Retinal segmentation method based on three-dimensional image search for serous pigment epithelial detachment

Abstract

The invention discloses a cutting method for a serous pigment epithelium layer disengagement retina based on three-dimensional diagram searching. The cutting method comprises (1) the method of rapid B scanning image alignment method based on retina upper interface cutting, (2) the method of multi-resolution image searching cutting method according to an interface obviousness degree sequence and with the cut interface as constraint conditions, (3) the method of obtaining a pigment epithelium layer lower interface with an upheaval area and a smooth retina bottom interface by utilizing the different restraint conditions to carry out an image searching algorithm, (4) the cutting method of serous pigment epithelium layer disengagement based on the position difference between the pigment epithelium layer lower interface and the retina bottom interface and by combining the size and brightness information of the area, and (5) the method of carrying out outer-layer retina hierarchical segmentation and correction after the image is smoothened. The cutting result of the cutting method for the serous pigment epithelium layer disengagement retina based on three-dimensional diagram searching has high accuracy, and the cutting method can replace manual cutting and has an important auxiliary function on diagnosis and treatment of clinical related ophthalmic diseases.

Description

technical field [0001] The invention belongs to a method for segmenting retinal images, in particular to a method for segmenting tissue levels and lesion regions in retinal images of SD-OCT (frequency-domain optical coherence tomography). Background technique [0002] The retina is an extension of the neural tissue of the brain and has a complex multi-layered organizational structure. SD-OCT technology has become a powerful tool for non-destructive evaluation of retinal diseases. It can provide fast, high-resolution, three-dimensional images showing the internal layers of the retina, and provide clinical ophthalmologists with a great tool for diagnosis and treatment of diseases. help. The segmentation of retinal OCT images is of great significance to clinical practice: the segmentation of lesion areas and the quantitative analysis of their shape, size, and location play a key role in the diagnosis and treatment of diseases; the segmentation of retinal tissue levels and the ...

AI Technical Summary

Problems solved by technology

However, at present, most ophthalmologists use manual methods to conduct quantitative analysis of retinopathy displayed by OCT, which is highly subjective, cannot guarantee accuracy and consistency, and is difficult to comprehensively analyze the large amount of data brought by 3D scanning

[0003] The current automatic retinal OCT image segmentation algorithm has the following defects: (1) Most of the algorithms are two-dimensional algorithms, that is, they are segmented independently in each slice image (x-z plane image, called B-scan image). Does not make full use of 3D context information and is more susceptible to image noise or artifacts, resulting in segmentation errors

(2) Most of the existing retinal tissue hierarchical segmentation algorithms are designed for normal retina. When the retinal tissue is deformed due to lesions, these algorithms will fail

So far, there is no report on a systematic 3D automatic segmentation method for all resolvable tissue layers and lesion regions in retinal OCT images of serous pigment epithelial detachment

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