Choroid is one of the structural layers, playing a significant role in physiology of the eye and lying between the sclera and the retina. The segmentation of this layer could guide ophthalmologists in diagnosing most of the eye pathologies such as choroidal tumors and polypoidal choroidal vasculopathy. High signal-to-noise ratio and high speed imaging in Spectral-Domain Optical Coherence Tomography (SD-OCT) make choroidal imaging feasible. Several variables such as pre-operative axial length (AXL), time of day and age affect thickness of the choroidal vascularization and should be considered for segmentation of this layer. These days most of the eye specialists manually segment the choroidal layer which is time-consuming, tiresome and dependent on human errors. To overcome these difficulties, some studies have introduced different automatic choroidal segmentation methods. In this paper, we have conducted a comprehensive review on existing recently published methods for automatic choroidal segmentation algorithms.
Background: Automatic segmentation of the choroid on Optical Coherence Tomography (OCT) images helps ophthalmologists in diagnosing eye pathologies. In nature, it is not as exhausting as manual segmentation and does not depend on human errors. In this study, sixty EDI-OCT (Enhanced Depth Imaging Optical Coherence Tomography) images of both normal and abnormal eyes gathered from Isfahan Feiz Medical Center were used. The data were manually segmented by a retinal ophthalmologist to draw comparison with the proposed automatic segmentation technique.Methods: In this study, curvelet transform based KSVD dictionary learning and Lucy-Richardson algorithm was used to remove speckle noise from OCT images. The Outer / Inner Choroidal Boundaries (O/ICB) were determined utilizing graph theory. The area between ICB and OCB was considered as choroidal region.Results: The method was evaluated on the EDI-OCT images and the average Dice Similarity Coefficient (DSC) was calculated to be 92.14% ± 3.30% between automatic and manual segmented regions. Moreover, by applying the latest presented open-source algorithm by Mazzaferri et al on our dataset the mean DSC was calculated to be 55.75% ± 14.54%. Conclusions: A significant similarity was observed between automatic and manual segmentations, in both normal and abnormal eyes. Automatic segmentation of the choroidal layer could be also utilized in large-scale quantitative studies of the choroid.
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