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Leukocyte positioning and segmentation method based on deep neural network

A deep neural network and white blood cell technology, applied in the field of white blood cell positioning and segmentation based on deep neural network, can solve problems such as being easily affected by complex backgrounds, and achieve the effect of improving segmentation accuracy

Inactive Publication Date: 2019-08-16
MINJIANG UNIV
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AI Technical Summary

Problems solved by technology

However, the above CNN-based methods all directly segment cells or organs on the whole image, which are easily affected by complex backgrounds.

Method used

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  • Leukocyte positioning and segmentation method based on deep neural network
  • Leukocyte positioning and segmentation method based on deep neural network
  • Leukocyte positioning and segmentation method based on deep neural network

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Embodiment Construction

[0030] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0031] The invention provides a method for positioning and segmenting white blood cells based on a deep neural network, comprising the following steps:

[0032] Step S1, feature extraction stage: designing an improved Feature Pyramid Network (FPN) to extract multi-scale white blood cell features to form a multi-scale feature map;

[0033] Step S2, candidate region positioning stage: use the region candidate network (Region Proposal Network, RPN) to locate the region where white blood cells may exist in the multi-scale feature map, and obtain the candidate region;

[0034] Step S3, Prediction stage: First, use the region of interest alignment (RoIAlign) layer to perform bilinear interpolation to align the positioning results of the candidate region positioning stage, and map each candidate region into a fixed-size feature map, and then res...

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Abstract

The invention relates to a leukocyte positioning and segmentation method based on a deep neural network. The method comprises the following steps: S1, a feature extraction stage of designing an improved FPN for extracting multi-scale leukocyte features to form a multi-scale feature map; S2, a candidate region positioning stage of positioning a region where leukocytes may exist in the multi-scale feature map by using an RPN to obtain a candidate region; S3, a prediction stage of firstly, carrying out bilinear interpolation by utilizing a RoIAlign layer to align positioning results of candidateareas in a positioning stage, mapping each candidate area into a feature map with a fixed size, and then respectively inputting the feature map as a positioning branch and a segmentation branch to carry out final positioning and segmentation so as to realize leukocyte segmentation. The method provided by the invention not only can obviously improve the segmentation precision, but also has good robustness for blood cell images under different acquisition environments and preparation technologies.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a white blood cell positioning and segmentation method based on a deep neural network. Background technique [0002] Information such as the total number of WBC (White Blood Cell, formerly known as Leukocyte) in the blood, the proportion and shape of various types of white blood cells are important indicators for diagnosing leukemia and other human blood diseases. An important part of the blood routine examination in the hospital is the differential count and abnormal morphology analysis of white blood cells. At present, domestic hospitals usually use a blood cell analyzer based on the electrical impedance method (physical method) plus the flow analysis method (physical-chemical method) to perform differential counting of blood cells. When the blood cell count results are abnormal or the attending doctor suspects that the patient has a blood disease, the lab...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06T7/187
CPCG06T7/11G06T7/187G06T2207/20016G06T2207/20081G06T2207/20084G06T2207/20152G06T2207/10056G06V20/69
Inventor 李佐勇樊好义沈丹莹刘伟霞周常恩
Owner MINJIANG UNIV
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