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Deep-learning based model implementation for pathological tissue characterization in brain MR images
Malík, Michael ; Nemčeková, Petra (referee) ; Chmelík, Jiří (advisor)
This bachelor thesis focuses on the issue of image segmentation by using a deep learning model. The theoretical part describes the anatomy and selected pathology of brain. The thesis also deals with the construction of MR device and creation of an MR image. In the closing section of theoretical part, the main focus is on describing the possibilities of image segmentation with the use of deep learning architectures and selected publicly available dataset. The aim of the practical part is to put the mentioned dataset and pre-processed data to the test and acquire results of image segmentations of individual patients from attached model of neural network. In conclusion, the achieved results are appropriately discussed.
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Implementation of a deep learning model for vertebral segmentation in CT data
Blažková, Lenka ; Chmelík, Jiří (referee) ; Nohel, Michal (advisor)
This bachelor’s thesis deals with the problem of vertebrae segmentation in CT data with the use of deep learning. Firstly, there is a theoretical review focused on the anatomy and the pathologies of the spine and the vertebrae, the CT systems, and the deep learning models for vertebrae segmentation in 3D data. The following section contains a more detailed description of the chosen model. The fifth section describes the implementation of the chosen model and the proposed modification, together with the results on the relevant database. In the end, the model with the modification is used on the clinical data provided by the supervisor and its evaluation is described.
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Analysis of diagnostic parameters from 4D CINE MRI data
Panáček, Oldřich ; Harabiš, Vratislav (referee) ; Chmelík, Jiří (advisor)
The thesis is focused on calculation and analysis of heart function parameters which could be calculated from image data obtained by magnetic resonance. Manually annotated image data in short axis of heart were used for calculation. Volumetric parameters were calculated directly from image data by counting voxels which were parts of specific heart structure and then multiplied by voxel volume. Modified radial method was used to compute functional parameters whereas myocardial contractility was calculated by using segmental strain analysis. Obtained results were given into table and visualised in box– plots. Correlation analysis of parameters was also performed and decision tree classifier was used to test discrimination capacity of estimated parameters.
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Deep-learning based segmentation of pathological tissue in brain MR images
Nantl, Ondřej ; Kolář, Radim (referee) ; Chmelík, Jiří (advisor)
This diploma thesis deals with the topic of segmentation of ischemic tissue in T1 weighted MRI image data using deep learning methods. The theoretical part deals with the anatomy of brain, brain imaging using MRI, available datasets for automatic segmentation of pathological brain tissue and automatic deep learning methods for segmentation of ischemic brain tissue. In the practical part the used dataset and its preprocessing, as well as the proposed deep learning methods (U-Net) and their training, are described. The models were implemented using Python. Finally, the results of the models are presented and discussed.
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Advanced registration of image sequences from video-ophthalmoscope
Dufková, Barbora ; Chmelík, Jiří (referee) ; Kolář, Radim (advisor)
This master's thesis deals with the issue of registration of ophthalmic video sequences. It describes basic geometric transformations that can be used for registration. The basic methods of image registration are also presented, from which the most suitable variant for this application is selected. This is then implemented using a script created in the MATLAB environment. The proposed method is further evaluated objectively using the brightness profile method, using mutual information and correlation, and using retinal vessel skeleton. The effect of polynomial transformation on registration and possible optimizations of the algorithm are discussed.
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Friction and lubrication of fascia
Chmelík, Jiří ; Daniel, Matej (referee) ; Vrbka, Martin (advisor)
The diploma thesis was part of the project "Regeneration and lubrication of fascia with hyaluronan" of the company Contipro a.s., whose main goal is the development of an injectable medicinal product. The number of patients suffering from so-called myofascial lower back pain is increasing. The cause of the pain is increased hyaluronic acid, which is produced between the layers of the fascia. If the viscosity is low, the fascia layers are not affected and the coefficient of friction is low. The purpose of the product is to improve the sliding movement of the fascia and relieve patients of pain. The main component of the medicine is hyaluronic acid. The project included rheological and tribological analysis of hyaluronic acid samples. A tribological model of fascia in a pin-on-plate configuration was created for the analysis of the coefficient of friction. Based on the fascia models used, the dependence between the coefficient of friction and viscosity of native hyaluronic acid solutions was found. For models and real fascias, the native solution (Bonharen) was found to have better friction properties than hyaluronic acid derivatives. These analyzes have contributed not only to the development of the drug, but also to the emergence of other studies that would like to address the biotribology of fascia.
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Image filtration effect on quality of subtractive angiography-based CT brain images of blood-vessels
Šipula, Samuel ; Nohel, Michal (referee) ; Chmelík, Jiří (advisor)
The aim of the bachelor thesis is to design filtering methods for the resulting quality of digital subtraction angiography. The role of filtration in this case is to suppress noise and strong structures to enhance vessel anatomy. Real patient data obtained using a computed tomography system are available for this purpose. In this work, the emphasis is mainly on noise suppression. Individual filtration techniques were implemented in MATLAB. The work further acquaints the reader with the theory of vascular supply to the brain, its imaging methods and the description of filters as discrete operators.
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Intracranial hemorrhage localization in axial slices of head CT images
Kopečný, Kryštof ; Chmelík, Jiří (referee) ; Nemček, Jakub (advisor)
This thesis is focused on detection of intracranial hemorrhage in CT images using both one-stage and two-stage object detectors based on convolutional neural networks. The fundamentals of intracranial hemorrhage pathology and CT imaging as well as essential insight into computer vision and object detection are listed in this work. The knowledge of these fields of studies is a starting point for the implemenation of hemorrhage detector. The use of open-source CT image datasets is also discussed. The final part of this thesis is a model evaluation on a test dataset and results examination.
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Segmentation and morphological analysis of mouse embryo choroid plexus
Parobková, Viktória ; Jakubíček, Roman (referee) ; Chmelík, Jiří (advisor)
Choroidálny plexus je regulovanou bránou medzi krvou a mozgovomiechovým mokom a má niekoľko funkcií spojených s nervovým systémom. Mnohé funkcie sú však stále neznáme, čo je spôsobené krehkosťou, umiestnením a tvarom plexu. Preto sa na prístup k tejto kľúčovej súčasti mozgu, ktorá sa nachádza v komorách, používajú neinvazívne techniky. Okrem toho existuje súvislosť medzi jeho tvarom a patologickými stavmi mozgu. Cieľom tohto projektu bolo extrahovať ChP 4. komory implementáciou segmentačných metód a následnou morfologickou analýzou s cieľom odhaliť zákonitosti medzi tvarom a ochorením.
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Thrombi detection in main brain arteries in CT image data
Líška, Martin ; Nemček, Jakub (referee) ; Chmelík, Jiří (advisor)
The master’s thesis deals with automatic preprocessing, segmentation and consecutive analysis of volume data of anonymized patient CTA acquisitions with an indication of stroke. Preprocessing of volume data is an essential step for proper vascular tree segmentation and analysis. The region growing method was used to segment the vascular tree of the brain. After extracting the vascular tree, the labeling of individual branches was applied in the algorithm and the appropriate features were extracted. The analysis examined the features of vessel lengths, their diameter and local brightness profiles, which are important indicators of possible stenosis or occlusion of the main vessels of the brain. The output of the algorithm are various modalities of diagnostic, assisted visualizations of the segmented vascular tree. The segmentation and analysis algorithm of cerebrovascular system was created in the MATLAB programming environment.
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