We present a locally-adaptive approach to segment the fluid and the interfaces between retinal layers in eyes affected by central serous retinopathy based on loosely-coupled level sets. The approach exploits the local attenuation coefficient differences of layers around an interface and introduces auxiliary interfaces to delineate the fluid. Thus, it can handle abrupt attenuation coefficient variations and topology-disrupting anomalies. The method was applied to in-vivo images of retinas acquired by optical coherence tomography. A quantitative comparison with manual annotations shows the method's high accuracy: we obtained a mean absolute deviation for the interfaces of 3.7-8.9 βm (1-2 pixels) and a Dice coefficient for the fluid segmentation of 0.96.

@en

Optical coherence tomography (OCT) is used to produce high-resolution three-dimensional images of the retina, which permit the investigation of retinal irregularities. In dry age-related macular degeneration (AMD), a chronic eye disease that causes central vision loss, disruptions such as drusen and changes in retinal layer thicknesses occur which could be used as biomarkers for disease monitoring and diagnosis. Due to the topology disrupting pathology, existing segmentation methods often fail. Here, we present a solution for the segmentation of retinal layers in dry AMD subjects by extending our previously presented loosely coupled level sets framework which operates on attenuation coefficients. In eyes affected by AMD, Bruch’s membrane becomes visible only below the drusen and our segmentation framework is adapted to delineate such a partially discernible interface. Furthermore, the initialization stage, which tentatively segments five interfaces, is modified to accommodate the appearance of drusen. This stage is based on Dijkstra's algorithm and combines prior knowledge on the shape of the interface, gradient and attenuation coefficient in the newly proposed cost function. This prior knowledge is incorporated by varying the weights for horizontal, diagonal and vertical edges. Finally, quantitative evaluation of the accuracy shows a good agreement between manual and automated segmentation. @en