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Intracranial aneurysm detection in TOF-MRI data
Vývoda, Jan ; Vičar, Tomáš (referee) ; Jakubíček, Roman (advisor)
Práce obsahuje teoretický přehled informací o intrakraniálních aneurysmatech, jejich diagnostice a terapii. Dále shrnuje metody detekce objektů jak klasickými metodami, tak metodami strojového učení a také obsahuje stručný popis některých metod detekce intrakraniálních aneurysmat. V praktické části je vyhodnoceno a porovnáno několik navrhnutých postupů segmentace a detekce těchto aneurysmat pomocí neuronové sítě U-net.
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Real Time Emg Detection In Therapeutic Game
Veselá, Cindy
This article focuses on real-time detection of activity in electromyographical signal. The study is based on controlling the therapeutic game through the muscle activity, called myofeedback. Many different algorithms can be used to detect EMG signal. Nowadays there is rapid development of artificial intelligence not only in biomedical engineering. In this paper there is implemented convolutional neural network for signal segmentation with accuracy 97,13%.
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Detection and localization of microbial colonies by means of deep learning algorithms
Čičatka, Michal ; Vičar, Tomáš (referee) ; Mézl, Martin (advisor)
Due to massive expansion of the mass spectrometry and constant price growth of the human labour the optimalisation of the microbial samples preparation comes into question. This master thesis deals with design and implementation of a machine learning algorithm for segmentation of images of microbial colonies cultivated on Petri dishes. This algorithm is going to be a part of a controlling software of a MBT Pathfinder device developed by the company Bruker s. r. o. that automates the process of smearing microbial colonies onto a MALDI target plates. In terms of this thesis a several models of neural networks based on the UNet, UNet++ and ENet architecture were implemented. Based on a number of experiments investigating various configurations of the networks and pre-processing of the training datatset there was chosen an ENet model with quadruplet filter count and additional convolutional block of the encoder trained on a dataset pre-processed with round mask.
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Analysis of Landscape Deforestation Using Satellite Imagery
Javorka, Martin ; Španěl, Michal (referee) ; Beran, Vítězslav (advisor)
Today is important to protect forest resources and tracking deforestation is essential. Re- mote sensing has an important role in this monitoring effort. This thesis studies four different techniques for detecting deforestation from satellite imagery - using both optical and radar data. The specifics of Earth observation data and geospatial analyses are described. The analytical techniques are used for detecting deforestation in the study area of Chocske vrchy. Image segmentation with Unet neural network model is used to classify there all deforested patches.
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Image segmentation methods with limited data sets
Horečný, Peter ; Kolařík, Martin (referee) ; Burget, Radim (advisor)
The goal of this thesis was to propose an image segmentation method, which is capable of effective segmentation process with small datasets. Recently published ODE neural network was used for this method, because its features should provide better generalization in case of tasks with only small datasets available. The proposed ODE-UNet network was created by combining UNet architecture with ODE neural network, while using benefits of both networks. ODE-UNet reached following results on ISBI dataset: Rand: 0,950272 and Info: 0,978061. These results are better than the ones received from UNet model, which was also tested in this thesis, but it has been proven that state of the art can not be outperformed using ODE neural networks. However, the advantages of ODE neural network over tested UNet architecture and other methods were confirmed, and there is still a room for improvement by extending this method.
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Deep learning methods for vessel and optic disc segmentation in ophthalmologic sequences
Rozhoňová, Andrea ; Odstrčilík, Jan (referee) ; Hesko, Branislav (advisor)
The aim of the following thesis was to study the issue of optical disc and retinal vessels segmentation in ophthalmologic sequences. The theoretical part of the thesis summarizes the principles of different approaches in the field of deep learning, which are used in connection with the given issue. Based on the theoretical part, methods for optical disk segmentation and retinal vessel segmentation based on the convolutional neural networks Linknet, PSPNet, Unet and MaskRCNN are proposed. The practical part of the thesis deals with the description of their implementation and subsequent evaluation.
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