Automated Segmentation of the Choroid in EDI-OCT Images with Retinal Pathology Using Convolution Neural Networks
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 chorioretinopathy, 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 was performed using 62 manually delineated choroid segmentations of retinal enhanced depth OCT images from patients with AMD. Our results show segmentation accuracy that surpasses those reported by state of the art approaches on healthy retinal images, and overall high values in images with pathology, which are difficult to address by existing methods without pathology specific heuristics.
KeywordsSegmentation Deep learning Convolution neural network Retina EDI-OCT
- 4.Kang, H.M., Kwon, H.J., Yi, J.H., Lee, C.S., Lee, S.C.: Subfoveal choroidal thickness as a potential predictor of visual outcome and treatment response after intravitreal ranibizumab injections for typical exudative age-related macular degeneration. Am. J. Ophthalmol. 157(5), 1013–1021 (2014)CrossRefGoogle Scholar
- 5.Moutray, T., Alarbi, M., Mahon, G., Stevenson, M., Chakravarthy, U.: Relationships between clinical measures of visual function, fluorescein angiographic and optical coherence tomography features in patients with subfoveal choroidal neovascularisation. Br. J. Ophthalmol. 92(3), 361–364 (2008)CrossRefGoogle Scholar
- 14.Philip, A.-M., Gerendas, B.S., Zhang, L., Faatz, H., Podkowinski, D., Bogunovic, H., Abramoff, M.D., Hagmann, M., Leitner, R., Simader, C., et al.: Choroidal thickness maps from spectral domain and swept source optical coherence tomography: algorithmic versus ground truth annotation. Br. J. Ophthalmol. 1–5 (2016)Google Scholar
- 16.González-López, A., Remeseiro, B., Ortega, M., Penedo, M.G., Charlón, P.: A texture-based method for choroid segmentation in retinal EDI-OCT images. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2015. LNCS, vol. 9520, pp. 487–493. Springer, Cham (2015). doi: 10.1007/978-3-319-27340-2_61 CrossRefGoogle Scholar
- 19.Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561 (2015)