OCT retina image field adaptive segmentation method and system

Abstract

The invention discloses an OCT retina image field adaptive segmentation method and system, and the method comprises the following steps: 1, obtaining OCT retina image data, and carrying out the image preprocessing; 2, respectively constructing an intra-domain segmentation model and a cross-domain segmentation model in combination with the feature extraction network, the feature classification network and the feature identification network; 3, according to the intra-domain segmentation model and the cross-domain segmentation model, obtaining pseudo labels of the target domain; 4, selecting an effective pseudo tag and an original image by using a self-selection active learning strategy, adding source domain rich image data, and training the cross-domain segmentation model again; 5, segmenting the target domain OCT retina image by using the trained cross-domain segmentation model. Through the technical scheme of the invention, the generalization performance of the segmentation model can be effectively improved, reliable quantitative data is provided for clinical ophthalmic disease diagnosis, and computer-aided personalized treatment is realized.

Description

technical field [0001] The invention relates to the cross field of artificial intelligence and medical image processing, in particular to a field-adaptive segmentation method and system for OCT retinal images. Background technique [0002] Retinopathy is a common ophthalmic disease that seriously harms the human visual sensory system. Optical coherence tomography (OCT) imaging technology can clearly present each cell layer of the retina. Clinical ophthalmologists can diagnose diseases by observing the changes in the structure of retinal tissue layers, and the precise segmentation of retinal lesion areas in OCT images It is an important prerequisite and guarantee for disease diagnosis. Recently, deep convolutional neural networks have become a mainstream method for medical image segmentation and have been successfully applied to retinal image segmentation. However, due to the different imaging parameters of different manufacturers' equipment (such as Heidelberg, Zeiss, etc....

AI Technical Summary

Problems solved by technology

However, these methods all learn the common features of the source domain and the target domain, while ignoring the unique features of the target domain.

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