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Cell image segmentation method based on Res2-UNeXt network structure

A res2-unext and image segmentation technology, applied in the field of image processing, can solve problems such as not paying attention to the ability to acquire multi-scale information on the network, and achieve the effect of improving accuracy

Active Publication Date: 2020-08-28
ZHEJIANG UNIV OF TECH
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Problems solved by technology

But all these variants basically pay little attention to the network's multi-scale information acquisition ability

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  • Cell image segmentation method based on Res2-UNeXt network structure
  • Cell image segmentation method based on Res2-UNeXt network structure
  • Cell image segmentation method based on Res2-UNeXt network structure

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Embodiment Construction

[0030] The present invention will be further described below in conjunction with the accompanying drawings.

[0031] refer to Figure 1 ~ Figure 4 , a kind of (cell) image segmentation method based on Res2-UNeXt network structure, comprises the following steps:

[0032] S1. Establish network model Res2-UNeXt

[0033] The network model Res2-UNeXt of the present invention includes the following 3 parts:

[0034] 1. Select the U-Net encoding and decoding model as the basic skeleton structure of the network. U-Net is a simple but effective image segmentation model that scales very well, so it is a suitable choice for the basic skeleton.

[0035] 2. In order to ensure the stability during the training process of the deep neural network, the ResNeXt model that combines group convolution and residual structure is a good choice. The residual structure can eliminate the gradient disappearance and gradient explosion problems in the deep neural network structure on a large scale. Gr...

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Abstract

The invention discloses a cell image segmentation method based on a Res2-UNeXt network structure, and the method comprises the steps: designing a network structure, and designing a proper and effective network according to the features of a cell image; adding a residual structure and a multi-scale convolution method into a U-Net network. The segmentation process comprises the following steps: obtaining a weight graph required for calculating Loss by utilizing a label graph of a training image; and inputting the original training data set into the Res2-NeXt network, and updating the parametersof the network according to the calculated loss; carrying out iterative training continuously until the precision of network prediction can reach a stable level; using a trained network to predict andinput new data to obtain a cell segmentation map. The invention provides a multi-scale network structure Res2-UNeXt, coarse-grained and fine-grained information can be better obtained, so that the segmentation performance of the method is improved.

Description

technical field [0001] The invention belongs to the field of image processing, and is a multi-scale end-to-end (cell) image segmentation method. Background technique [0002] The purpose of image segmentation is to segment an image into several specific, unique regions and extract objects of interest. This is a critical step from image processing to image analysis. With the development of medical image segmentation technology, it not only promotes the development of medical image processing related technologies such as image visualization and 3D reconstruction, but also plays an extremely important role in biomedical image analysis. In recent years, medical image segmentation technology has made great progress due to the application of deep learning algorithms in medical image segmentation. [0003] A simpler segmentation method is a deep neural network based on a sliced ​​architecture, which selects a small patch around each pixel with the label of that pixel to train the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06T7/00G06N3/04G06K9/62G06K9/34
CPCG06T7/11G06T7/0012G06T2207/20081G06T2207/20084G06T2207/30024G06T2207/20112G06V10/267G06N3/045G06F18/253
Inventor 产思贤黄诚丁维龙白琮陈胜勇
Owner ZHEJIANG UNIV OF TECH
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