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

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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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 iteration time

But in fact, it is difficult for us to guarantee that all dyeing styles can be covered during training, and there is no way to predict what kind of dyeing styles will be encountered in practical applications.

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