Retinal vessel segmentation method and terminal based on residual network feature extraction

A retinal blood vessel and network feature technology, applied in the retinal blood vessel segmentation method and terminal field based on residual network feature extraction, can solve the problems of complex implementation and unsatisfactory segmentation results, and achieve the effect of improving evaluation indicators

Pending Publication Date: 2021-10-08
SHANGHAI MARITIME UNIVERSITY
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Problems solved by technology

However, the implementation of most fully automatic segmentation algorithms is complicated, the segmentation results are not ideal, and the speed and performance of segmentation also need to be improved.

Method used

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  • Retinal vessel segmentation method and terminal based on residual network feature extraction
  • Retinal vessel segmentation method and terminal based on residual network feature extraction
  • Retinal vessel segmentation method and terminal based on residual network feature extraction

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Embodiment

[0045]This embodiment discloses a retinal vessel segmentation method based on residual network feature extraction, which can be used in the design and implementation of residual network and retinal vessel segmentation methods. Aiming at the poor accuracy of retinal vessel segmentation in current medical image segmentation, the present invention proposes The ResDouble-Unet network model fused with the improved residual neuron is proposed, which effectively improves the segmentation accuracy.

[0046] With the popularity of computer hardware GPU and the rapid development of computer vision, Olaf Ronneberger and Philipp Fischer (Ronneberger O, Fischer P, Brox T.U-Net: Convolutional Networks for Biomedical Image Segmentation[J].2015.) et al. proposed a coding The decoding format is based on the deep learning algorithm of neural network, and applied to medical image segmentation, which has achieved good results. JMJ Valanarasu and VASindagi (Valanarasu J M J, Sindagi V A, Hacihalil...

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Abstract

The invention provides a retina vessel segmentation method based on residual network feature extraction, which is applied to a neural network model, and comprises the following steps: enabling an original retina vessel image to pass through a pre-trained VGG coding layer to obtain a plurality of images, wherein the number of the images is five, the sizes of the images have a preset proportional relation with the sizes of the original retinal blood vessel images. Therefore, the segmentation precision of the network is obviously improved, and the fitting ability and generalization ability of the model are better optimized. Compared with a Unet, the network also generally has better performance in other data sets.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a retinal vessel segmentation method based on residual network feature extraction and a terminal. Background technique [0002] The development of medical image segmentation technology is a process from manual segmentation to man-machine semi-automatic segmentation, and then gradually to fully automatic segmentation. Manual segmentation means that experienced clinicians directly outline the tissue boundaries on the original film, or use an image editor to outline the tissue boundaries or regions of interest on the image displayed on the screen. Prior knowledge is in high demand. With the development of computer technology, semi-automatic segmentation technology has emerged. This segmentation technology combines the data storage and calculation functions of the computer with the experience and knowledge of medical experts, and uses the method of human-computer in...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/10G06T9/00G06N3/04G06N3/08
CPCG06T7/10G06T9/002G06N3/04G06N3/08G06T2207/10024G06T2207/20081G06T2207/20084G06T2207/30041
Inventor 董小宇胡西川
Owner SHANGHAI MARITIME UNIVERSITY
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