Quantitative Assessment of Central Serous Chorioretinopathy in Angiographic Sequences of Retinal Images

Abstract

Central serous chorioretinopathy is a retinal disease in which there is a leakage of fluid into the subretinal space through a retinal pigment epithelium lesion that may cause a serous detachment of the neurosensory retina. Fluorescein angiography images allow the identification of these leaks. In this type of images, the lesions and the blood vessels appear bright and the remaining anatomical structures of the retina appear dark. The area related to the leakage increases throughout the angiographic sequences and, in general, the leakage can only be visualized completely in the later phases of the exam. In this work, computational methods of image processing and image analysis are used for the detection, characterization, and determination of the size progression of dye leaks along the angiographic sequences. These methods were integrated into a computer-aided diagnosis tool. To the best of our knowledge, a computer-aided diagnosis tool that allows the automatic characterization of leakage of central serous chorioretinopathy in fluorescein angiography images is not described in the literature. The central serous chorioretinopathy leakage segmentation problem is similar to leakage diseases like the diabetic macular edema, the macular retinopathy or the choroidal neovascularization. The segmentation methods for these three diseases are divided into three main categories: comparative by subtraction of images, comparative with classification and saliency detection. The main challenges to characterize the leakage are the difference in luminosity between images of the angiography sequence, the similar pixel intensities of the leaks and the vessels, and the late staining of the optic disc. The comparative methods by subtraction of images are used for the automatic characterization of the leakage of central serous chorioretinopathy in angiographic sequences because they use temporal information. As the leakage area grows during examination, temporal information helps with identification. Furthermore, algorithm steps were introduced to reduce the influence of anatomical elements (such as the background, vessels, and optic disc) in localization and segmentation of leaks. The steps are frame selection, image denoising, image registration, vessel segmentation, candidate selection in early frames, vessel inpainting, IV optic disc detection, intensity normalization, background removal, image subtraction and leakage segmentation. The leakage segmentation is processed in three phases. First, candidates are selected in the subtraction image with the Otsu method. Second, the region growing algorithm is applied to the candidate regions to segment leaks in the last frame of the sequence. Finally, the segmentation of the leaks in the remaining frames of the sequence is achieved through an algorithm of active contours, i.e., in each frame, the leaks are segmented using as input the contours of the segmented leaks in the frame immediately later. As all the leaks are segmented in all frames of the sequence, the size progression of the l...

Research on the Segmentation of Biomarker for Chronic Central Serous Chorioretinopathy Based on Multimodal Fundus Image

“…As far as we know, the automatic segmentation of CSCR biomarkers in the angiography image has not been widely studied. To the best of our knowledge, Ferreira has carried out relevant research so far [ 7 ] and achieved certain success. Specifically, the image processing skills such as image registration, blood vessel segmentation, optic disc detection, image inpainting, and image segmentation were combined to locate the biomarkers based on the angiography image sequence.…”