Fundus image blood vessel segmentation method based on shared decoder and residual error tower type structure

Abstract

The invention discloses a fundus image blood vessel segmentation method based on a shared decoder and a residual error tower type structure. The method comprises the following steps: obtaining a training data set image block and a test data set image block through a data input module; obtaining a residual error tower type sequence through a residual error tower type module; obtaining multi-level semantic features through an encoding module; obtaining a multi-level probability graph through a shared decoding module; constructing a probability graph obtained by the multi-scale label, the residual error tower type sequence and the shared decoder into a model total loss, carrying out gradient optimization by utilizing PyTorch, training parameters in an encoding module and a shared decoding module; and sequentially inputting the test data set image blocks into the trained encoding module and the shared decoding module to obtain a probability graph, and splicing and binarizing the obtained probability graph to obtain a final segmentation result. The invention solves the problems of non-uniform blood vessel aperture distribution and weaker fundus image contrast.

Description

technical field [0001] The present invention relates to the technical field of image processing, and more specifically, to a fundus image blood vessel segmentation method based on a shared decoder and a residual tower structure. Background technique [0002] Accurate segmentation of retinal vessels plays a key role in the diagnosis of diabetic retinopathy, age-related macular degeneration, glaucoma and other ophthalmic diseases. The purpose of this technology is to classify fundus images at the pixel level, that is, to determine whether each pixel is a retinal blood vessel. [0003] For the segmentation of retinal blood vessels, the current mainstream technologies include U-Net and its improved methods. A U-shaped network consists of an encoder and a decoder connected in series. In order to improve the segmentation effect of U-shaped network, the main improvement methods are multi-modular network method (MS-NFN) and double-pass encoded U-shaped network (DEU-Net). [0004]...

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Problems solved by technology

[0007] In order to overcome the problems of uneven distribution of blood vessel calibers and weak contrast of fundus images in the prior art when segmenting fundus image blood vessels, the present invention provides a fundus image blood vessel segmentation method based on a shared decoder and a residual tower structure

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