Volumetric Estimation of Cystic Macular Edema in OCT Scans

Greenwood, Luke (2019) Volumetric Estimation of Cystic Macular Edema in OCT Scans. Masters thesis, University of Lincoln.

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Volumetric Estimation of Cystic Macular Edema in OCT Scans
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Item Type:Thesis (Masters)
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Abstract

The analysis of retinal Spectral Domain Optical Coherence Tomography (SDOCT) images by trained medical professionals can be used to provide useful
insights into various diseases. It is the most popular method of retinal imaging due to its non invasive nature and the useful information it provides
for making an accurate diagnosis, however there is a clear lack of publicly
available data available to researchers in the domain. In this report, a deep
learning approach for automating the segmentation of cystic macular edema
(fluid) in retinal OCT B-Scan images is presented that is consequently used
for volumetric analysis of OCT scans. This solution is a fast and accurate
semantic segmentation network which makes use of a shortened encoderdecoder UNet-like architecture with an integrated DenseASPP module and
Attention Gate for producing an accurate and refined retinal fluid segmentation map. The network is evaluated against both publicly and privately
available datasets; on the former the network achieved a Dice coefficient of
0.804, thus making it the current best performing approach on this dataset
and on the very small and challenging private dataset, it achieved a score of
0.691. Due to the issue presented by a lack of publicly available data in this
domain, a Graphical User Interface that aims to semi-automate the labelling
process of OCT images was also created, thus greatly simplifying the process
of dataset creation and potentially leading to an increase in labelled data
production.

Divisions:College of Science > School of Computer Science
ID Code:38920
Deposited On:18 Nov 2019 13:40

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