Thesis Details

Hluboké neuronové sítě pro analýzu 3D obrazových dat

Bachelor's Thesis Student: Hlavoň David Academic Year: 2015/2016 Supervisor: Španěl Michal, Ing., Ph.D.
English title
Deep Learning for 3D Image Analysis
Language
Czech
Abstract

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.

Keywords

fully convolution neural network, segmentation, patches, limited dataset

Department
Degree Programme
Information Technology
Files
Status
defended, grade C
Date
16 June 2016
Reviewer
Committee
Herout Adam, prof. Ing., Ph.D. (DCGM FIT BUT), předseda
Drahanský Martin, prof. Ing., Dipl.-Ing., Ph.D. (DITS FIT BUT), člen
Chudý Peter, doc. Ing., Ph.D. MBA (DCGM FIT BUT), člen
Očenášek Pavel, Mgr. Ing., Ph.D. (DIFS FIT BUT), člen
Strnadel Josef, Ing., Ph.D. (DCSY FIT BUT), člen
Citation
HLAVOŇ, David. Hluboké neuronové sítě pro analýzu 3D obrazových dat. Brno, 2016. Bachelor's Thesis. Brno University of Technology, Faculty of Information Technology. 2016-06-16. Supervised by Španěl Michal. Available from: https://www.fit.vut.cz/study/thesis/18508/
BibTeX
@bachelorsthesis{FITBT18508,
    author = "David Hlavo\v{n}",
    type = "Bachelor's thesis",
    title = "Hlubok\'{e} neuronov\'{e} s\'{i}t\v{e} pro anal\'{y}zu 3D obrazov\'{y}ch dat",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2016,
    location = "Brno, CZ",
    language = "czech",
    url = "https://www.fit.vut.cz/study/thesis/18508/"
}
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