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master's thesis
Duboko učenje za segmentaciju krvnih žila srca
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. urn:nbn:hr:168:657141
Šošić, Lucija
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Šošić, L. (2020). Duboko učenje za segmentaciju krvnih žila srca (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:657141

Šošić, Lucija. "Duboko učenje za segmentaciju krvnih žila srca." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:657141

Šošić, Lucija. "Duboko učenje za segmentaciju krvnih žila srca." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2020. https://urn.nsk.hr/urn:nbn:hr:168:657141

Šošić, L. (2020). 'Duboko učenje za segmentaciju krvnih žila srca', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 August 2024, https://urn.nsk.hr/urn:nbn:hr:168:657141

Šošić L. Duboko učenje za segmentaciju krvnih žila srca [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2020 [cited 2024 August 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:657141

L. Šošić, "Duboko učenje za segmentaciju krvnih žila srca", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:168:657141

Metadata
TitleDuboko učenje za segmentaciju krvnih žila srca
Title (english)Deep Learning for Cardiac Blood Vessel Segmentation
AuthorLucija Šošić
MentorSven Lončarić (mentor)
Committee memberSven Lončarić (član povjerenstva)
Committee memberMarko Subašić (član povjerenstva)
Committee memberTomislav Petković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2020-07-08, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Metode za segmentaciju medicinskih slika igraju kritičnu ulogu pri kvalitetnoj primjeni terapije, ali se pri tome moraju nositi s različitim ograničenjima koje ona povlači. U okviru diplomskog rada prikazan je pristup segmentaciji krvnih žila srca uporabom modela dubokog učenja. Detaljno je objašnjen cjevovod model gdje Mask R-CNN model vrši detekciju krvnih žila na slici, a potom UNet model daje konačnu semantičku masku. Rad je takod̄er posvećen opisu tehnika obrade slika kao odgovor na veliku složenost problema segmentacije medicinskih slika. Uspored̄eni su rezultati dobiveni uporabom različitih funkcija gubitka.
Abstract (english)
Methods for medical image segmentation play a critical role when it comes to the quality application of therapy, but they must deal with the various limitations that medical images entail. The thesis presents an approach to the segmentation of cardiac blood vessels using deep learning models. The pipeline model consists of the Mask R-CNN model that performs the detection of blood vessels in the image, and the UNet model that provides the final semantic mask. The paper is also devoted to the description of image processing techniques in response to the great complexity of medical image segmentation. The results obtained using different loss functions were compared.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:657141
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2023-01-13 13:58:14