2/25/2024 0 Comments Retina oct![]() ![]() The position of the curve corresponding to minimum energy is the contour of the layer. They used continuous curves and energy function to transform the process of segmentation into a process of solving the minimum value of the energy function. 23 proposed the use of active contours for the stratification of fundus retinal OCT images. Existing retinal OCT image segmentation methods are mainly based on the active contour, classifiers, three-dimensional map search, and deep learning. 10, 11 Although many algorithms for segmenting the retinal layer have been developed, 12 – 22 these algorithms have a problem in that some layers are not segmented, which remains a daunting task. 9 Second, affected by retinal diseases, the retinal layer may deteriorate and cause severe deformation. 8 First, it is difficult to accurately segment the retinal layers because of the complexity of retinal OCT images and the limited resolution of the OCT scanning system. In the development of computer-aided diagnostic systems for ophthalmic diseases, automatic segmentation of the retina has been considered a critical and challenging step. In recent decades, computer-aided analysis methods for the segmentation of retinal OCT images have become increasingly popular, including automatic segmentation methods. Therefore, automatic segmentation of the retinal layers in retinal OCT images is of great significance for the diagnosis and treatment of fundus retinal diseases. 7 A quantitative analysis of retinal OCT images is critical for the diagnosis and treatment of retinal diseases however, retinal OCT images are susceptible to speckle noise, and the contrast between adjacent faults is small, making it difficult to accurately segment the images. OCT is a noninvasive, real-time, micro-resolution medical imaging tool for micro-resolution volumetric scanning of biological tissues and is ideal for examining fundus nerve tissue. Interlaminar structure diagram of the retinal OCT cross section of the macular area. ![]() 6 Figure 1 shows the retinal OCT cross section of the macula region, including the different layers of the retina. In this study, the 10-layer structure of the retina, including the nerve fiber layer (NFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), outer limiting membrane (OLM), photoreceptor inner segment (IS), photoreceptor outer segment (OS), and pigment epithelium layer (PEL), is analyzed. 5 Automating the segmentation of retinal layers in retinal optical coherence tomography (OCT) images can help effectively diagnose and monitor retinal diseases. In recent years, retinal fundus images have been widely used in the diagnosis, screening, and treatment of retinal diseases. Therefore, performing a retinal layer analysis is essential for the early diagnosis and timely treatment of retinal diseases. 3, 4 Studies have shown that in most fundus diseases, retinal morphologic changes are observed earlier than visual field changes, and the analysis of retinal morphologic structure using specific and sensitive methods will contribute to the early detection of fundus retinal diseases. 1, 2 It is estimated that more than 300 million people worldwide have fundus diseases such as age-related macular degeneration (AMD), diabetic retinopathy (DR), and central serous chorioretinopathy. Fundus retinal diseases are very commonly diagnosed by ophthalmologists, and most fundus diseases are caused by retinopathy.
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