Cell nucleus segmentation method based on attention learning

An attention, nucleus technique, applied in the field of nucleus segmentation

Pending Publication Date: 2021-03-05
黑龙江机智通智能科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the problem of cell nucleus segmentation in intelligent pathological diagnosis, and propose a cell nucleus segmentation method based on attention learning mechanism

Method used

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  • Cell nucleus segmentation method based on attention learning
  • Cell nucleus segmentation method based on attention learning
  • Cell nucleus segmentation method based on attention learning

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Embodiment 1

[0040] A kind of cell nucleus segmentation method based on attention learning mechanism provided by the present invention comprises the following steps:

[0041] S1, preparing a training data set;

[0042] S2. Prepare the attention learning loss label map and load the data;

[0043] S3. Build an attention learning structure;

[0044] S4. Build a network structure;

[0045] S5. Train the model and segment the cell nucleus.

[0046] The embodiment of the present invention is described in detail below:

[0047] The following embodiments of the present invention are implemented in detail.

[0048] S1. The preparation of the training data set consists of four steps:

[0049] (1) Selected images: The nuclear images are from 100 cervical smears, and the exfoliated cells are collected from people of different ages and diseases, including all lesion levels of the TBS diagnostic criteria.

[0050] (2) Marking the nuclei: manually draw the outline of the nucleus, and save the posit...

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Abstract

The invention discloses a cell nucleus segmentation method based on attention learning, and relates to the cell nucleus segmentation problem in intelligent pathological diagnosis. The intelligent pathological diagnosis technology utilizes a deep learning technology to segment and identify abnormal cells in a cell image. However, there are few models for cell nucleus segmentation in cell images. The following problems exist: (1) the problems of cell nucleus overlapping and unobvious boundary are not considered, so that the segmentation precision is low; and (2) the contextual information of thecell nucleus edge is not considered, so that under-segmentation or over-segmentation is caused, and the subsequent classification result is influenced. Therefore, the invention provides a cell nucleus segmentation method based on attention learning. Experiments show that the model can effectively solve the problem of segmentation of overlapped cells, and the problems of under-segmentation or over-segmentation of unclear boundaries and the like. The invention is applied to cell nucleus segmentation in intelligent pathological diagnosis.

Description

technical field [0001] The invention relates to intelligent pathological diagnosis, especially cell nucleus segmentation Background technique [0002] Cervical cancer is the second leading killer of women's health. Globally, a woman dies from cervical cancer every two minutes. Early cervical cancer can be completely cured, so early diagnosis and early treatment are effective means to deal with cancer outbreaks. Liquid-based thin-layer cytology detection is currently the most commonly used cervical cancer detection technology in the world, and it can detect some precancerous lesions and microbial infections. However, the traditional pathological diagnosis completely relies on manual reading by doctors and naked eye observation. However, the workload is heavy and the diagnostic accuracy is low, so it is impossible to promote large-scale screening. With the development of computer image processing and artificial intelligence technology, pathological automatic diagnosis tech...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06T7/13G06T5/50G06N3/04G06N3/08G06K9/62
CPCG06T7/11G06T7/13G06T5/50G06N3/084G06T2207/30024G06T2207/20221G06N3/045G06F18/214
Inventor 何勇军赵晶
Owner 黑龙江机智通智能科技有限公司
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