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Recognizing significant temporal changes in the thickness of the choroid and retina at an early stage is a crucial factor in the prevention and treatment of ocular diseases such as myopia or glaucoma. Such changes are expected to be among the first indicators of pathological manifestations and are commonly dealt using segmentation-based approaches. However, segmenting the choroid is challenging due to low contrast, loss of signal and presence of artifacts in optical coherence tomography (OCT) images. In this paper, we present a novel method for early detection of choroidal changes based on piecewise rigid image registration. In order to adhere to the eye’s natural shape, the regularization enforces the local homogeneity of the transformations in nasal-temporal (x-) and superior-inferior (y-) direction by penalizing their radial differences. We restrict our transformation model to anterior-posterior (z-) direction, as we focus on juvenile myopia, which correlates to thickness changes in the choroid rather than to structural alterations. First, the precision of the method was tested on an OCT scan-rescan data set of 62 healthy Asian children, ages 7 to 13, from a population with a high prevalence of myopia. Furthermore, the accuracy of the method in recognizing synthetically induced changes in the data set was evaluated. Finally, the results were compared to those of manually annotated scans.
Series title
Lecture Notes in Computer Science book series (LNCS)
