Pathological image color standardization method with invariable structure based on deep learning

A deep learning and pathological image technology, applied in the field of image processing, can solve problems such as difficult to guarantee the dyeing style, long iteration time, and no way to predict the dyeing style, so as to avoid structural deformation problems, strong learning ability, and avoid mismatching Effect

Active Publication Date: 2020-07-31
GUANGDONG GENERAL HOSPITAL
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Problems solved by technology

Non-negative matrix decomposition is an iterative process, which has two disadvantages: 1. The separation result is easy to fall into the local minimum, 2. In order to calculate a more accurate value, it takes a long it

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  • Pathological image color standardization method with invariable structure based on deep learning
  • Pathological image color standardization method with invariable structure based on deep learning
  • Pathological image color standardization method with invariable structure based on deep learning

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Embodiment

[0045] Such as figure 2 As shown, a method for normalizing the color of pathological images based on deep learning invariant structure provided in this embodiment includes the following steps:

[0046] S100: Construct a model training set;

[0047] The staining method of the pathological images in the training set is hematoxylin and eosin staining, and the imaging should be performed at 40X or 20X. The obtained high-resolution pathological images are cut into small images with a pixel size of 512*512 pixels. In order to ensure that the model can fit, finally select 2000 images from the cut images as the training set. In this embodiment, the high-resolution pathological images used should all come from the same batch of stained samples, go through the same staining and preparation process, and be scanned and imaged by the same scanner. And it is necessary to ensure that the images used as the training set are of good staining quality and the pathological structure is clear....

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Abstract

The invention discloses a pathological image color standardization method with an invariable structure based on deep learning. The method comprises the following steps: constructing a model training set; selecting an image with excellent dyeing quality from the training set as a template image, and taking other images as original images; building a deep learning model; inputting the grayscale image of the original image and the Lab color space image of the template image into a network, and training a deep learning model to obtain standardized a and b channel images; and combining the L channel of the original image with the standardized a and b channels to obtain a standardized RGB channel image. According to the method, a deep learning model is combined with an automatic coloring principle, the color style of an original image is converted into the color style of a template image by utilizing the structure information of a gray level image and the color information of the template image, and color standardization of a pathological image is realized.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a structure-invariant pathological image color standardization method based on deep learning. Background technique [0002] With the innovation of medical technology, the cell-level imaging of tumors can be presented in pathological images through a series of processing, and pathological images have become an important reference for tumor diagnosis and prognosis. However, in the process of pathological image imaging, the preparation and digitization of tissue samples will lead to color changes, which not only cause interference when pathologists read the images, but also bring inestimable errors to computer pathological image analysis. Therefore, standardizing the pathological images and standardizing the obtained pathological images to a certain color distribution has very important clinical significance. [0003] There are mainly two existing standardizati...

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Application Information

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IPC IPC(8): G06T11/00G06T11/40G06T7/90G06N3/04G06N3/08
CPCG06T11/001G06T11/40G06T7/90G06N3/08G06T2207/30096G06T2207/20081G06T2207/20084G06N3/045
Inventor 刘再毅赵秉超梁长虹韩楚孙洪赞陈鑫黄燕琪叶维韬
Owner GUANGDONG GENERAL HOSPITAL
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