Semi-Automated Labelling of Cystoid Macular Edema in OCT Scans

Greenwood, Luke, Habib, Maged and Al-Diri, Bashir (2021) Semi-Automated Labelling of Cystoid Macular Edema in OCT Scans. Biomedical Journal of Scientific & Technical Research, 36 (4). pp. 28761-28767. ISSN 2574-1241

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Semi-Automated Labelling of Cystoid Macular Edema in OCT Scans
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Item Type:Article
Item Status:Live Archive


The analysis of retinal Spectral-Domain Optical Coherence Tomography (SD-OCT)
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. A deep
learning approach for automating the segmentation of Cystoid Macular Edema (fluid)
in retinal OCT B-Scan images was developed that is consequently used for volumetric
analysis of OCT scans. This solution is a fast and accurate semantic segmentation network
that makes use of a shortened encoder-decoder UNet like architecture with an integrated
Dense ASPP module and Attention Gate for producing an accurate and refined retinal
fluid segmentation map. Our system was 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 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
the dataset creation and potentially leading to an increase in labelled data production.

Keywords:Retinal Spectral Domain Optical Coherence Tomography Images, Cystoid Macular Edema, Volumetric Analysis of OCT Scans
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science
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ID Code:46617
Deposited On:05 Oct 2021 14:31

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