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Blood leukocyte staining style conversion method based on cyclic generative adversarial network

A style conversion and white blood cell technology, applied in the field of image processing, can solve expensive problems and achieve the effect of improving performance

Pending Publication Date: 2022-06-24
MINJIANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] Introduce an unsupervised segmentation strategy, obtain semantic guidance information, and realize leukocyte staining style conversion, so as to overcome the costly problem of obtaining leukocyte image label information as a semantic condition and make full use of existing image data;

Method used

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  • Blood leukocyte staining style conversion method based on cyclic generative adversarial network
  • Blood leukocyte staining style conversion method based on cyclic generative adversarial network
  • Blood leukocyte staining style conversion method based on cyclic generative adversarial network

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Embodiment Construction

[0040] The technical solutions of the present invention will be described in detail below with reference to the accompanying drawings.

[0041] The present invention is a kind of blood leukocyte staining style conversion method based on cyclic generative adversarial network, comprising:

[0042] Introduce unsupervised segmentation strategy, obtain semantic guidance information, and realize leukocyte staining style conversion, so as to overcome the expensive problem of obtaining label information of leukocyte images as a semantic condition restriction, and make full use of existing image data;

[0043] The multi-scale discriminator is improved, and the deep and shallow feature information obtained by the multi-scale downsampling operation is used for feature fusion correction through a self-correction module to ensure the clarity and semantic accuracy of the converted image.

[0044] The following is the specific implementation process of the present invention.

[0045] figur...

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Abstract

The invention relates to a blood leukocyte staining style conversion method based on a cyclic generative adversarial network. By introducing an unsupervised segmentation strategy, the problem that the cost is high when tag information of a leukocyte image is obtained as semantic condition limitation is solved, and existing image data are fully utilized; through an improved multi-scale discriminator, deep and shallow layer feature information obtained by multi-scale down-sampling operation is subjected to feature fusion correction through a self-correction module (SCBlock), so that the definition and semantic accuracy of a converted image are ensured. Wide experiments show that the converted leukocyte rapid staining image generated by the method is clear and accurate, and meanwhile, the leukocyte image segmentation performance can be greatly improved by utilizing the leukocyte rapid staining image generated by the method.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a blood leukocyte staining style conversion method based on a cyclically generated adversarial network. Background technique [0002] White blood cells are an important type of blood cell in the blood and are also commonly referred to as immune cells. As part of the immune system, it helps the body fight infectious diseases as well as foreign pathogens. Abnormal numbers or shapes of white blood cells are critical for diagnosing diseases such as infections, autoimmune diseases, genetic disorders, and cancers that affect blood cells or the bone marrow. For example, when the body suffers from certain diseases, the total number and percentage of different types of white blood cells can change significantly. Differential white blood cell count is an important step in routine blood tests. By detecting the number of white blood cells, the percentage of each type o...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06T5/00G06T7/11G06V10/774G06V10/762G06V10/80
CPCG06T7/11G06T2207/30024G06F18/23G06F18/253G06F18/214G06T5/70
Inventor 李佐勇黄茂叶樊好义陈春强蔡远征
Owner MINJIANG UNIV
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