Thesis Details

Detekce významných bodů v medicínských obrazech pomocí hlubokých neuronových sítí

Bachelor's Thesis Student: Škandera Juraj Academic Year: 2017/2018 Supervisor: Kodym Oldřich, Ing., Ph.D.
English title
Landmark Detection in Medical Images Using Deep Neural Networks
Language
Czech
Abstract

This thesis deals with detection of anatomical landmarks from cephalometric X-ray images using convolutional neural networks. Program works with public available dataset, which consists of side X-ray images of skull. There are two architectures of convolutional neural networks proposed in this thesis.  The best architecture achieves accuracy of 73.22% for detection within 5 mm. Program is created in Python language with use of Tensorflow framework.

Keywords

cephalometric landmarks, convolutional neural networks, landmark detection, deep learning, cephalogram

Department
Department of Computer Graphics and Multimedia FIT BUT
Degree Programme
Information Technology
Files
Status
defended, grade B
Date
13 June 2018
Reviewer
Španěl Michal, Ing., Ph.D.
Committee
Herout Adam, prof. Ing., Ph.D. (DCGM FIT BUT), předseda
Křena Bohuslav, Ing., Ph.D. (DITS FIT BUT), člen
Martínek Tomáš, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Novák Michal, doc. RNDr., Ph.D. (DMAT FEEC BUT), člen
Ryšavý Ondřej, doc. Ing., Ph.D. (DIFS FIT BUT), člen
Citation
ŠKANDERA, Juraj. Detekce významných bodů v medicínských obrazech pomocí hlubokých neuronových sítí. Brno, 2018. Bachelor's Thesis. Brno University of Technology, Faculty of Information Technology. 2018-06-13. Supervised by Kodym Oldřich. Available from: https://www.fit.vut.cz/study/thesis/21193/
BibTeX 
@bachelorsthesis{FITBT21193,
    author = "Juraj \v{S}kandera",
    type = "Bachelor's thesis",
    title = "Detekce v\'{y}znamn\'{y}ch bod\r{u} v medic\'{i}nsk\'{y}ch obrazech pomoc\'{i} hlubok\'{y}ch neuronov\'{y}ch s\'{i}t\'{i}",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2018,
    location = "Brno, CZ",
    language = "czech",
    url = "https://www.fit.vut.cz/study/thesis/21193/"
}

Theses

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