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Cancer cell detection method based on Faster R-CNN and density estimation

A technology of density estimation and detection method, applied in the field of deep learning target detection, can solve the problems of low contrast between background and foreground, uneven light and shade of local images, low signal-to-noise ratio, etc., to achieve the effect of improving detection accuracy

Inactive Publication Date: 2019-08-30
ZHEJIANG UNIV OF TECH
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Problems solved by technology

However, phase-contrast microscopic images are gray-scale images, local images are uneven in light and shade, low contrast between background and foreground, and low signal-to-noise ratio with a lot of noise, so that some boundary areas also meet the regional homogeneity conditions, especially some areas It has complex cell topological structures such as adhesion occlusion and dense cells, so it is prone to detection errors, which will bring great challenges to the detection process of cancer cells

Method used

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  • Cancer cell detection method based on Faster R-CNN and density estimation
  • Cancer cell detection method based on Faster R-CNN and density estimation
  • Cancer cell detection method based on Faster R-CNN and density estimation

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Embodiment Construction

[0041] Further illustrate the present invention below in conjunction with accompanying drawing.

[0042] refer to Figure 1-Figure 3 , a cancer cell detection method based on Faster R-CNN and density estimation, including the following steps:

[0043] Step 1. Operating environment platform and data set format;

[0044] Step 2. Optimized network structure, the process is as follows:

[0045] 2.1 Density map generated by regression-based density estimation method

[0046] The density map (density map) generation method that the present invention adopts mainly utilizes Gaussian function and impulse function to do convolution operation to reach density map, and the calculation formula of density map is as follows:

[0047]

[0048]

[0049]

[0050] where x i Indicates the pixel position of the cell in the image; δ(x-x i ) represents a simple impulse function at the position of the cell in the image; N represents the total number of cells in the image; Indicates th...

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Abstract

The invention discloses a cancer cell detection method based on Faster R-CNN and density estimation. The cancer cell detection method comprises the following steps: step 1, making a data set; step 2,optimizing a network structure: step 2.1, generating a density map by using a regression-based density estimation method; step 2.2, measuring a regression-based density estimation loss function by using the Euclidean distance between the density map obtained by network prediction and a real value; and step 3, obtaining a detection classification result. The cancer cell detection method based on Faster R-CNN and density estimation provided by the invention effectively improves the detection accuracy under the conditions of shielding and high density.

Description

technical field [0001] The invention proposes a cancer cell detection method based on Faster R-CNN and density estimation, which belongs to the field of deep learning target detection. [0002] technical background [0003] With the development of computer technology, image processing algorithms for automatic analysis of medical pathological images are also more and more widely used. Quantitative analysis of microscope images is widely used in medical research fields such as early diagnosis of cancer, cancer grading, and drug use. In medical image analysis, the detection of cells is particularly basic and important, and it is the basic premise for identifying and counting cell images. In many biomedical applications, the detection of cancer cells under microscopic sequence images is the basis for recording and analyzing the life cycle of cancer cells, especially for the development of subsequent anticancer drugs. However, phase-contrast microscopic images are gray-scale ima...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/04
CPCG06T7/0012G06T2207/30096G06N3/045
Inventor 胡海根郑熠星肖杰周乾伟管秋
Owner ZHEJIANG UNIV OF TECH
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