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undergraduate thesis
Semantička segmentacija novotvorina u slikama magnetske rezonancije
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:995339
Rabuzin, Lovro
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Rabuzin, L. (2021). Semantička segmentacija novotvorina u slikama magnetske rezonancije (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:995339

Rabuzin, Lovro. "Semantička segmentacija novotvorina u slikama magnetske rezonancije." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:995339

Rabuzin, Lovro. "Semantička segmentacija novotvorina u slikama magnetske rezonancije." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:995339

Rabuzin, L. (2021). 'Semantička segmentacija novotvorina u slikama magnetske rezonancije', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 27 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:995339

Rabuzin L. Semantička segmentacija novotvorina u slikama magnetske rezonancije [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 December 27] Available at: https://urn.nsk.hr/urn:nbn:hr:168:995339

L. Rabuzin, "Semantička segmentacija novotvorina u slikama magnetske rezonancije", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:995339

Metadata
TitleSemantička segmentacija novotvorina u slikama magnetske rezonancije
Title (english)Semantic segmentation of neoplasms in magnetic resonance images
AuthorLovro Rabuzin
MentorSiniša Šegvić (mentor)
Committee memberSiniša Šegvić (predsjednik povjerenstva)
Committee memberMatej Grcić (član povjerenstva)
Committee memberIvan Grubišić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-08, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom radu primijenjene su metode dubokog učenja u svrhu semantičke segmentacije glioma iz snimki magnetske rezonancije mozga. Napravljen je kratki pregled osnovnih koncepata dubokog učenja i predstavljene su tri arhitekture modela koje sam koristio za segmentaciju: U-Net, SwiftNet i DenseNet121 s ljestvičastim naduzorkovanjem. Modeli su korišteni za segmentaciju tumora u kriškama snimki mozga. Uz to, korištena je i 3D adaptacija modela ResNet18 za segmentaciju u volumenima mozga. Napravljena je usporedba dobivenih rezultata i rezultata iz literature.
Abstract (english)
This paper applies deep learning to the problem of semantic segmentation of glioma in MR brain scans. This paper offers a short overview of basic concepts of deep learning and presents three model architectures that were used: U-Net, SwiftNet and DenseNet121 with ladder-style upsampling. The models were used for semantic segmentation of glioma in individual slices of the brain scan volumes. A 3D adaptation of ResNet18 was used for segmentation in entire brain scan volumes. The paper also compares results obtained using the aforementioned models with results from other papers on the same dataset.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:995339
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 on2023-01-14 21:30:10