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master's thesis
Segmentacija srčanih komora iz 2D MRI slika pomoću U-Net konvolucijske neuronske mreže
2021. urn:nbn:hr:200:053630
Grubeša, Marko
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Software Engineering
Chair of Visual Computing

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Grubeša, M. (2021). Segmentacija srčanih komora iz 2D MRI slika pomoću U-Net konvolucijske neuronske mreže (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:053630

Grubeša, Marko. "Segmentacija srčanih komora iz 2D MRI slika pomoću U-Net konvolucijske neuronske mreže." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2021. https://urn.nsk.hr/urn:nbn:hr:200:053630

Grubeša, Marko. "Segmentacija srčanih komora iz 2D MRI slika pomoću U-Net konvolucijske neuronske mreže." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2021. https://urn.nsk.hr/urn:nbn:hr:200:053630

Grubeša, M. (2021). 'Segmentacija srčanih komora iz 2D MRI slika pomoću U-Net konvolucijske neuronske mreže', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 05 March 2025, https://urn.nsk.hr/urn:nbn:hr:200:053630

Grubeša M. Segmentacija srčanih komora iz 2D MRI slika pomoću U-Net konvolucijske neuronske mreže [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2021 [cited 2025 March 05] Available at: https://urn.nsk.hr/urn:nbn:hr:200:053630

M. Grubeša, "Segmentacija srčanih komora iz 2D MRI slika pomoću U-Net konvolucijske neuronske mreže", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:200:053630

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Metadata
TitleSegmentacija srčanih komora iz 2D MRI slika pomoću U-Net konvolucijske neuronske mreže
Title (english)Whole heart segmentation from MRI images using U-Net architecture
AuthorMarko Grubeša
MentorIrena Galić (mentor)
Committee memberIrena Galić (predsjednik povjerenstva)
Committee memberKrešimir Nenadić (član povjerenstva)
Committee memberHrvoje Leventić (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Software Engineering)
(Chair of Visual Computing)
Osijek
Defense date and country2021-07-23, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Abstract
U ovom diplomskom radu, predstavljen je sustav za segmentaciju 2D MRI (engl. Magnetic Resonance Image) slika srčanih komora. Za segmentaciju korištena je U-net konvolucijska neuronska mreža. Kroz rad dana je klinička pozadina o strukturi srca i fizikalna pozadina rada uređaja magnetske rezonancije. Opisani su formati zapisa slika magnetske rezonancije i način rada konvolucijskih neuronskih mreža. Sustav primjenjuje posebnu metodu obrade slike temeljena na Bayesovom teoremu. Evaluacija rezultata uspoređuje rezultate segmentacije sa i bez predobrade podataka. Provedena evaluacija postiže bolje rezultate sa predobradom podataka, ali postoje slučajevi kada zbog prejake predobrade podataka, rezultati nisu uvijek zadovoljavajući.
Abstract (english)
In this thesis, a system for segmentation of 2D MRI images of heart chambers is presented. The U-net convolutional neural network was used for the segmentation process. The clinical background on the structure of the heart and the physical background of the operation of MRI device were given throughout the work. Magnetic resonance image recording formats and convolutional neural networks are described. The system applies a special image processing method based on Bayes' theorem. Results evaluation compares segmentation results with and without data preprocessing. The conducted evaluation achieves better results with data pre-processing, but there are cases when due to too strong data pre-processing, the results are not always satisfactory.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:053630
Study programmeTitle: Graduate University Study Programme in Computer Engineering
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 conditionsOpen access
Terms of useLicence In copyright icon
Created on2021-08-16 07:41:04