3D deep convolutional neural network for predicting neurosensory retinal thickness map from spectral domain optical coherence tomography volumes

Oscar J. Perdomo; Hernan A. Rios; Francisco J. Rodríguez; Fabio A. González

doi:10.1117/12.2511597

21 December 2018 3D deep convolutional neural network for predicting neurosensory retinal thickness map from spectral domain optical coherence tomography volumes

Oscar J. Perdomo, Hernan A. Rios, Francisco J. Rodríguez, Fabio A. González

Author Affiliations +

Proceedings Volume 10975, 14th International Symposium on Medical Information Processing and Analysis; 109750I (2018) https://doi.org/10.1117/12.2511597
Event: 14th International Symposium on Medical Information Processing and Analysis, 2018, Mazatlán, Mexico

Abstract

Age-related macular degeneration is a common cause of vision loss in people aging 55 and older. The condition affects the light-sensing cells in the macula limiting the sharp and central vision. On the other hand, Spectral Domain Optical Coherence Tomography (SD-OCT) allow highlighting abnormalities and thickness in the retinal layers which are useful for age-related macular degeneration diagnosis and follow up. The Neurosensory retina (NSR) map is defined as the thickness between the inner limiting membrane layer and the inner aspect of the retinal pigment epithelium complex. Additionally, the NSR map has been used to differentiate between healthy and subjects with macular problems, but the plotting of the retinal thickness map depends critically on additional manufacturer interpretation software to automatically drawing. Therefore, this paper presents an end-to-end 3D convolutional neural network to automatically extract nine thickness mean values to draw the NSR map from an SD-OCT.

Citation Download Citation

Oscar J. Perdomo, Hernan A. Rios, Francisco J. Rodríguez, and Fabio A. González "3D deep convolutional neural network for predicting neurosensory retinal thickness map from spectral domain optical coherence tomography volumes", Proc. SPIE 10975, 14th International Symposium on Medical Information Processing and Analysis, 109750I (21 December 2018); https://doi.org/10.1117/12.2511597

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