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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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Optical measurement of edema of limb
Šeptun, Roman ; Chmelík, Jiří (referee) ; Harabiš, Vratislav (advisor)
This thesis deals with methods of edema measuring. In my work, I designed hardware and software solutions for a device reconstructing surface of the limb part. Purpose of my work is evaluating a discussing possibilities of this device. For making 3D reconstruction of scene from 2D images I chose reconstruction method Structure from motion. For acquisition of 2D images a device controlled by Arduino platform was constructed, the whole device is realized in program language Matlab. In the end of the thesis is described, how to improve the device for using in real conditions.
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Segmentation of soft tissues in facial part of mouse embryos from X-ray computed microtomography data
Janštová, Michaela ; Harabiš, Vratislav (referee) ; Chmelík, Jiří (advisor)
This diploma thesis deals with a segmentation of soft tissues in facial part of mouse embryos in Matlab. Segmentation of soft tissues of mouse embryos was not fully automated and every case needs a specific solution. Solving parts of this issues can provide valuable data for evolutionary biologists. Issues about staining and segmentation techniques are described. On the basis of accessible literature otsu thresholding, region growing, k-means clustering and segmentation with atlas were tested. In the end of this paper are those methods tested and evaluated on 3D microtomography data.
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Segmentation of bone lesions in spinal CT data
Zaťko, Martin ; Chmelík, Jiří (referee) ; Jakubíček, Roman (advisor)
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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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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Machine learning based method for medical image generation
Hrtoňová, Valentina ; Chmelík, Jiří (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the use of generative adversarial networks for the synthesis of medical images. Firstly, artificial neural networks are described with a focus on convolutional neural networks and generative adversarial networks. Applications of generative adversarial networks in medicine are reviewed, and selected publications on the topic of medical image synthesis are described in more detail. Furthermore, multiple models of generative adversarial networks are designed and implemented in the Python programming language. First is a model of the deep convolutional generative adversarial network and the model „pix2pix“ for the generation of skin lesion images. Moreover, the „pix2pix“ model is used for the generation of both axial and sagittal CT images of the spine. Finally, the results of generating medical images using generative adversarial networks are presented and discussed.
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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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Analýza vzťahov medzi radiomickými priznakmi heterogenity trombu v akútnych ischemických mozgových príhodách
Nemčeková, Petra ; Škrváň, Adam ; Henk, Marquering ; Chmelík, Jiří ; Jakubíček, Roman
Cievne mozgové príhody sú jedným z najznámejších patológií mozgu. Prvotnou diagnostickou metódou je použitie počítačovej tomografie (CT). Avšak pre správne určenie liečby by bolo potrebné vedieť bližšie charakteristiky trombu, na základe ktorých by bol lekár schopný usúdiť najmenej riskantnú cestu pre pacienta. Táto štúdia sa zameriava na analýzu heterogenity trombov na CT snímkach u pacientov s ischemickou mozgovou príhodou. Na základe extrahovaných radiomických príznakov získaných z reprezentatívnych masiek trombov bolo získané rozmiestnenie voxelov jednotlivých trombov v novom parametrickom priestore. To bolo následne podrobené vizualizačným technikám tSNE a UMAP. Na základe vyhodnotenia morfologickej štruktúry jednotlivých vytvorených zhlukov u pacientov by bolo možné určiť počet častí trombu s rôznym zložením, na základe čoho by lekár mohol byť schopný predikovať záťaž pre pacienta pri trombektómii, ako napríklad pomocou počtu pokusov potrebných na spriechodnenie cievy.
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Možnosti přístupu k obrazovým datům v rámci projektů ÚBMI ve spolupráci s klinickými pracovišti
Jakubíček, Roman ; Nemčeková, Petra ; Ouředníček, Petr ; Chmelík, Jiří
Tento článek zkoumá výzvy a možnosti spojené se zpracováním a sdílením obrazových dat v kontextu biomedicíny a počítačem podporované diagnostiky. S rostoucím výpočetním výkonem a využitím strojového učení se metody analýzy obrazů stávají stále efektivnějšími, ale potýkají se s problémy dostupnosti dat a obtížnou interpretovatelností. Autoři diskutují legislativní a etické aspekty ochrany osobních údajů a upozorňují na význam spolupráce mezi akademickými institucemi a klinickými pracovišti. Článek také prezentuje dva aktuální výzkumné projekty ÚBMI v oblasti analýzy obrazů, tj. analýza trombu v CT mozku a kardiovaskulární zobrazování magnetickou rezonancí, ve kterých se aktuálně využívají pokročilé algoritmy strojového učení. Spolupráce mezi ÚBMI a klinickými pracovišti přináší nové možnosti pro zlepšení diagnostiky a léčby pacientů.
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