Overlapped chromosome segmentation network based on multi-scale feature extraction

A chromosome and multi-scale technology, applied in biological neural network models, image analysis, image data processing, etc., can solve problems such as insufficient semantic features, difficulty in analyzing overlapping chromosome images, and different sizes, and achieve stable data generalization capabilities , Improve the overall segmentation effect, the effect of high-precision segmentation

Pending Publication Date: 2020-09-01
CHINA UNIV OF MINING & TECH
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Problems solved by technology

The size of the target area of ​​chromosome image segmentation is different, and the semantic featu

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  • Overlapped chromosome segmentation network based on multi-scale feature extraction
  • Overlapped chromosome segmentation network based on multi-scale feature extraction
  • Overlapped chromosome segmentation network based on multi-scale feature extraction

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Embodiment Construction

[0036] Specific implementation plan

[0037] The following describes the present invention in further detail with reference to the drawings and specific embodiments:

[0038] Step 1. Perform data amplification on overlapping chromosome images;

[0039] 1a) Amplify the size of overlapping chromosome images to a size of 128×128;

[0040] 1b) Generate pixel-level category label images of corresponding sizes, such as Figure 5 Shown. Among them, (a) is the chromosome α with β Overlapping composite images, (b)-(e) are their corresponding category label images, and the light-colored areas in (b) and (c) correspond to chromosomes respectively α with β The non-overlapping area of, (d) corresponds to the overlapping area, (e) corresponds to the background area;

[0041] Step 2, build SSPM module, such as figure 2 Shown

[0042] 2a) Unify the step size of each pooling layer, all set to 2;

[0043] 2b) Considering that the size of the feature map at the bottom of the network is 4×4, the pooling s...

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Abstract

The invention provides a multi-scale U-shaped convolutional neural network MACS Net in order to solve the problems that target segmentation regions in overlapped chromosome images are different in size, not obvious in distinguishing and the like. Multi-layer cavity convolution and synchronous long pooling technology is introduced at the bottommost layer of UNet to realize detection of target segmentation regions of different sizes and extraction of features; convolution block connection is introduced between UNet codecs, so that semantic information difference is relieved. An intersection-to-parallel ratio (IoU) of chromosome overlapping regions is used as an evaluation index; the result shows that the segmentation IOU of the MACS Net at the chromosome overlapping part reaches 0.9860, which is improved by 2.78% compared with 0.99593 of UNet, and the MACS Net respectively shows more ideal noise robustness in the data set polluted by spiced salt, Gauss and Poisson noise.

Description

Technical field [0001] The invention belongs to the field of medical image segmentation, and particularly relates to a multi-scale feature extraction and image segmentation method, which can be used for segmentation of overlapping chromosomes in medical images and subsequent karyotype analysis. Background technique [0002] There are 23 pairs of chromosomes in human healthy cell nuclei, including 22 pairs of autosomes and 1 pair of sex chromosomes. As a carrier of genes, chromosomal abnormalities account for more than 50% of spontaneous abortions, stillbirths, and premature deaths, and are also important causes of many congenital diseases. The incidence of newborns is about 1%. Chromosomal abnormalities include quantitative variation and morphological structural aberrations, which can occur on each chromosome. Therefore, how to identify abnormalities in the number and morphological structure of chromosomes has become a key way to diagnose genetic diseases, especially early diagn...

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06N3/04G06N3/08
CPCG06T7/0012G06T7/11G06N3/08G06T2207/20084G06T2207/20081G06T2207/30024G06N3/045
Inventor 张林王广杰易先鹏李港深路霖朱静逸刘辉
Owner CHINA UNIV OF MINING & TECH
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