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master's thesis
Duboko učenje za segmentaciju slojeva u slikama retine
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:525501
Biliš, Nikola
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Biliš, N. (2019). Duboko učenje za segmentaciju slojeva u slikama retine (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:525501

Biliš, Nikola. "Duboko učenje za segmentaciju slojeva u slikama retine." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:525501

Biliš, Nikola. "Duboko učenje za segmentaciju slojeva u slikama retine." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:525501

Biliš, N. (2019). 'Duboko učenje za segmentaciju slojeva u slikama retine', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 27 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:525501

Biliš N. Duboko učenje za segmentaciju slojeva u slikama retine [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 December 27] Available at: https://urn.nsk.hr/urn:nbn:hr:168:525501

N. Biliš, "Duboko učenje za segmentaciju slojeva u slikama retine", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:525501

Metadata
TitleDuboko učenje za segmentaciju slojeva u slikama retine
Title (english)Deep Learning for Layer Segmentation in Retinal Images
AuthorNikola Biliš
MentorSven Lončarić (mentor)
Committee memberSven Lončarić (predsjednik povjerenstva)
Committee memberDavor Petrinović (član povjerenstva)
Committee memberMarko Subašić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-11, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Posljednjih godina su metode dubokog učenja u mnogim slučajevima ostvarile su- periorne rezultate za probleme segmentacije slika u odnosu na tradicionalne metode. Modeli ispitani u ovom radu, CNN-S i U-Net su duboke konvolucijske neuronske mreže čija je arhitektura dizajnirana za segmentaciju slojeva retine na slikama dobive- nima optičkom koherentnom tomografijom. CNN-S koristi regresijski model, a U-Net klasifikacijski. Oblik i debljina slojeva retine su ključni za dijagnozu brojnih bolesti oka. Slike nad kojima su trenirane obje mreže prikazuju retine pacijenata koji boluju od senilne makularne degeneracije. Ovaj rad detaljno opisuje pripremu podataka za treniranje. Zatim je izložena korištena implementacija obje mreže da bi se konačno usporedili i objasnili validacijski rezultati pomoću standardnih statističkih mjera toč- nosti.
Abstract (english)
In recent years deep learning based methods have demonstrated superior results in image segmentation problems over traditional methods. The models investigated in this study, CNN-S and U-Net are deep convolutional neural networks whose arhitec- ture is designed for segmentation of retinal layers in images acquired through optical coherence tomography. CNN-S implements regression model, while U-Net imple- ments classification model. Shape and thickness of retinal layers are very important features for diagnosing various eye diseases. Images used for training both networks show retinal layers taken from patients that suffer from age-related macular degenera- tion. This thesis describes data preprocessing in detail. Afterwards, implementations of both networks is presented. Finally, the validation results of two networks are shown and compared using standard statistical measures.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:525501
Study programmeTitle: Information and Communication Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije (magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2020-01-07 16:48:13