Automatic detection of the foveal center in optical coherence tomography

Biomed Opt Express. 2017 Oct 23;8(11):5160-5178. doi: 10.1364/BOE.8.005160. eCollection 2017 Nov 1.


We propose a method for automatic detection of the foveal center in optical coherence tomography (OCT). The method is based on a pixel-wise classification of all pixels in an OCT volume using a fully convolutional neural network (CNN) with dilated convolution filters. The CNN-architecture contains anisotropic dilated filters and a shortcut connection and has been trained using a dynamic training procedure where the network identifies its own relevant training samples. The performance of the proposed method is evaluated on a data set of 400 OCT scans of patients affected by age-related macular degeneration (AMD) at different severity levels. For 391 scans (97.75%) the method identified the foveal center with a distance to a human reference less than 750 μm, with a mean (± SD) distance of 71 μm ± 107 μm. Two independent observers also annotated the foveal center, with a mean distance to the reference of 57 μm ± 84 μm and 56 μm ± 80 μm, respectively. Furthermore, we evaluate variations to the proposed network architecture and training procedure, providing insight in the characteristics that led to the demonstrated performance of the proposed method.

Keywords: (100.2960) Image analysis; (100.4996) Pattern recognition, neural networks; (110.4500) Optical coherence tomography; (170.1610) Clinical applications; (170.4470) Ophthalmology.