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Deep Learning for 3D Image Analysis
Hlavoň, David ; Herout, Adam (referee) ; Španěl, Michal (advisor)
This work deals with usage of fully convolutional neural network for segmentation of bones in CT scans. Typical issue is limited size of dataset while training on medical images. Experiments show that training on patches gives score of segmentation 95,1%. Training on whole images gives score 30% less than training on patches. As metric F-measure was used. BVLC Caffe Framework was used for training neural network.
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Interactive web applications supporting education of 3D graphics
Morávek, Jan ; Mokrý, Ondřej (referee) ; Rajmic, Pavel (advisor)
This diploma thesis is focused on computer 3D graphics and the implementation of educational applications in JavaScript language. Discussed topics of computer graphics include object transformations, Bezier patches and the role of the camera in the scene. The thesis describes the basic theory of these areas and educational applications. The thesis also includes the detailed description of the features and the implementation of the created applications. In the end of the thesis possible extensions are discussed.
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Interactive web applications supporting education of 3D graphics
Morávek, Jan ; Mokrý, Ondřej (referee) ; Rajmic, Pavel (advisor)
This diploma thesis is focused on computer 3D graphics and the implementation of educational applications in JavaScript language. Discussed topics of computer graphics include object transformations, Bezier patches and the role of the camera in the scene. The thesis describes the basic theory of these areas and educational applications. The thesis also includes the detailed description of the features and the implementation of the created applications. In the end of the thesis possible extensions are discussed.
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Deep Learning for 3D Image Analysis
Hlavoň, David ; Herout, Adam (referee) ; Španěl, Michal (advisor)
This work deals with usage of fully convolutional neural network for segmentation of bones in CT scans. Typical issue is limited size of dataset while training on medical images. Experiments show that training on patches gives score of segmentation 95,1%. Training on whole images gives score 30% less than training on patches. As metric F-measure was used. BVLC Caffe Framework was used for training neural network.
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