Robusna segmentacija slojeva retine dubokim učenjem

master's thesis

• Title (english): Robust Retinal Layer Segmentation Using Deep Learning
• Author: Lovre Antonio Budimir
• Mentor: Sven Lončarić (mentor)
• Committee member: Sven Lončarić (predsjednik povjerenstva)
• Committee member: Marko Subašić (član povjerenstva)
• Committee member: Tomislav Petković (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2020-07-14, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Segmentacija anatomskih struktura očne pozadine olakšava dijagnostiku i liječenje očnih bolesti. U diplomskog radu opisana je potpuno konvolucijska neuronska mreža za semantičku segmentaciju slojeva retine koja je temeljena na U-Net i EfficientNet arhitekturi. Eksperimenti provedeni u radu pokazali su kako odabrana arhitektura uspijeva postići bolje rezultate u usporedbi s najsuvremenijim metodama iz područja. Također, arhitektura je pokazala robusnost prilikom treniranja na slikama iz dva različita OCT uređaja. Dobri rezultati robusnih modela sugeriraju kako mreža uspijeva naučiti značajke koje razlikuju slojeve retine, te se ne oslanja na razlike u tehnikama snimanja između različitih OCT uređaja.

Abstract (english)

Segmentation of the anatomical structures of the ocular background facilitates the diagnosis and treatment of ocular diseases. This master's thesis has described a fully convolutional neural network for semantic segmentation of retinal layers based on U-Net and EfficientNet architecture. Experiments conducted in this thesis have shown that the selected architecture achieves better results compared with the state-of-the-art methods in the field. Also, the architecture has shown robustness when training on images from two different OCT devices. Good results from robust models suggest that the network manages to learn features that distinguish retinal layers and does not rely on differences in imaging techniques between different OCT devices.

• Keywords: semantička segmentacija; robusnost; duboko učenje; konvolucijska neuronska mreža; U-Net; EfficientNet; Adam; funkcija gubitka; augmentacija; OCT
• Keywords (english): semantic segmentation; robustness; deep learning; convolutional neural network; U-Net; EfficientNet; Adam; loss function; augmentation; OCT
• Language: croatian
• URN:NBN: urn:nbn:hr:168:487111
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-13 11:55:08