Classification and Quantification of Retinal Cysts in OCT B-Scans: Efficacy of Machine Learning Methods

Teja, R V and Reddy, Manne S and Jana, Soumya and et al, . (2019) Classification and Quantification of Retinal Cysts in OCT B-Scans: Efficacy of Machine Learning Methods. In: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, 23-27 July 2019, Berlin, Germany.

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Abstract

The automatic segmentation of fluid spaces in optical coherence tomography (OCT) imaging facilitates clinically relevant quantification and monitoring of eye disorders over time. Eyes with florid disease are particularly challenging to segment, as the anatomy is often highly distorted from normal. In this context, we propose an end-to-end machine learning method consisting of near perfect detection of retinal fluid using random forest classifier and an efficient DeepLab algorithm for quantification and labeling of the target fluid compartments. In particular, we achieve an average Dice score of 86.23% with reference to manual delineations made by a trained expert.

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