airways are further analysed. Based on the analysis of these parameters, a representative model corresponding to the airways of a newborn of a given gestational age was subsequently proposed.
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Detection and evaluation of distorted frames in retinal image data
Vašíčková, Zuzana ; Chmelík, Jiří (oponent) ; Kolář, Radim (vedoucí práce)
The master's thesis deals with detection and evaluation of distorted frames in retinal image data. The theoretical part contains brief summary of eye anatomy and methods for image quality assessment generally, and also particularly on retinal images. The practical part is carried out in programming language Python. It contains preprocessing of the available retinal images in order to create an appropriate dataset. Further a method for evaluation of three types of blur in distorted retinal images is proposed, specifically Inception-ResNet-v2 model. This method is not feasible and thus another method consisting of two steps is designed - classification of the type of blur and subsequently evaluation of the particular blur level. Filtered Fourier spectrum is used to classify the type of blur and features extracted by ResNet50 serve as the input for regression model. This method is further extended with initial step of detection of blurred frames in retinal sequences.
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Classification of glioma grading in brain MRI
Olešová, Kristína ; Mézl, Martin (oponent) ; Chmelík, Jiří (vedoucí práce)
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 (oponent) ; Chmelík, Jiří (vedoucí práce)
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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Segmentace klenby lebeční u pacientů po kraniektomii
Vavřinová, Pavlína ; Chmelík, Jiří (oponent) ; Jakubíček, Roman (vedoucí práce)
Tato práce se zabývá segmentací klenby lebeční v CT snímcích pacientů po kraniektomii. Zadaná problematika byla řešena pomocí segmentační architektury U-Net, konkrétně její 2D i 3D variantou. S první verzí architektury bylo dosaženo průměrné hodnoty Jaccardova indexu 89,4 %, u druhé úspěšnosti 67,1 % vyhodnocené stejnou metrikou. Při zaměření na oblasti po chirurgickém zákroku nebyl u výsledků jednotlivých variant již tak velký rozdíl, zjištěný Jaccardův index pro 2D síťě byl průměrně 98,4 % a pro 3D verze 97,0 %.
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