Abstract
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.
Highlights
The use of deep learning for medical analysis becomes the first methodology of choice for automated medical image analysis [1]
A deep learning approach for automating the segmentation of Cystoid Macular Edema in retinal Optical Coherence Tomography (OCT) B-Scan images was developed that is used for volumetric analysis of OCT scans
Optical Coherence Tomography (OCT) retinal imaging is a non-invasive technology in which high-resolution cross-sectional images of retinal tissue are acquired, allowing for in-depth assessment and identification of abnormalities
Summary
The use of deep learning for medical analysis becomes the first methodology of choice for automated medical image analysis [1]. Deep learning allows for patterns to be recognised from data without any human input, with the resulting recognised information potentially being so abstract that it be insurmountably difficult for humans to manually construct features for them [2]. Optical Coherence Tomography (OCT) retinal imaging is a non-invasive technology in which high-resolution cross-sectional images of retinal tissue are acquired, allowing for in-depth assessment and identification of abnormalities. This analysis requires the skill of a trained medical professional, who would ex- amine the images and make judgments on the features that they see present. This is naturally subject to observer error, along with this it is very much a subjective area and often has inter-observer variability [4], potentially culminating in misdiagnosis which can be detrimental to a patient’s eyesight
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