prikaz prve stranice dokumenta Semantička segmentacija slika magnetske rezonance tumora na mozgu pomoću strojnog učenja
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undergraduate thesis
Semantička segmentacija slika magnetske rezonance tumora na mozgu pomoću strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:936047
Žužić, Fran
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Žužić, F. (2023). Semantička segmentacija slika magnetske rezonance tumora na mozgu pomoću strojnog učenja (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:936047

Žužić, Fran. "Semantička segmentacija slika magnetske rezonance tumora na mozgu pomoću strojnog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:936047

Žužić, Fran. "Semantička segmentacija slika magnetske rezonance tumora na mozgu pomoću strojnog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:936047

Žužić, F. (2023). 'Semantička segmentacija slika magnetske rezonance tumora na mozgu pomoću strojnog učenja', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:936047

Žužić F. Semantička segmentacija slika magnetske rezonance tumora na mozgu pomoću strojnog učenja [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 June 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:936047

F. Žužić, "Semantička segmentacija slika magnetske rezonance tumora na mozgu pomoću strojnog učenja", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:936047

Metadata
TitleSemantička segmentacija slika magnetske rezonance tumora na mozgu pomoću strojnog učenja
Title (english)Semantic Segmentation of Magnetic Resonance Images of Brain Tumors Using Machine Learning
AuthorFran Žužić
MentorMarin Šilić (mentor)
Committee memberMarin Šilić (predsjednik povjerenstva)
Committee memberHelena Čeović (član povjerenstva)
Committee memberZorana Doždor (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-06-28, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U sklopu ovog rada razmotren je problem segmentacije medicinskih slika magnetske rezonance tumora na mozgu. Razmotrena je dvodimenzionalna i trodimenzionalna segmentacija. Primijenjen je relevantan pristup segmentiranja pomoću konvolucijskih neuronskih mreža, temeljenih na U-Net arhitekturi. Eksperimenti za navedene vrste segmentiranja medicinskih slika napravljeni su na dva različita skupa podataka. Rezultati dobiveni izradom i primjenom modela neuronskih mreža tabelirani su i grafički prikazani, uz prikaze segmentiranih slika. Raspravljeni su i problemi koji su česti u području segmentacije medicinskih slika kao što su veliki zahtjevi za memorijskim prostorom, neravnoteža klasa u ground truthu te mali broj označenih slika.
Abstract (english)
As part of this work, the problem of segmentation of medical magnetic resonance images of brain tumors was considered. Two-dimensional and three-dimensional segmentation were considered. A relevant segmentation approach using convolutional neural networks, based on the U-Net architecture, was applied. Experiments for the mentioned types of segmentation of medical images were made on two different data sets. The results obtained from the creation and application of neural network models are tabulated and graphically presented, along with segmented images. Problems that are common in the field of medical image segmentation, such as large memory space requirements, class imbalance in the ground truth and a small number of labeled images, are also discussed.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:936047
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-10-11 11:06:09