Abstract
Optical coherence tomography (OCT) is a commonly used ophthalmic imaging modality. While OCT has traditionally been viewed cross-sectionally (i.e., as a sequence of B-scans), higher A-scan rates have increased interest in en face OCT visualization and analysis. The recent clinical introduction of OCT angiography (OCTA) has further spurred this interest, with chorioretinal OCTA being predominantly displayed via en face projections. Although en face visualization and quantitation are natural for many retinal features (e.g., drusen and vasculature), it requires segmentation. Because manual segmentation of volumetric OCT data is prohibitively laborious in many settings, there has been significant research and commercial interest in developing automatic segmentation algorithms. While these algorithms have achieved impressive results, the variability of image qualities and the variety of ocular pathologies cause even the most robust automatic segmentation algorithms to err. In this study, we develop a user-assisted segmentation approach, complementary to fully-automatic methods, wherein correction propagation is used to reduce the burden of manually correcting automatic segmentations. The approach is evaluated for Bruch's membrane segmentation in eyes with advanced age-related macular degeneration.
Highlights
Optical coherence tomography (OCT) is a standard imaging modality in ophthalmology, where it is used for disease detection as well as for monitoring progression and treatment response
With reference to this figure, the framework consists of three modules: in the first module, the OCT volume is automatically segmented; in the second module, the user identifies regions wherein the automatic segmentation is insufficiently accurate and corrects a subset of B-scans within these regions; and, in the third module, the user corrections are propagated to the other regions of the volume
We define the graph used to segment the RPE of the k-th B-scan, V|K={k}, as the ordered triple GkRPE = PkRPE, EkRPE, wRk PE, where PkRPE is the set of points/vertices, EkRPE ⊂ PkRPE × PkRPE is the set of edges, and wRPE : EkRPE → R+ ∪ {+∞} is the edge-weight function
Summary
Optical coherence tomography (OCT) is a standard imaging modality in ophthalmology, where it is used for disease detection as well as for monitoring progression and treatment response. While en face analysis can be performed by extracting a slab that is perpendicular to the OCT beam, it is often desirable to use a modified slab that follows one or more retinal layers. Extracting such layer-fitted slabs requires segmentation, which is complicated by the natural retinal curvature, retinal layer variations, and distortions introduced by pathology. Rather than using an entirely automatic or manual approach, this paper develops a general, user-assisted segmentation framework with the aim of reducing—rather than eliminating—user input [20]. We develop and evaluate a particular instantiation of the framework applied to segmentation of Bruch’s membrane in eyes with advanced age-related macular degeneration (AMD)—in particular, geographic atrophy (GA) and choroidal neovascularization (CNV)
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