Segmentation of Intra-Retinal Layers From Optical Coherence Tomography Images Using an Active Contour Approach

Abstract

Optical coherence tomography (OCT) is a noninvasive, depth-resolved imaging modality that has become a prominent ophthalmic diagnostic technique. We present a semi-automated segmentation algorithm to detect intra-retinal layers in OCT images acquired from rodent models of retinal degeneration. We adapt Chan-Vese's energy-minimizing active contours without edges for the OCT images, which suffer from low contrast and are highly corrupted by noise. A multiphase framework with a circular shape prior is adopted in order to model the boundaries of retinal layers and estimate the shape parameters using least squares. We use a contextual scheme to balance the weight of different terms in the energy functional. The results from various synthetic experiments and segmentation results on OCT images of rats are presented, demonstrating the strength of our method to detect the desired retinal layers with sufficient accuracy even in the presence of intensity inhomogeneity resulting from blood vessels. Our algorithm achieved an average Dice similarity coefficient of 0.84 over all segmented retinal layers, and of 0.94 for the combined nerve fiber layer, ganglion cell layer, and inner plexiform layer which are the critical layers for glaucomatous degeneration.

Automated retinal layers segmentation in SD-OCT images using dual-gradient and spatial correlation smoothness constraint

“…Active contour approaches [25][26][27][28][29] have been used for OCT image segmentation, minimizing the energy function composed by shape prior knowledge, edge information and regional information. However, active contour methods are time-consuming and have limited accuracy, making clinical application difficult.…”

Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology

“…These include a 3D segmentation algorithm by Ruggeri and colleagues that segments two retinal layer boundaries [14], and a two-algorithm method by Molnár and colleagues that segments three retinal layer boundaries by first calculating borders using row projections in a sliding window and then refining these borders iteratively [15]. The method by Yazdanpanah and colleagues utilizes active contours to segment retinal layers in SD-OCT images of rat eyes [16]. However, that paper was limited in application, as the test images were preselected based on three limiting criteria: 1) The test images were chosen from wild-type (WT) or diseased eyes in which no retinal layer was completely missing.…”

Automated detection of retinal layers from OCT spectral-domain images of healthy eyes

“…In open literature, only a few segmentation methods allow to reconstruct properly several retinal layers (e.g. [16,17]) and they are based on several algorithms such as the non-linear complex diffusion, edge detection via proper kernels, active contours, Markov Random Field, Kalman filtering, and level sets [18][19][20][21][22][23][24]. Hereafter, the algorithms previously mentioned are briefly discussed.…”