Cancer auxiliary analysis system and device based on he-stained pathological images

An auxiliary analysis and pathological image technology, applied in the field of medical imaging, can solve the problem of inability to accurately segment the cytoplasm, and achieve the effect of effective network model parameters, ensuring segmentation accuracy, and good segmentation accuracy and accuracy.

Active Publication Date: 2022-06-28
湖南医药学院
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Problems solved by technology

[0006] The present invention is to solve the problem that the existing cell segmentation neural network model cannot accurately segment the cytoplasm

Method used

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  • Cancer auxiliary analysis system and device based on he-stained pathological images
  • Cancer auxiliary analysis system and device based on he-stained pathological images
  • Cancer auxiliary analysis system and device based on he-stained pathological images

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specific Embodiment approach 1

[0049]The existing cell segmentation methods cannot accurately segment the cytoplasm. On the one hand, the existing segmentation network model itself cannot accurately segment the cytoplasm, that is, for complex and difficult-to-divide images, the segmentation result of the network model itself is not enough. There is a problem of inaccuracy; on the other hand, due to the current HE staining, the layers are not clear enough, the distinction between the nucleus and the cytoplasm is not obvious, the layers of the cytoplasm and the extracellular space are less clear, and the distinction is even less obvious, which further reduces the staining image of the neural network. Handling accuracy.

[0050] This embodiment is a cancer auxiliary analysis system based on HE-stained pathological images, including:

[0051] The dyeing section image acquisition module is used to obtain the HE-stained dyed section image, and segment the image into image blocks;

[0052] The cell nucleus segmen...

specific Embodiment approach 2

[0089] This embodiment is a cancer auxiliary analysis system based on HE-stained pathological images, further comprising:

[0090] The overall cell unit determination module, for the image block corresponding to the stained slice image, maps the segmentation result of the cytoplasmic segmentation module to the corresponding image block, and maps the segmentation result of the nucleus segmentation module to the same image block, and finally forms the stained slice. A segmented image of an image, such as Figure 4 shown.

[0091] In fact, the results of the cytoplasmic segmentation module are mapped to the corresponding image blocks, while the results of the nucleus segmentation module are mapped to the same image block. Sometimes, multiple nuclei are mapped in the same cytoplasmic divided area. As long as the situation occurs when cells accumulate or become cancerous, such effects will not affect the auxiliary analysis of cancer. You can add reference factors for the arrangeme...

specific Embodiment approach 3

[0092] This embodiment is a cancer auxiliary analysis system based on HE-stained pathological images, further comprising:

[0093] The cancer auxiliary analysis module is used to identify and classify cancerous cells based on the results of the cell whole unit determination module using the expert database. The identification and classification process is carried out by means of an expert database, which stores the judgment rules of cancerous cells, and the judgment rules of cancerous cells are the morphological characteristics of cancerous cells determined by experts based on the big data of pathological images, such as the arrangement of nuclei or cells The state (whether disordered, agglomerated into pieces, etc.), the size of the nucleus (the size of each nucleus, and whether multiple nuclei are different in size, etc.), the shape of the nucleus, etc., this embodiment is characterized in that it can also include cytoplasm related morphological features, such as nucleocytop...

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Abstract

The invention relates to a cancer auxiliary analysis system and device based on HE staining pathological images, belonging to the technical field of medical imaging. In order to solve the problem that the existing cell segmentation neural network model cannot accurately segment the cytoplasm. The system of the present invention includes a dyed slice image acquisition module for acquiring HE-stained stained slice images, a cell nucleus segmentation module for transferring nuclei segmentation network models to image blocks, a cell nucleus masking module for masking cell nuclei, and adjustment Taking the cytoplasmic segmentation network model to perform cytoplasmic segmentation on the image masked by the nucleus masking module, the system also includes a cell overall unit determination module that maps the results of the cytoplasmic and nuclear segmentation modules into the same image block, and provides Ancillary Analysis Module for Cancer Ancillary Analysis. It is mainly used to provide auxiliary analysis for cancer identification.

Description

technical field [0001] The invention relates to a cancer auxiliary analysis system and device, belonging to the technical field of medical imaging. Background technique [0002] At present, the further judgment and analysis of many cancers basically rely on the analysis of the stained images of cancer sections. For the section staining process, hematoxylin-eosin (HE) staining is a commonly used staining method. . Due to the differences in HE staining operations and procedures, the staining effects are not the same, so there are also differences in the accuracy of judging pathological images based on HE staining. [0003] At the same time, with the development of artificial intelligence, deep learning technology has become the mainstream technology or research direction in many application fields, and has achieved very good recognition and detection results in many fields. At present, many researchers and scholars use deep learning technology to identify cancer cells, so as...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T7/11G06V10/28G06V10/764G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06T7/11G06N3/084G06T2207/10024G06T2207/10056G06T2207/20081G06T2207/20084G06T2207/30024G06T2207/30096G06V10/28G06N3/048G06N3/045G06F18/241
Inventor 王晓乔张在其尹辉明阳大庆唐娜萍
Owner 湖南医药学院
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