Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes

Abstract

Optical coherence tomography (OCT) is a noninvasive imaging modality that has begun to find widespread use in retinal imaging for the detection of a variety of ocular diseases. In addition to structural changes in the form of altered retinal layer thicknesses, pathological conditions may also cause the formation of edema within the retina. In multiple sclerosis, for instance, the nerve fiber and ganglion cell layers are known to thin. Additionally, the formation of pseudocysts called microcystic macular edema (MME) have also been observed in the eyes of about 5% of MS patients, and its presence has been shown to be correlated with disease severity. Previously, we proposed separate algorithms for the segmentation of retinal layers and MME, but since MME mainly occurs within specific regions of the retina, a simultaneous approach is advantageous. In this work, we propose an automated globally optimal graph-theoretic approach that simultaneously segments the retinal layers and the MME in volumetric OCT scans. SD-OCT scans from one eye of 12 MS patients with known MME and 8 healthy controls were acquired and the pseudocysts manually traced. The overall precision and recall of the pseudocyst detection was found to be 86.0% and 79.5%, respectively.

Keywords: graph-cuts; graph-theoretic approach; microcysts; multiple surface segmentation; optical coherence tomography; retina.

Figure 1

Visualization of the pseudocysts seen in an SDOCT volume obtained from an MS subject on (a) the scanning laser ophthalmoscope fundus image, and (b) a B-scan from the parafoveal location indicated by the white arrow.

Figure 2

Illustrations of (a) the minimum-cost closed set formulation proposed for the segmentation of multiple surfaces, and (b) the graph-cut image segmentation, and (c) the combined graph used for the segmentation of multiple retinal surfaces and the pseudocysts.

Figure 3

Flowchart illustrating the simultaneous segmentation of 4 inner retinal surfaces and the pseudocysts using the proposed graph-theoretic approach.

Figure 4

A slice from an MS patient scan showing (a) the original B-scan, (b) the manual tracings and (c) the automated result obtained using the proposed method.

Figure 5

A slice from an MS patient scan showing (a) the original B-scan, (b) the segmentation result of the inner surfaces obtained using the previously described graph method and (c) the segmentation result obtained by the proposed method that simultaneously segments the surfaces and the pseudocysts. The red arrows indicate areas where the segmentation of the RNFL-GCIP and the GCIP-INL boundaries failed, even cutting through MMEs at certain locations. However, the use of the proposed method where the MMEs and the surfaces are simultaneous segmented prevents such errors.