Accurate detection of anatomical and pathological structures in Spectral Domain Optical Coherence Tomography (SDOCT) images is critical for the diagnosis and study of ocular diseases. OCT systems are equipped with segmentation software, which have been mainly targeted to measure the nerve fiber layer and the total retinal thicknesses with varying rates of success. As for other ocular features of interest, such as the thickness of the photoreceptor layer, quantitative data is mainly obtained by manual segmentation. Manual segmentation is not only demanding for expert graders, but is also extremely time-consuming for clinical use, or for large scale, multi-center trials. Furthermore, the inherent variability between graders yields subjective results.
Previous reports have addressed different approaches to segmenting retinal layer boundaries with varying levels of success. It is desired to provide improved techniques for segmenting and identifying features in images. More particularly, it is desired to provide systems and methods for segmenting and identifying features of interest in ocular images.