PMID: 29757338May 15, 2018Paper

Automated segmentation of the choroid in EDI-OCT images with retinal pathology using convolution neural networks

Fetal, Infant and Ophthalmic Medical Image Analysis : International Workshop, FIFI 2017, and 4th International Workshop, OMIA 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings
Min ChenJames C Gee

Abstract

The choroid plays a critical role in maintaining the portions of the eye responsible for vision. Specific alterations in the choroid have been associated with several disease states, including age-related macular degeneration (AMD), central serous choroiretinopathy, retinitis pigmentosa and diabetes. In addition, choroid thickness measures have been shown as a predictive biomarker for treatment response and visual function. Where several approaches currently exist for segmenting the choroid in optical coherence tomography (OCT) images of healthy retina, very few are capable of addressing images with retinal pathology. The difficulty is due to existing methods relying on first detecting the retinal boundaries before performing the choroidal segmentation. Performance suffers when these boundaries are disrupted or suffer large morphological changes due to disease, and cannot be found accurately. In this work, we show that a learning based approach using convolutional neural networks can allow for the detection and segmentation of the choroid without the prerequisite delineation of the retinal layers. This avoids the need to model and delineate unpredictable pathological changes in the retina due to disease. Experimental validation...Continue Reading

Citations

Sep 19, 2019·Scientific Reports·Jason KugelmanMichael J Collins
May 20, 2020·Scientific Reports·Aaron CarassIpek Oguz
Oct 27, 2018·The British Journal of Ophthalmology·Daniel Shu Wei TingTien Yin Wong

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