WO2018157381A1 - 病理切片图像智能分类方法及装置 - Google Patents

病理切片图像智能分类方法及装置 Download PDF

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WO2018157381A1
WO2018157381A1 PCT/CN2017/075566 CN2017075566W WO2018157381A1 WO 2018157381 A1 WO2018157381 A1 WO 2018157381A1 CN 2017075566 W CN2017075566 W CN 2017075566W WO 2018157381 A1 WO2018157381 A1 WO 2018157381A1
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image
similarity
pathological slice
pixel point
sample
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French (fr)
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屈军乐
陈秉灵
罗腾
林丹樱
彭晓
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Shenzhen University
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Shenzhen University
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    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis

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  • the present invention relates to the field of image processing, and in particular, to an intelligent classification method and apparatus for pathological slice images.
  • the difference in the morphological structure and functional stability of normal cells in the development of the individual is called cell differentiation.
  • Some cells in the body lose their normal death regulation due to gene mutation, and the division and proliferation are out of control, and disordered excessive proliferation leads to destruction of normal tissue structure and becomes cancer cells.
  • Differentiation in tumor pathology often refers to the similarity between tumor cells and normal cells from which they originate. It is the main basis for the differentiation of benign and malignant tumors. The tumors with high differentiation have benign behavior, and the tumors with low differentiation have many malignant manifestations.
  • pathological section image analysis is to identify tumor cells or tissues under the microscope to show structural features different from normal cells or tissues, and usually need to be assisted by HE staining and other means of labeling.
  • Light microscopy can only describe the morphology of the nucleus, which is subjective and lacks accurate and more objective quantitative criteria.
  • Quantitative quantitative analysis reflects the morphological structure of tissues and cells, and can exclude the influence of subjective factors.
  • Image analysis in tumor pathology is mainly the determination of nuclear morphological parameters, distinguishing between precancerous lesions and cancer, distinguishing between benign and malignant tumors, and pathological grading of tumors. And judge the prognosis and so on.
  • researchers began to try to convert medical analog images into digital images, and carried out preliminary research on computer-aided diagnosis, trying to assist doctors to read medical images to a certain extent, excluding human subjective factors, improving diagnostic accuracy and effectiveness.
  • the main object of the present invention is to provide an intelligent classification method and apparatus for pathological slice images, which aims to solve the technical problem of low accuracy in classifying pathological slice images in the prior art.
  • a first aspect of the present invention provides a method for intelligently classifying a pathological slice image, the method comprising:
  • training data including a mean set of similarity indicators, a variance set, and Information entropy set
  • the pathological slice image to be classified is input to the trained machine classification model, and the type of the trained machine classification model output is used as the type of the pathological slice image to be classified.
  • a second aspect of the present invention provides an apparatus for intelligently classifying a pathological slice image, the device comprising:
  • a processing module configured to perform image processing on each of the preset normal sample and the cancer sample, to obtain training data of the normal sample and the cancer sample, wherein the training data includes an average of similarity indicators Sets, variance sets, and information entropy sets;
  • a training module configured to train a preset machine classification model based on training data of the normal sample and the cancer sample, to obtain a trained machine classification model
  • a classification module configured to input the pathological slice image to be classified into the trained machine classification model, and use the type of the trained machine classification model output as the type of the pathological slice image to be classified.
  • the invention provides an intelligent classification method for pathological slice images, which comprises: performing image processing on each of the preset normal samples and the cancer sample images, and obtaining training data of normal samples and cancer samples, wherein, training The data contains the mean set, the variance set, and the letter of similarity indicators. Entropy set, and training the preset machine classification model based on the training data of the normal sample and the cancer sample, obtaining the trained machine classification model, inputting the pathological slice image to be classified into the trained machine classification model, and The type of the machine classification model output after training is used as the type of the pathological slice image to be classified.
  • the mean and variance of the similarity index are used to discriminate the difference between the normal slice image and the cancer slice image, but also the information entropy is introduced as an independent dimension of the degree of image structure confusion, and the information is used.
  • Entropy can achieve the purpose of quantitatively describing the degree of differentiation of tumor cells or tissues, and train the machine classification model through the training data including the mean set, the variance set and the information entropy set of the similarity index of the normal sample and the cancer sample, and pass the The machine classification model classifies the pathological slice patterns, which can effectively improve the accuracy of intelligent classification of pathological slice images.
  • FIG. 1 is a schematic flow chart of an intelligent classification method for pathological slice images according to a first embodiment of the present invention
  • FIG. 2 is a schematic flow chart of an intelligent classification method for pathological slice images according to a second embodiment of the present invention
  • FIG. 3 is a schematic diagram of functional modules of a pathological slice image intelligent classification device according to a third embodiment of the present invention.
  • FIG. 4 is a schematic diagram of functional modules of an intelligent classification device for pathological slice images according to a fourth embodiment of the present invention.
  • Figure 5a is a three-dimensional spatial distribution of the fluorescence lifetime of the slice HE staining in the mean ⁇ , the variance ⁇ and the entropy value S;
  • Figure 5b is a support vector machine linear discrimination of the slice HE staining fluorescence lifetime in the ⁇ _ ⁇ plane;
  • Figure 5c is a support vector machine linear discrimination of the slice HE staining fluorescence lifetime in the S_ ⁇ plane;
  • Figure 5d is a support vector machine linear discrimination of the slice HE staining fluorescence lifetime in the S_ ⁇ plane.
  • the present invention proposes an intelligent classification method for pathological slice images, in which not only the mean value and the variance of the similarity index are used to discriminate the difference between the normal slice image and the cancer slice image, but also introduces As an independent dimension of the degree of image structure disorder, information entropy can achieve the purpose of quantitatively describing the degree of differentiation of tumor cells or tissues, and through the mean set, variance set and information entropy set of similarity indicators including normal samples and cancer samples.
  • the training data is used to train the machine classification model, and the pathological slice pattern is classified by the machine classification model, so that the accuracy of the intelligent classification of the pathological slice image can be effectively improved.
  • FIG. 1 is a flowchart of a method for intelligently classifying a pathological slice image according to a first embodiment of the present invention, the method comprising:
  • Step 101 Perform image processing on each of the preset normal sample and the cancer sample to obtain training data of the normal sample and the cancer sample, where the training data includes a mean set of similarity indicators, Variance set and information entropy set;
  • the pathological slice image intelligent classification method is implemented by a pathological slice image intelligent classification device (hereinafter referred to as: classification device).
  • classification device a pathological slice image intelligent classification device
  • the machine classification model it is necessary to train the machine classification model first to classify the classified pathological slice images using the machine classification model.
  • the training sample includes a normal sample and a cancer sample, wherein the normal sample includes a pathological slice image that is diagnosed as normal, and the cancer sample includes a diagnosis that has been cancerous. Pathological slice image.
  • the classification device performs image processing on the preset normal sample and each pathological slice image in the cancer sample to obtain training data of the normal sample and the cancer sample, wherein the training data of the normal sample includes all normal pathological slice images.
  • Mean set of similarity index, variance set of similarity index and information entropy set the training data of cancer sample contains all pathological slice images of cancerous The mean set of similarity indicators, the variance set of similarity indicators, and the set of information entropy.
  • Step 102 Train a preset machine classification model based on training data of the normal sample and the cancer sample to obtain a trained machine classification model
  • Step 103 Input the pathological slice image to be classified into the trained machine classification model, and use the type of the trained machine classification model output as the type of the pathological slice image to be classified.
  • the classification device trains the preset machine classification model based on the training data of the normal sample and the cancer sample, obtains the trained machine classification model, and inputs the pathological slice image to be classified into the trained machine.
  • the classification model uses the type of the trained machine classification model as the type of the pathological slice image to be classified.
  • the classification device performs image processing on each of the preset normal sample and the cancer sample image to obtain training data of the normal sample and the cancer sample, wherein the training data includes the mean value of the similarity index.
  • the model, and the type of the trained machine classification model output is taken as the type of the pathological slice image to be classified.
  • FIG. 2 is a flowchart of a method for intelligently classifying a pathological slice image according to a second embodiment of the present invention, the method comprising:
  • each pathological slice image in the normal sample and the cancer sample is processed according to steps 201 to 203, and each pathological slice image is obtained.
  • Mean, variance and information entropy of similarity indicators details as follows:
  • Step 201 Read a three-dimensional image of the pathological slice image containing structural information, where the three-dimensional image forms a third dimension by the photon number distribution of each pixel point;
  • the classification device will read the three-dimensional image of the pathological slice image containing the structural information, and the three-dimensional image is composed of the coordinates of each pixel to form the first and second dimensions, and each pixel The photon number distribution of points constitutes the third dimension.
  • data containing structural information is stored in a third dimension perpendicular to the pixel coordinates, and is a photon attenuation sequence in fluorescence lifetime imaging and a Raman spectrum in Raman imaging.
  • Step 202 Extract a similarity indicator of each pixel point determined by the third dimension data set in the three-dimensional image.
  • the classification device extracts the similarity index of each pixel point determined by the third dimension data set in the three-dimensional image.
  • the three-dimensional image may be fluorescence lifetime imaging or Raman imaging.
  • the related photon counting promotes fluorescence lifetime imaging (FLIM), and the fluorescence lifetime of the fluorophore is considered to be only related to its structure and microenvironment, and is not affected by excitation light intensity, molecular concentration, etc., so it can be used to characterize The degree of similarity between the marked material structures.
  • the Raman spectrum reflects the internal karyotype structure of the material.
  • the Raman spectral correlation coefficient matrix is obtained by cross-correlation operation on the Raman spectrum.
  • the Raman spectral correlation coefficient matrix can further reflect the similarity of the internal structure of the structure.
  • Both the fluorescence lifetime and the Raman spectral correlation coefficient matrix can be used as a quantitative index for discriminating the degree of similarity between pixels of a medical image, that is, a calculation parameter as a similarity index.
  • quantitative indicators of image similarity statistical analysis of similarity can be performed on medical images.
  • the degree of cell differentiation is high, and there is a wide similarity distribution between each pixel.
  • the degree of differentiation of tumor tissue is manifested as the structural difference of each pixel is smaller, the similarity distribution is relatively narrow, and the tumor is tumor. The higher the malignancy, the lower the differentiation, and the similarity distribution is concentrated.
  • the similarity index of the pathological slice image can be averaged, and the mean and variance of the similarity index can be obtained to determine the difference between the normal cell or tissue and the cancer cell or tissue.
  • information entropy can be further introduced as an independent dimension index of the degree of image structure disorder, which can quantitatively describe the degree of differentiation of tumor cells or tissues, and the mean and variance of the similarity index are used as the differentiation of tumor cells or tissues from normal cells or tissues.
  • a set of criteria criteria to improve accuracy is the principle of the invention.
  • the fluorescence lifetime or the phase mapping coordinate may be calculated by a fitting or phase mapping algorithm.
  • the above step 202 may be the following step A, or step B:
  • step A the fluorescence lifetime of each pixel is obtained by least square fitting using the time decay curve corresponding to each pixel point, and the fluorescence lifetime of each pixel is used as the similarity index of each pixel.
  • the time decay curve of the pixel is:
  • I i,j (t) represents the fluorescence intensity of the pixel point ij after decay at time t
  • t represents time
  • I 0 i,j represents the total pixel point ij
  • the fluorescence intensity, ⁇ i,j represents the fluorescence lifetime of the pixel point ij.
  • the total fluorescence intensity of the pixel point ij can be determined based on the pixel point ij photon number attenuation sequence in the three-dimensional image.
  • step B the fluorescence lifetime of each pixel point is calculated by using a preset phase mapping algorithm, and the fluorescence lifetime of each pixel point is used as the similarity index of each pixel point.
  • the phase mapping algorithm includes:
  • ⁇ represents the laser pulse angular frequency of the laser pulse used to achieve fluorescence lifetime imaging
  • ⁇ i,j the fluorescence lifetime of the pixel point ij.
  • the fluorescence lifetime of each pixel point can be obtained by the above method, and the fluorescence lifetime of each pixel point is used as the similarity index of each pixel point.
  • the similarity index of each pixel point can be obtained by the Pearson cross-correlation algorithm.
  • the above step 202 can be the following step C, specifically:
  • Step C performing a pairwise cross-correlation operation on the Raman spectra of each pixel by using a preset Pearson cross-correlation algorithm to obtain a Raman spectral correlation coefficient matrix of each pixel point, and pulling the pixel points
  • the MN spectral correlation coefficient matrix is used as an index of similarity of the respective pixel points.
  • C l,m represents a Raman spectral correlation coefficient matrix
  • R l and R m respectively represent Raman spectra of two different pixel points, with Representing the average of the two spectral lines
  • k is the kth data point in the Raman spectrum
  • N is the total number of spectral data points
  • the Raman spectral correlation coefficient matrix C l,m is an N ⁇ N symmetric matrix.
  • the analyzing device uses the Raman spectral coefficient matrix of each pixel as the similarity index of each pixel.
  • Step 203 Calculate a mean value, a variance, and an information entropy of the similarity index of the pathological slice image by using a similarity index of each pixel point;
  • the analyzing device after obtaining the similarity index of each pixel in the pathological slice image, performs averaging operation using the similarity index of each pixel point to obtain the similarity index of the pathological slice image.
  • the mean value, and the variance index of each pixel is used to calculate the variance, and the variance of the similarity index of the pathological slice image is obtained, and the entropy operation is performed by using the similarity index of each pixel to obtain the information entropy of the pathological slice image.
  • the fluorescence lifetime of each pixel point may be averaged, the variance calculation, and the entropy calculation performed, to obtain the mean, variance, and information entropy of the similarity index.
  • the upper triangular matrix element of the Raman spectral correlation coefficient matrix of each pixel point may be respectively used for averaging operation, variance calculation, and entropy calculation to obtain a similarity index. Mean, variance, and information entropy.
  • p i,j denotes the probability that the fluorescence lifetime of the pixel point ij occupies the sum of the fluorescence lifetimes of all the pixel points, or the Raman spectral correlation coefficient of the pixel point ij occupies the Raman spectral correlation coefficient of all the pixel points The probability of sum.
  • An upper triangular matrix element representing a matrix of Raman spectral correlation coefficients.
  • the mean, variance and information of the similarity index of each path slice image can be obtained. entropy.
  • Step 204 classify the mean value, the variance, and the information entropy of the similarity indicators of all the pathological slice images in the normal sample into the mean set, the variance set, and the information entropy set of the similarity index of the normal sample, respectively.
  • the training data of the normal sample, and the mean, variance and information entropy of the similarity index of all the pathological slice images in the cancer sample are respectively classified into the mean set, the variance set and the information entropy of the similarity index of at least one category Assorted to serve as training data for the cancer sample;
  • the analyzing device obtains the mean set ⁇ n ⁇ , the variance set ⁇ n ⁇ , and the information entropy set ⁇ S n ⁇ of the similarity index after the normal sample is classified, as the training of the normal sample.
  • Data, and the cancer sample is classified into a mean set ⁇ e ⁇ of the similarity index after at least one category, a variance set ⁇ c ⁇ , and an information entropy set ⁇ S e ⁇ as training data for the cancer sample.
  • cancer samples can be classified based on different periods, for example, classified into 4 categories and the like.
  • Step 205 Train the preset machine classification model based on the training data of the normal sample and the cancer sample to obtain a trained machine classification model
  • Step 206 Input the pathological slice image to be classified into the trained machine classification model, and use the type of the trained machine classification model output as the type of the pathological slice image to be classified.
  • the analyzing device trains the preset machine classification model based on the training data of the normal sample and the cancer sample, and inputs the pathological slice image to be classified into the trained machine classification model, by the trained
  • the machine classification model performs classification to determine whether the pathological slice image is a normal slice image or a cancer slice image, and the type of the trained machine classification model output is used as the type of the pathological slice image, wherein the type of the pathological slice image may be It is a normal slice image or a cancer slice image.
  • the above machine classification model may be a support vector machine neural network model, or a Bayesian linear or nonlinear classifier, or other linear or nonlinear classifier with machine learning function, in practical applications,
  • the model used is selected according to specific needs, and is not limited herein.
  • the information entropy is introduced to characterize the similarity or chaos of the structure of the substance, and the degree of differentiation of the tissue cells is statistically and objectively quantitatively described, and the differentiation of the tissue cells can be directly reflected.
  • the degree combined with the mean and variance of the similarity index, as a set of criteria for distinguishing tumor cells or tissues from normal cells or tissues, can effectively improve the accuracy of intelligent classification of pathological slice images.
  • FIG. 3 is a schematic diagram of functional modules of an intelligent classification device for pathological slice images according to a third embodiment of the present invention.
  • the device includes:
  • the processing module 301 is configured to perform image processing on each of the preset normal sample and the cancer sample to obtain training data of the normal sample and the cancer sample, where the training data includes a similarity index Mean set, variance set and information entropy set;
  • the machine classification model it is necessary to train the machine classification model first to classify the classified pathological slice images using the machine classification model.
  • the training sample includes a normal sample and a cancer sample, wherein the normal sample includes a pathological slice image that is diagnosed as normal, and the cancer sample includes a diagnosis that has been cancerous. Pathological slice image.
  • the processing module 301 performs image processing on the preset normal sample and each pathological slice image in the cancer sample to obtain training data of the normal sample and the cancer sample, wherein the training data of the normal sample includes all normal pathological slices.
  • the training data of the cancer sample includes the mean set of the similarity index of all the cancerous pathological slice images, the variance set of the similarity index, and the information entropy. set.
  • the training module 302 is configured to train the preset machine classification model based on the training data of the normal sample and the cancer sample to obtain a trained machine classification model;
  • the classification module 303 is configured to input the pathological slice image to be classified into the trained machine classification model, and use the type of the trained machine classification model output as the type of the pathological slice image to be classified.
  • the training module 302 trains the preset machine classification model based on the training data of the normal sample and the cancer sample to obtain the trained machine classification model, and inputs the pathological slice image to be classified by the classification module 303.
  • the trained machine classification model uses the type of the trained machine classification model as the type of the pathological slice image to be classified.
  • the classification device performs image processing on each of the preset normal samples and the cancer sample images, and obtains training data of the normal samples and the cancer samples, wherein, the training The data includes the mean set, the variance set and the information entropy set of the similarity index, and the preset machine classification model is trained based on the training data of the normal sample and the cancer sample, and the trained machine classification model is obtained, and the pathological slice image to be classified is to be classified.
  • the trained machine classification model is input, and the type of the trained machine classification model output is used as the type of the pathological slice image to be classified.
  • FIG. 4 is a schematic diagram of functional modules of a path segmentation image intelligent classification device according to a fourth embodiment of the present invention.
  • the device includes a processing module 301, a training module 302, and a classification module 303 in the third embodiment, and a third
  • the content described in the embodiment is similar and will not be described here.
  • the processing module 301 includes: a reading module 401, an extracting module 402, a calculating module 403, and a categorizing module 404.
  • the reading module 401, the extracting module 402, and the calculating module 403 are used for pairing Processing each of the normal sample and the pathological slice image of the cancer sample;
  • the reading module 401 is specifically configured to read a three-dimensional image of the pathological slice image containing structural information, where the three-dimensional image forms a third dimension by the photon number distribution of each pixel point;
  • the reading module 401 will read the three-dimensional image of the pathological slice image containing the structural information, and the three-dimensional image is composed of the coordinates of each pixel to form the first and second dimensions, The photon number distribution of each pixel constitutes a third dimension.
  • data containing structural information is stored in a third dimension perpendicular to the pixel coordinates, and is a photon attenuation sequence in fluorescence lifetime imaging and a Raman spectrum in Raman imaging.
  • the extraction module 402 is specifically configured to extract a similarity indicator of each pixel determined by the third dimension data set in the three-dimensional image;
  • the calculating module 403 is specifically configured to calculate a mean value, a variance, and an information entropy of the similarity index of the pathological slice image by using a similarity index of each pixel point;
  • the categorization module 404 is configured to classify the mean value, the variance, and the information entropy of the similarity indicators of all the pathological slice images in the normal sample as the mean set, the variance set, and the information entropy of the similarity index of the normal sample, respectively. Collecting, as the training data of the normal sample, and classifying the mean, variance and information entropy of the similarity index of all pathological slice images in the cancer sample into a mean set of similarity indicators of at least one category, A set of variances and a set of information entropy are used as training data for the cancer sample.
  • the extraction module 402 will extract the similarity index of each pixel point determined by the third dimension data set in the three-dimensional image.
  • the three-dimensional image may be fluorescence lifetime imaging or Raman imaging.
  • the related photon counting promotes fluorescence lifetime imaging (FLIM), and the fluorescence lifetime of the fluorophore is considered to be only related to its structure and microenvironment, and is not affected by excitation light intensity, molecular concentration, etc., so it can be used to characterize The degree of similarity between the marked material structures.
  • the Raman spectrum reflects the internal karyotype structure of the material.
  • the Raman spectral correlation coefficient matrix is obtained by cross-correlation operation on the Raman spectrum.
  • the Raman spectral correlation coefficient matrix can further reflect the similarity of the internal structure of the structure.
  • Both the fluorescence lifetime and the Raman spectral correlation coefficient matrix can be used as a quantitative index for discriminating the degree of similarity between pixels of a medical image, that is, a calculation parameter as a similarity index.
  • quantitative indicators of image similarity statistical analysis of similarity can be performed on medical images.
  • the degree of cell differentiation is high, and there is a wide similarity distribution between each pixel.
  • the degree of differentiation of tumor tissue is manifested as the structural difference of each pixel is smaller, the similarity distribution is relatively narrow, and the tumor is tumor. The higher the malignancy, the lower the differentiation, and the similarity distribution is concentrated.
  • the similarity index of the pathological slice image can be averaged, and the mean and variance of the similarity index can be obtained as two independent indicators for discriminating the difference between normal cells or tissues and cancer cells and tissues.
  • information entropy can be further introduced as an independent dimension index of the degree of image structure disorder, which can quantitatively describe the degree of differentiation of tumor cells or tissues, and the mean and variance of the similarity index are used as the differentiation of tumor cells or tissues from normal cells or tissues.
  • a set of criteria criteria to improve accuracy is the principle of the invention.
  • the fluorescence lifetime or phase mapping coordinates may be calculated by a fitting or phase mapping algorithm, and the extraction module 402 is specifically configured to:
  • the fluorescence lifetime of each pixel is obtained by least square fitting using the acquired time decay curve corresponding to each pixel point, and the fluorescence lifetime of each pixel is used as the similarity index of each pixel.
  • the time decay curve of the pixel is:
  • I i,j (t) represents the fluorescence intensity of the pixel point ij after decay at time t
  • t represents time
  • I 0 i,j represents the total pixel point ij
  • the fluorescence intensity, ⁇ i,j represents the fluorescence lifetime of the pixel point ij.
  • the total fluorescence intensity of the pixel point ij can be determined based on the pixel point ij photon number attenuation sequence in the three-dimensional image.
  • the extraction module 402 is specifically configured to calculate a fluorescence lifetime of each pixel by using a preset phase mapping algorithm, and use a fluorescence lifetime of each pixel as a similarity index of each pixel.
  • the phase mapping algorithm includes:
  • ⁇ represents the laser pulse angular frequency of the laser pulse used to achieve fluorescence lifetime imaging
  • ⁇ i,j the fluorescence lifetime of the pixel point ij.
  • the fluorescence lifetime of each pixel point can be obtained by the above method, and the fluorescence lifetime of each pixel point is used as the similarity index of each pixel point.
  • the similarity index of each pixel point can be obtained by the Pearson cross-correlation algorithm.
  • the extraction module 402 is specifically configured to:
  • C l,m represents a Raman spectral correlation coefficient matrix
  • R l and R m respectively represent Raman spectra of two different pixel points, with Representing the average of the two spectral lines
  • k is the kth data point in the Raman spectrum
  • N is the total number of spectral data points
  • the Raman spectral correlation coefficient matrix C l,m is an N ⁇ N symmetric matrix.
  • the analyzing device uses the Raman spectral coefficient matrix of each pixel as the similarity index of each pixel.
  • the calculating module 403 performs averaging operation using the similarity index of each pixel to obtain the pathological slice image.
  • the mean value of the similarity index is calculated by using the similarity index of each pixel to calculate the variance of the similarity index of the pathological slice image, and the entropy operation is performed by using the similarity index of each pixel to obtain the pathological slice image.
  • Information entropy is performed by using the similarity index of each pixel to obtain the pathological slice image.
  • the calculation module 403 can perform the averaging operation, the variance calculation, and the entropy calculation on the fluorescence lifetimes of the respective pixel points to obtain the mean value and the variance of the similarity index.
  • the information entropy or if the three-dimensional image of the pathological slice image is Raman imaging, the calculation module 403 can perform the averaging operation, the variance calculation, and the entropy by using the upper triangular matrix elements of the Raman spectral correlation coefficient matrix of each pixel point. The operation obtains the mean, variance and information entropy of the similarity index.
  • S denotes information entropy
  • p i,j denotes the probability that the fluorescence lifetime of the pixel point ij occupies the sum of the fluorescence lifetimes of all the pixel points, or the Raman spectral correlation coefficient of the pixel point ij accounts for the Raman spectrum of all the pixel points The probability of the sum of the coefficients;
  • An upper triangular matrix element representing a matrix of Raman spectral correlation coefficients.
  • the categorization module 404 obtains the mean set ⁇ n ⁇ , the variance set ⁇ n ⁇ , and the information entropy set ⁇ S n ⁇ of the similarity index after the normal sample is classified as the normal sample. Training data, and the cancer sample is classified into a mean set ⁇ c ⁇ , a variance set ⁇ c ⁇ , and an information entropy set ⁇ S c ⁇ of the similarity index after at least one category, as a cancer sample. Training data. It can be understood that since cancer can have many different periods, cancer samples can be classified based on different periods, for example, classified into four categories and the like.
  • the training module 302 trains the preset machine classification model based on the training data of the normal sample and the cancer sample, and the classification module 303 inputs the pathological slice image to be classified into the trained machine classification model. Sorting by the trained machine classification model to determine whether the pathological slice image is a normal slice image or a cancer slice image, and the type of the trained machine classification model output is used as the type of the pathological slice image, wherein the pathology
  • the type of the slice image may be a normal slice image or a cancer slice image.
  • the above machine classification model may be a model in a support vector machine neural network, or a Bayesian linear or nonlinear classifier, or other linear or nonlinear classifier with machine learning function, in practical applications.
  • the model used can be selected according to specific needs, and is not limited herein.
  • the information entropy is introduced to characterize the similarity or chaos of the structure of the substance, and the degree of differentiation of the tissue cells is statistically and objectively quantitatively described, and the degree of differentiation of the tissue cells can be directly reflected, and the similarity index is combined.
  • Mean and variance are a set of criterion criteria for distinguishing tumor cells or tissues from normal cells or tissues, which can effectively improve the accuracy of intelligent classification of pathological slice images.
  • a femtosecond laser with a wavelength of 780 nm and a repetition rate of 75.4 MHz was used as the excitation source.
  • Two-photon fluorescence lifetime imaging analysis was performed on the LEICA DMIRE2 confocal microscope system with TCSPC from B&H. All samples were imaged using a 60x objective.
  • the acquired two-photon fluorescence lifetime image is imported into MATLAB, and a program (which can be a program corresponding to the intelligent classification method for pathological slice images in the embodiment of the present invention) is used to perform phase mapping calculation on the fluorescence lifetime image, and pixels with similar structures mean The fluorescence lifetimes are also similar.
  • the data points clustered in one block, and the structure in the fluorescence lifetime image can be segmented according to the phase coordinate clustering.
  • the purpose of this step is to segment the tissue part of the melanocytes from the image and extract the fluorescence lifetime of the HE stain for the mean statistics and entropy calculation.
  • the sample data is linearly classified by the support vector machine on the ⁇ _ ⁇ plane, as shown in Fig. 5b. It can be seen from the figure that the data of the normal sample and the cancer sample are partially intertwined on the ⁇ _ ⁇ plane, and cannot be linearly classified, indicating that the possibility of misjudgment can be made only when the fluorescence lifetime mean and the variance are used for the pathological slice diagnosis. great.
  • the sample data After introducing the entropy value, the sample data can obtain very obvious linear classification effects by performing support vector machine linear classification on the S_ ⁇ plane (Fig. 5c) or the S_ ⁇ plane (Fig. 5d).
  • the disclosed apparatus and method may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of the modules is only a logical function division.
  • there may be another division manner for example, multiple modules or components may be combined or Can be integrated into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or module, and may be electrical, mechanical or otherwise.
  • the modules described as separate components may or may not be physically separated.
  • the components displayed as modules may or may not be physical modules, that is, may be located in one place, or may be distributed to multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. in.
  • the above integrated modules can be implemented in the form of hardware or in the form of software functional modules.
  • the integrated modules if implemented in the form of software functional modules and sold or used as separate products, may be stored in a computer readable storage medium.
  • the technical solution of the present invention which is essential or contributes to the prior art, or all or part of the technical solution, may be embodied in the form of a software product stored in a storage medium.
  • a number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

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Abstract

一种病理切片图像智能分类方法及装置，方法包括：对预置的正常样本及癌症样本中的每一幅病理切片图像进行图像处理，得到正常样本和癌症样本的训练数据，训练数据包含相似度指标的均值集合、方差集合及信息熵集合，并基于训练数据对预置的机器分类模型进行训练，以便利用训练后的机器分类模型确定待分类病理切片图像的类型，通过引入信息熵作为图像结构混乱程度的一个独立维度，则可达到定量描述肿瘤细胞或组织的分化程度的目的，且通过利用包含信息熵集合的训练数据对机器分类模型进行训练，使得能够有效提高病理切片图像智能分类的准确性。

Description

病理切片图像智能分类方法及装置 技术领域
本发明涉及图像处理领域，尤其涉及一种病理切片图像智能分类方法及装置。
背景技术
正常细胞在个体发育中表现出来的形态结构和功能上发生稳定性的差异过程称为细胞分化，分化程度越高，差异就越大。体内部分细胞由于基因突变失去正常死亡调控，分裂增生失去控制，无序过度增殖等导致正常组织结构遭受破坏，成为癌症细胞。分化在肿瘤病理学中常指肿瘤细胞与其起源的正常细胞的相似程度，是肿瘤良恶性鉴别的主要依据，分化高的肿瘤具有良性行为，分化低的肿瘤多有恶性表现。
病理切片图像分析的主要任务就是甄别肿瘤细胞或组织在显微镜下表现出有别于正常细胞或组织的结构特征，通常需要辅助予HE染色等标记手段。光镜观察对细胞核形态只能作大致的描述，易带主观性，缺乏精确而更为客观的定量标准。近年来，科技进步促使病理学的研究手段已远远超越了传统的形态观察，而涌现出许多新方法、新技术，从根本上要求分析工作往客观化、定量化的标准发展。形态定量分析量化反映组织和细胞的形态结构，可排除主观因素的影响，在肿瘤病理方面图像分析主要是核形态参数的测定，区别癌前病变和癌，区别肿瘤的良恶性，肿瘤组织病理分级及判断预后等。随着电子计算机的发展，研究者开始尝试把医学模拟图像转化为数字图像，开展了计算机辅助诊断的初步研究，试图在一定程度上辅助医生判读医学图像，排除人为主观因素，提高诊断准确性和效率。
光学成像技术的发展促使数据采集高维化，传统的二维灰度值图像已经很难理解如此海量的信息。医学图像分析不再局限于过去具有明显诊断特征的病种，开始扩展到多种不同器官、解剖形态、功能过程的图像，试图利用自动精 确定量的计算机辅助图像分析，帮助临床医生和研究者高效准确地处理海量图像信息，然而，目前的技术还是难以实现有效的确定病理切片图像是正常切片图像还是癌症切片图像，准确性较低。
发明内容
本发明的主要目的在于提供一种病理切片图像智能分类方法及装置，旨在解决现有技术中对病理切片图像进行分类的准确性较低的技术问题。
为实现上述目的，本发明第一方面提供一种病理切片图像智能分类方法，所述方法包括：
对预置的正常样本及癌症样本中的每一幅病理切片图像进行图像处理，得到所述正常样本和所述癌症样本的训练数据，所述训练数据包含相似度指标的均值集合、方差集合及信息熵集合；
基于所述正常样本和所述癌症样本的训练数据对预置的机器分类模型进行训练，得到训练后的机器分类模型；
将待分类病理切片图像输入所述训练后的机器分类模型，且将所述训练后的机器分类模型输出的类型作为所述待分类病理切片图像的类型。
为实现上述目的，本发明第二方面提供一种病理切片图像智能分类装置，该装置包括：
处理模块，用于对预置的正常样本及癌症样本中的每一幅病理切片图像进行图像处理，得到所述正常样本和所述癌症样本的训练数据，所述训练数据包含相似度指标的均值集合、方差集合及信息熵集合；
训练模块，用于基于所述正常样本和所述癌症样本的训练数据对预置的机器分类模型进行训练，得到训练后的机器分类模型；
分类模块，用于将待分类病理切片图像输入所述训练后的机器分类模型，且将所述训练后的机器分类模型输出的类型作为所述待分类病理切片图像的类型。
本发明提供一种病理切片图像智能分类方法，该方法包括：对预置的正常样本及癌症样本中的每一幅病理切片图像进行图像处理，得到正常样本和癌症样本的训练数据，其中，训练数据包含相似度指标的均值集合、方差集合及信 息熵集合，并基于正常样本和癌症样本的训练数据对预置的机器分类模型进行训练，得到训练后的机器分类模型，将待分类病理切片图像输入该训练后的机器分类模型，且将该训练后的机器分类模型输出的类型作为该待分类病理切片图像的类型。相对于现有技术，不仅使用了相似度指标的均值及方差用于判别正常切片图像和癌症切片图像之间的差异，还引入了信息熵作为图像结构混乱程度的一个独立维度，则通过使用信息熵可达到定量描述肿瘤细胞或组织的分化程度的目的，且通过包含正常样本和癌症样本的相似度指标的均值集合、方差集合及信息熵集合的训练数据对机器分类模型进行训练，并通过该机器分类模型对病理切片图形进行分类，使得能够有效提高病理切片图像智能分类的准确性。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本发明第一实施例中病理切片图像智能分类方法的流程示意图；
图2为本发明第二实施例中病理切片图像智能分类方法的流程示意图；
图3为本发明第三实施例中病理切片图像智能分类装置的功能模块的示意图；
图4为本发明第四实施例中病理切片图像智能分类装置的功能模块的示意图；
图5a为切片HE染色荧光寿命在均值μ、方差σ和熵值S构成的三维空间分布；
图5b为切片HE染色荧光寿命在μ_σ平面内的支持向量机线性判别；
图5c为切片HE染色荧光寿命在S_μ平面内的支持向量机线性判别；
图5d为切片HE染色荧光寿命在S_σ平面内的支持向量机线性判别。
具体实施方式
为使得本发明的发明目的、特征、优点能够更加的明显和易懂，下面将结 合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而非全部实施例。基于本发明中的实施例，本领域技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
由于现有技术中对病理切片图像进行分类的准确性较低技术问题。
为了解决上述技术问题，本发明提出一种病理切片图像智能分类方法，该方法中，不仅使用了相似度指标的均值及方差用于判别正常切片图像和癌症切片图像之间的差异，还引入了信息熵作为图像结构混乱程度的一个独立维度，则可达到定量描述肿瘤细胞或组织的分化程度的目的，且通过包含正常样本和癌症样本的相似度指标的均值集合、方差集合及信息熵集合的训练数据对机器分类模型进行训练，并通过该机器分类模型对病理切片图形进行分类，使得能够有效提高病理切片图像智能分类的准确性。
请参阅图1，为本发明第一实施例中病理切片图像智能分类方法的流程图，该方法包括：
步骤101、对预置的正常样本及癌症样本中的每一幅病理切片图像进行图像处理，得到所述正常样本和所述癌症样本的训练数据，所述训练数据包含相似度指标的均值集合、方差集合及信息熵集合；
在本发明实施例中，病理切片图像智能分类方法由病理切片图像智能分类装置(以下简称为：分类装置)实现。
在本发明实施例中，需要先训练机器分类模型，以便使用机器分类模型对待分类病理切片图像进行分类。其中，为了对机器分类模型进行训练，需要预先准备训练样本，该训练样本包括正常样本和癌症样本，其中，正常样本中包含确诊为正常的病理切片图像，癌症样本中包含确诊为已发生癌变的病理切片图像。
其中，分类装置将预置的正常样本及癌症样本中的每一幅病理切片图像进行图像处理，得到正常样本和癌症样本的训练数据，其中，正常样本的训练数据中包含所有正常的病理切片图像的相似度指标的均值集合、相似度指标的方差集合及信息熵集合，癌症样本的训练数据中包含所有癌变的病理切片图像的 相似度指标的均值集合、相似度指标的方差集合及信息熵集合。
步骤102、基于所述正常样本和所述癌症样本的训练数据对预置的机器分类模型进行训练，得到训练后的机器分类模型；
步骤103、将待分类病理切片图像输入所述训练后的机器分类模型，且将所述训练后的机器分类模型输出的类型作为所述待分类病理切片图像的类型。
在本发明实施例中，分类装置将基于正常样本和癌症样本的训练数据对预置的机器分类模型进行训练，得到训练后的机器分类模型，并将待分类病理切片图像输入该训练后的机器分类模型，将训练后的机器分类模型输出的的类型作为该待分类病理切片图像的类型。
在本发明实施例中，分类装置对预置的正常样本及癌症样本中的每一幅病理切片图像进行图像处理，得到正常样本和癌症样本的训练数据，其中，训练数据包含相似度指标的均值集合、方差集合及信息熵集合，并基于正常样本和癌症样本的训练数据对预置的机器分类模型进行训练，得到训练后的机器分类模型，将待分类病理切片图像输入该训练后的机器分类模型，且将该训练后的机器分类模型输出的类型作为该待分类病理切片图像的类型。相对于现有技术，不仅使用了相似度指标的均值及方差用于判别正常切片图像和癌症切片图像之间的差异，还引入了信息熵作为图像结构混乱程度的一个独立维度，则可达到定量描述肿瘤细胞或组织的分化程度的目的，且通过包含正常样本和癌症样本的相似度指标的均值集合、方差集合及信息熵集合的训练数据对机器分类模型进行训练，并通过该机器分类模型对病理切片图形进行分类，使得能够有效提高病理切片图像智能分类的准确性。
请参阅图2，为本发明第二实施例中病理切片图像智能分类方法的流程图，该方法包括：
在本发明实施例中，为了得到正常样本及癌症样本的训练数据，将对正常样本及癌症样本中的每一幅病理切片图像按照步骤201至步骤203进行处理，得到每一幅病理切片图像的相似度指标的均值、方差及信息熵。具体如下：
步骤201、读取病理切片图像含结构信息的三维图像，所述三维图像由各像素点的光子数分布构成第三维度；
在本发明实施例中，对于每一幅病理切片图像，分类装置将读取该病理切片图像含结构信息的三维图像，该三维图像由各像素点坐标构成第一、第二维度，由各像素点的光子数分布构成第三维度。其中，在该三维图像中，在垂直于像素坐标的第三维度存储包含结构信息的数据，且在荧光寿命成像中为光子衰减数列，而在拉曼成像中为拉曼光谱。
步骤202、提取所述三维图像中由第三维度数据集决定的各像素点的相似度指标；
在本发明实施例中，分类装置将提取三维图像中由第三维度数据集决定的各像素点的相似度指标。
其中，该三维图像可以是荧光寿命成像也可以是拉曼成像。
其中，相关光子计数(TCSPC)推动荧光寿命成像(FLIM)，荧光基团的荧光寿命被认为只与其结构和所处微环境有关，不受激发光强、分子浓度等影响，因此可用来表征被标记的物质结构之间的相似程度。另外，拉曼光谱反映的是物质内部核型结构，通过对拉曼光谱进行互相关运算得到拉曼光谱相关系数矩阵，由该拉曼光谱相关系数矩阵可进一步反映组织内部结构的相似性。不管是荧光寿命或是拉曼光谱相关系数矩阵都可以作为判别医学图像各像素点之间的相似程度的定量指标，即作为相似度指标的计算参数。有了图像相似度的定量指标，便可对医学图像做相似度统计分析。正常组织里面细胞分化程度高，各像素点之间呈现出比较宽泛的相似度分布，而肿瘤组织的分化程度底，表现为各像素点的结构差异变小，相似度分布相对教窄，而且肿瘤恶性越高，分化越低，相似度分布就约集中。一般而言，可以把病理切片图像的相似度指标作平均统计，可以得到相似度指标的均值和方差两个独立指标用于判别正常细胞或组织与癌症细胞或组织之间的差异。且可以进一步引入信息熵作为图像结构混乱程度的一个独立维度指标，则可达到定量描述肿瘤细胞或组织的分化程度，结合相似度指标的均值和方差作为肿瘤细胞或组织区别于正常细胞或组织的一组判据标准，提高准确性，此即是本发明的原理。
若该三维图像为荧光寿命成像，则可以通过拟合或相位映射算法计算荧光寿命或者相位映射坐标，具体的，上述步骤202可以为以下步骤A，或者步骤B：
步骤A，利用采集到的各像素点对应的时间衰减曲线进行最小二乘拟合得到各像素点的荧光寿命，将该各像素点的荧光寿命作为该各像素点的相似度指标。
其中，像素点的时间衰减曲线为：
Ii，j(t)＝I0 i，jexp(-t/τi，j)
其中，i及j表示像素点在三维图像中的坐标，Ii，j(t)表示像素点ij在t时刻衰减后的荧光强度，t表示时间，I0 i，j表示像素点ij总的荧光强度，τi，j表示像素点ij的荧光寿命。其中，像素点ij总的荧光强度可以基于三维图像中该像素点ij光子数衰减数列确定。
或者，
步骤B，利用预置的相位映射算法计算所述各像素点的荧光寿命，将所述各像素点的荧光寿命作为所述各像素点的相似度指标。
其中，相位映射算法包括：
Figure PCTCN2017075566-appb-000001
Figure PCTCN2017075566-appb-000002
Figure PCTCN2017075566-appb-000003
其中，ω表示用于实现荧光寿命成像使用的激光脉冲的激光脉冲角频率，τi，j表示像素点ij的荧光寿命。
在本发明实施例中，在三维图像为荧光寿命成像的情况下，可以通过上述方式得到各像素点的荧光寿命，并将各像素点的荧光寿命作为各像素点的相似度指标。
此外，在三维图像为拉曼成像的情况下，可以通过皮尔逊互相关算法得到各像素点的相似度指标，具体的，上述步骤202可以为以下步骤C，具体的：
步骤C、利用预置的皮尔逊互相关算法对各像素点的拉曼光谱分别进行两两互相关运算，得到所述各像素点的拉曼光谱相关系数矩阵，将所述各像素点的拉曼光谱相关系数矩阵作为所述各像素点的相似度指标。
其中，在拉曼成像中，三维图像的数据中包含的是拉曼光谱，上述皮尔逊互相关算法具体如下：
Figure PCTCN2017075566-appb-000004
其中，Cl，m表示拉曼光谱相关系数矩阵，Rl和Rm分别代表不同两个像素点的拉曼光谱，
Figure PCTCN2017075566-appb-000005
和
Figure PCTCN2017075566-appb-000006
代表各自两条谱线的平均值，k是拉曼光谱中的第k个数据点，N是光谱数据点总数，因此拉曼光谱相关系数矩阵Cl，m为N×N对称矩阵。
在本发明实施例中，分析装置在得到各像素点的拉曼光谱系数矩阵之后，将该各像素点的拉曼光谱系数矩阵作为各像素点的相似度指标。
步骤203、利用所述各像素点的相似度指标计算所述病理切片图像的相似度指标的均值，方差及信息熵；
在本发明实施例中，分析装置在得到一幅病理切片图像中各像素点的相似度指标之后，将利用该各像素点的相似度指标进行求平均运算，得到该病理切片图像的相似度指标的均值，并利用各像素点的相似度指标进行求方差运算，得到该病理切片图像的相似度指标的方差，且利用各像素点的相似度指标进行熵运算，得到该病理切片图像的信息熵。
具体的，若病理切片图像的三维图像是荧光寿命成像，则可将各像素点的荧光寿命分别进行求平均运算、求方差运算及求熵运算，得到相似度指标的均值、方差及信息熵，或者，若病理切片图像的三维图像是拉曼成像，则可利用各像素点的拉曼光谱相关系数矩阵的上三角阵元素分别进行求平均运算、求方差运算及求熵运算，得到相似度指标的均值、方差及信息熵。
其中，信息熵的计算可以采用香农熵定义或其它信息熵定义，其中，香农信息熵定义公式如下：
Figure PCTCN2017075566-appb-000007
其中，
Figure PCTCN2017075566-appb-000008
或者
Figure PCTCN2017075566-appb-000009
其中，S表示信息熵，pi，j表示像素点ij的荧光寿命占所有像素点的荧光寿命总和的概率，或者，像素点ij的拉曼光谱相关系数占所有像素点的拉曼光谱相关系数总和的概率。
其中，
Figure PCTCN2017075566-appb-000010
表示拉曼光谱相关系数矩阵的上三角阵元素。
在本发明实施例中，通过对正常样本及癌症样本中的每一幅病理切片图像按照上述步骤201至步骤203进行处理，能够得到每一幅病理切片图像的相似度指标的均值、方差及信息熵。
步骤204、将所述正常样本中所有病理切片图像的相似度指标的均值、方差及信息熵分别归类为所述正常样本的相似度指标的均值集合、方差集合及信息熵集合，以作为所述正常样本的训练数据，及将所述癌症样本中所有病理切片图像的相似度指标的均值、方差及信息熵分别归类为至少一种类别的相似度指标的均值集合、方差集合及信息熵集合，以作为所述癌症样本的训练数据；
在本发明实施例中，分析装置将得到正常样本归类后的相似度指标的均值集合{μn}、方差集合{σn}和信息熵集合{Sn}，以作为该正常样本的训练数据，且将得到癌症样本进行归类为至少一种类别后的相似度指标的均值集合{μe}、方差集合{σc}及信息熵集合{Se}，以作为癌症样本的训练数据。可以理解的是，由于癌症可以有很多不同的时期，可以将癌症样本基于不同的时期进行归类，例如归为4类等等。
步骤205、基于所述正常样本和所述癌症样本的训练数据对预置的机器分类模型进行训练，得到训练后的机器分类模型；
步骤206、将待分类病理切片图像输入所述训练后的机器分类模型，且将所述训练后的机器分类模型输出的类型作为所述待分类病理切片图像的类型。
在本发明实施例中，分析装置将基于正常样本及癌症样本的训练数据对预置的机器分类模型进行训练，并将待分类病理切片图像输入训练后的机器分类模型中，由该训练后的机器分类模型进行分类，以确定该病理切片图像是正常切片图像还是癌症切片图像，且将该训练后的机器分类模型输出的类型作为该病理切片图像的类型，其中，该病理切片图像的类型可以是正常切片图像，也可以是癌症切片图像。
其中，上述机器分类模型可以是支持向量机神经网络模型，也可以是贝叶斯的线性或非线性分类器，或者是其他具有机器学习功能的线性或非线性分类器，在实际应用中，可根据具体的需要选择所使用的模型，此处不做限定。
在本发明实施例中，通过引入信息熵来表征物质结构的相似性或混乱程度，从统计上客观定量描述组织细胞的分化程度，能够直接的反应组织细胞的分化 程度，并结合相似度指标的均值及方差作为肿瘤细胞或组织区别于正常细胞或组织的一组判据标准，能够有效提高病理切片图像智能分类的准确性。
请参阅图3，为本发明第三实施例中病理切片图像智能分类装置的功能模块的示意图，该装置包括：
处理模块301，用于对预置的正常样本及癌症样本中的每一幅病理切片图像进行图像处理，得到所述正常样本和所述癌症样本的训练数据，所述训练数据包含相似度指标的均值集合、方差集合及信息熵集合；
在本发明实施例中，需要先训练机器分类模型，以便使用机器分类模型对待分类病理切片图像进行分类。其中，为了对机器分类模型进行分类，需要预先准备训练样本，该训练样本包括正常样本和癌症样本，其中，正常样本中包含确诊为正常的病理切片图像，癌症样本中包含确诊为已发生癌变的病理切片图像。
其中，处理模块301将预置的正常样本及癌症样本中的每一幅病理切片图像进行图像处理，得到正常样本和癌症样本的训练数据，其中，正常样本的训练数据中包含所有正常的病理切片图像的相似度指标的均值集合、相似度指标的方差集合及信息熵集合，癌症样本的训练数据中包含所有癌变的病理切片图像的相似度指标的均值集合、相似度指标的方差集合及信息熵集合。
训练模块302，用于基于所述正常样本和所述癌症样本的训练数据对预置的机器分类模型进行训练，得到训练后的机器分类模型；
分类模块303，用于将待分类病理切片图像输入所述训练后的机器分类模型，且将所述训练后的机器分类模型输出的类型作为所述待分类病理切片图像的类型。
在本发明实施例中，训练模块302将基于正常样本和癌症样本的训练数据对预置的机器分类模型进行训练，得到训练后的机器分类模型，并由分类模块303将待分类病理切片图像输入该训练后的机器分类模型，将训练后的机器分类模型输出的的类型作为该待分类病理切片图像的类型。
在本发明实施例中，分类装置对预置的正常样本及癌症样本中的每一幅病理切片图像进行图像处理，得到正常样本和癌症样本的训练数据，其中，训练 数据包含相似度指标的均值集合、方差集合及信息熵集合，并基于正常样本和癌症样本的训练数据对预置的机器分类模型进行训练，得到训练后的机器分类模型，将待分类病理切片图像输入该训练后的机器分类模型，且将该训练后的机器分类模型输出的类型作为该待分类病理切片图像的类型。相对于现有技术，不仅使用了相似度指标的均值及方差用于判别正常切片图像和癌症切片图像之间的差异，还引入了信息熵作为图像结构混乱程度的一个独立维度，则可达到定量描述肿瘤细胞或组织的分化程度的目的，且通过包含正常样本和癌症样本的相似度指标的均值集合、方差集合及信息熵集合的训练数据对机器分类模型进行训练，并通过该机器分类模型对病理切片图形进行分类，使得能够有效提高病理切片图像智能分类的准确性。
请参阅图4，为本发明第四实施例中病理切片图像智能分类装置的功能模块的示意图，该装置包括第三实施例中的处理模块301、训练模块302及分类模块303，且与第三实施例中描述的内容相似，此处不做赘述。
在本发明实施例中，处理模块301包括：读取模块401、提取模块402、计算模块403、归类模块404，所述读取模块401、提取模块402及所述计算模块403用于对对所述正常样本及所述癌症样本中的每一幅病理切片图像进行处理；
所述读取模块401具体用于读取病理切片图像含结构信息的三维图像，所述三维图像由各像素点的光子数分布构成第三维度；
在本发明实施例中，对于每一幅病理切片图像，读取模块401将读取该病理切片图像含结构信息的三维图像，该三维图像由各像素点坐标构成第一、第二维度，由各像素点的光子数分布构成第三维度。其中，在该三维图像中，在垂直于像素坐标的第三维度存储包含结构信息的数据，且在荧光寿命成像中为光子衰减数列，而在拉曼成像中为拉曼光谱。
所述提取模块402具体用于提取所述三维图像中由第三维度数据集决定的各像素点的相似度指标；
所述计算模块403具体用于利用所述各像素点的相似度指标计算所述病理切片图像的相似度指标的均值，方差及信息熵；
所述归类模块404用于将所述正常样本中所有病理切片图像的相似度指标的均值、方差及信息熵分别归类为所述正常样本的相似度指标的均值集合、方差集合及信息熵集合，以作为所述正常样本的训练数据，及将所述癌症样本中所有病理切片图像的相似度指标的均值、方差及信息熵分别归类为至少一种类别的相似度指标的均值集合、方差集合及信息熵集合，以作为所述癌症样本的训练数据。
在本发明实施例中，提取模块402将提取三维图像中由第三维度数据集决定的各像素点的相似度指标。
其中，该三维图像可以是荧光寿命成像也可以是拉曼成像。
其中，相关光子计数(TCSPC)推动荧光寿命成像(FLIM)，荧光基团的荧光寿命被认为只与其结构和所处微环境有关，不受激发光强、分子浓度等影响，因此可用来表征被标记的物质结构之间的相似程度。另外，拉曼光谱反映的是物质内部核型结构，通过对拉曼光谱进行互相关运算得到拉曼光谱相关系数矩阵，由该拉曼光谱相关系数矩阵可进一步反映组织内部结构的相似性。不管是荧光寿命或是拉曼光谱相关系数矩阵都可以作为判别医学图像各像素点之间的相似程度的定量指标，即作为相似度指标的计算参数。有了图像相似度的定量指标，便可对医学图像做相似度统计分析。正常组织里面细胞分化程度高，各像素点之间呈现出比较宽泛的相似度分布，而肿瘤组织的分化程度底，表现为各像素点的结构差异变小，相似度分布相对教窄，而且肿瘤恶性越高，分化越低，相似度分布就约集中。一般而言，可以把病理切片图像的相似度指标作平均统计，可以得到相似度指标的均值和方差两个独立指标用于判别正常细胞或组织与癌症细胞和组织之间的差异。且可以进一步引入信息熵作为图像结构混乱程度的一个独立维度指标，则可达到定量描述肿瘤细胞或组织的分化程度，结合相似度指标的均值和方差作为肿瘤细胞或组织区别于正常细胞或组织的一组判据标准，提高准确性，此即是本发明的原理。
若该三维图像为荧光寿命成像，则可以通过拟合或相位映射算法计算荧光寿命或者相位映射坐标，则所述提取模块402具体用于：
利用采集到的各像素点对应的时间衰减曲线进行最小二乘拟合得到各像素点的荧光寿命，将所述各像素点的荧光寿命作为所述各像素点的相似度指标。
其中，像素点的时间衰减曲线为：
Ii，j(t)＝I0 i，jexp(-t/τi，j)
其中，i及j表示像素点在三维图像中的坐标，Ii，j(t)表示像素点ij在t时刻衰减后的荧光强度，t表示时间，I0 i，j表示像素点ij总的荧光强度，τi，j表示像素点ij的荧光寿命。其中，像素点ij总的荧光强度可以基于三维图像中该像素点ij光子数衰减数列确定。
或者，
该提取模块402具体用于利用预置的相位映射算法计算所述各像素点的荧光寿命，将所述各像素点的荧光寿命作为所述各像素点的相似度指标。
其中，相位映射算法包括：
Figure PCTCN2017075566-appb-000011
Figure PCTCN2017075566-appb-000012
Figure PCTCN2017075566-appb-000013
其中，ω表示用于实现荧光寿命成像使用的激光脉冲的激光脉冲角频率，τi，j表示像素点ij的荧光寿命。
在本发明实施例中，在三维图像为荧光寿命成像的情况下，可以通过上述方式得到各像素点的荧光寿命，并将各像素点的荧光寿命作为各像素点的相似度指标。
此外，在三维图像为拉曼成像的情况下，可以通过皮尔逊互相关算法得到各像素点的相似度指标，具体的，所述提取模块402具体用于：
利用预置的皮尔逊互相关算法对各像素点的拉曼光谱分别进行两两互相关运算，得到所述各像素点的拉曼光谱相关系数矩阵，将所述各像素点的拉曼光谱相关系数矩阵作为所述各像素点的相似度指标。
其中，在拉曼成像中，三维图像的数据中包含的是拉曼光谱，上述皮尔逊互相关算法具体如下：
Figure PCTCN2017075566-appb-000014
其中，Cl，m表示拉曼光谱相关系数矩阵，Rl和Rm分别代表不同两个像素点的拉曼光谱，
Figure PCTCN2017075566-appb-000015
和
Figure PCTCN2017075566-appb-000016
代表各自两条谱线的平均值，k是拉曼光谱中的第k个数据点，N是光谱数据点总数，因此拉曼光谱相关系数矩阵Cl，m为N×N对称矩阵。在本发明实施例中，分析装置在得到各像素点的拉曼光谱系数矩阵之后，将该各像素点的拉曼光谱系数矩阵作为各像素点的相似度指标。
在本发明实施例中，分析装置在得到一幅病理切片图像中各像素点的相似度指标之后，计算模块403将利用该各像素点的相似度指标进行求平均运算，得到该病理切片图像的相似度指标的均值，并利用各像素点的相似度指标进行求方差运算，得到该病理切片图像的相似度指标的方差，且利用各像素点的相似度指标进行熵运算，得到该病理切片图像的信息熵。
具体的，若病理切片图像的三维图像是荧光寿命成像，则计算模块403可将各像素点的荧光寿命分别进行求平均运算、求方差运算及求熵运算，得到相似度指标的均值、方差及信息熵，或者，若病理切片图像的三维图像是拉曼成像，则计算模块403可利用各像素点的拉曼光谱相关系数矩阵的上三角阵元素分别进行求平均运算、求方差运算及求熵运算，得到相似度指标的均值、方差及信息熵。
其中，信息熵的计算可以采用香农熵定义或其它信息熵定义，其中，香农信息熵定义公式如下：
Figure PCTCN2017075566-appb-000017
其中，
Figure PCTCN2017075566-appb-000018
或者
Figure PCTCN2017075566-appb-000019
其中，S表示信息熵，pi，j表示像素点ij的荧光寿命占所有像素点的荧光寿命的总和的概率，或者，像素点ij的拉曼光谱相关系数占所有像素点的拉曼光谱相关系数的总和的概率；
其中，
Figure PCTCN2017075566-appb-000020
表示拉曼光谱相关系数矩阵的上三角阵元素。
在本发明实施例中，归类模块404将得到正常样本归类后的相似度指标的均值集合{μn}、方差集合{σn}和信息熵集合{Sn}，以作为该正常样本的训练数据，且将得到癌症样本进行归类为至少一种类别后的相似度指标的均值集合{μc}、方差集合{σc}及信息熵集合{Sc}，以作为癌症样本的训练数据。可以理 解的是，由于癌症可以有很多不同的时期，可以将癌症样本基于不同的时期进行归类，例如归为4类等等。
在本发明实施例中，训练模块302将基于正常样本及癌症样本的训练数据对预置的机器分类模型进行训练，并由分类模块303将待分类病理切片图像输入训练后的机器分类模型中，由该训练后的机器分类模型进行分类，以确定该病理切片图像是正常切片图像还是癌症切片图像，且将该训练后的机器分类模型输出的类型作为该病理切片图像的类型，其中，该病理切片图像的类型可以是正常切片图像，也可以是癌症切片图像。
其中，上述机器分类模型可以是支持向量机神经网络中的模型，也可以是贝叶斯的线性或非线性分类器，或者是其他具有机器学习功能的线性或非线性分类器，在实际应用中，可根据具体的需要选择所使用的模型，此处不做限定。
在本发明实施例中，通过引入信息熵来表征物质结构的相似性或混乱程度，从统计上客观定量描述组织细胞的分化程度，能够直接的反应组织细胞的分化程度，并结合相似度指标的均值及方差作为肿瘤细胞或组织区别于正常细胞或组织的一组判据标准，能够有效提高病理切片图像智能分类的准确性。
为了验证通过上述的方法及装置能够提高病理切片图像智能分类的准确性，进行以下实验：
从医院皮肤科取得经过HE染色的正常皮肤组织和皮肤癌组织样本的病理切片标本，对正常样本和癌症样本进行分别标注。
采用波长为780nm、重复频率为75.4MHz的飞秒激光器作为激发光源，在LEICA DMIRE2共聚焦显微镜系统上配备德国B&H公司的TCSPC实现双光子荧光寿命成像分析。所有样片均采用60倍物镜进行图像采集。
将获取的双光子荧光寿命图像导入MATLAB，编写程序(该程序可以为本发明实施例中的病理切片图像智能分类方法对应的程序)对荧光寿命图像进行相位映射计算，结构相近的像素点意味着荧光寿命也相近，在相位坐标下表现为聚类在一块的数据点，根据相位坐标聚类可以分割出荧光寿命图像中的结构。此步骤目的是从图像中分割出黑色素细胞的组织部分像素点，提取其HE染色的荧光寿命，用于均值统计和熵值计算。
对采集到的50组癌细胞图像数据和50组正常细胞图像数据分别进行均值μ、方差σ和熵值S统计分析，其中所有数据点在μ_σ_S三维空间的分布如图5a所示，为方便观察我们用深黑色标记癌症样本数据，即编号A所示，灰色标记正常样本数据，即编号B所示，并分别把数据分布映射到各自不同的三个平面上。
将样本数据在μ_σ平面上做支持向量机线性分类，如图5b所示。从图中可以看出正常样本和癌症样本的数据在μ_σ平面上部分交织在一起，不能做线性分类，说明只用荧光寿命均值和方差两个参量来做病理切片诊断时，误判的可能性极大。
引入熵值之后，样本数据无论是在S_μ平面(图5c)或是在S_σ平面(图5d)上做支持向量机线性分类均能获得非常明显的线性分类的效果。
最后，我们分别从正常样本和癌症样本图像中随机以7∶3的比例选取训练样本和测试样本，进行交叉验证实验。采用线性核进行的支持向量机交叉验证结果为，当仅采用μ和σ两个参数做支持向量机训练和判别时，得到线性判别准确率为85.9％；而采用μ、σ和S三个参数做支持向量机训练和判别时，得到线性判别准确率为97.2％，明显优于两个参数的线性判别结果。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述模块的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个模块或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或模块的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的模块可以是或者也可以不是物理上分开的，作为模块显示的部件可以是或者也可以不是物理模块，即可以位于一个地方，或者也可以分布到多个网络模块上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
另外，在本发明各个实施例中的各功能模块可以集成在一个处理模块中，也可以是各个模块单独物理存在，也可以两个或两个以上模块集成在一个模块 中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。
所述集成的模块如果以软件功能模块的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
需要说明的是，对于前述的各方法实施例，为了简便描述，故将其都表述为一系列的动作组合，但是本领域技术人员应该知悉，本发明并不受所描述的动作顺序的限制，因为依据本发明，某些步骤可以采用其它顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于优选实施例，所涉及的动作和模块并不一定都是本发明所必须的。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见其它实施例的相关描述。
以上为对本发明所提供的一种病理切片图像智能分类方法及装置的描述，对于本领域的技术人员，依据本发明实施例的思想，在具体实施方式及应用范围上均会有改变之处，综上，本说明书内容不应理解为对本发明的限制。

Claims (10)

  1. 一种病理切片图像智能分类方法，其特征在于，所述方法包括：
    对预置的正常样本及癌症样本中的每一幅病理切片图像进行图像处理，得到所述正常样本和所述癌症样本的训练数据，所述训练数据包含相似度指标的均值集合、方差集合及信息熵集合；
    基于所述正常样本和所述癌症样本的训练数据对预置的机器分类模型进行训练，得到训练后的机器分类模型；
    将待分类病理切片图像输入所述训练后的机器分类模型，且将所述训练后的机器分类模型输出的类型作为所述待分类病理切片图像的类型。
  2. 根据权利要求1所述的方法，其特征在于，所述对预置的正常样本及癌症样本中的每一幅病理切片图像进行图像处理，得到所述正常样本及所述癌症样本的训练数据的步骤包括：
    对所述正常样本及所述癌症样本中的每一幅病理切片图像进行以下处理：
    读取病理切片图像含结构信息的三维图像，所述三维图像由各像素点的光子数分布构成第三维度；
    提取所述三维图像中由第三维度数据集决定的各像素点的相似度指标；
    利用所述各像素点的相似度指标计算所述病理切片图像的相似度指标的均值，方差及信息熵；
    所述步骤还包括：
    将所述正常样本中所有病理切片图像的相似度指标的均值、方差及信息熵分别归类为所述正常样本的相似度指标的均值集合、方差集合及信息熵集合，以作为所述正常样本的训练数据，及将所述癌症样本中所有病理切片图像的相似度指标的均值、方差及信息熵分别归类为至少一种类别的相似度指标的均值集合、方差集合及信息熵集合，以作为所述癌症样本的训练数据。
  3. 根据权利要求2所述的方法，其特征在于，若所述三维图像为荧光寿命成像，则所述提取所述三维图像中由第三维度数据集决定的各像素点的相似度指标的步骤包括：
    利用采集到的各像素点对应的时间衰减曲线进行最小二乘拟合得到各像素 点的荧光寿命，将所述各像素点的荧光寿命作为所述各像素点的相似度指标；
    或者，
    利用预置的相位映射算法计算所述各像素点的荧光寿命，将所述各像素点的荧光寿命作为所述各像素点的相似度指标。
  4. 根据权利要求3所述的方法，其特征在于，若所述三维图像为拉曼成像，则所述提取所述三维图像中由第三维度数据集决定的各像素点的相似度指标的步骤包括：
    利用预置的皮尔逊互相关算法对各像素点的拉曼光谱分别进行两两互相关运算，得到所述各像素点的拉曼光谱相关系数矩阵，将所述各像素点的拉曼光谱相关系数矩阵作为所述各像素点的相似度指标。
  5. 根据权利要求1至3任意一项所述的方法，其特征在于，所述机器分类模型是支持向量机神经网络模型，或者是基于贝叶斯的线性或非线性分类器。
  6. 一种病理切片图像智能分类装置，其特征在于，所述装置包括：
    处理模块，用于对预置的正常样本及癌症样本中的每一幅病理切片图像进行图像处理，得到所述正常样本和所述癌症样本的训练数据，所述训练数据包含相似度指标的均值集合、方差集合及信息熵集合；
    训练模块，用于基于所述正常样本和所述癌症样本的训练数据对预置的机器分类模型进行训练，得到训练后的机器分类模型；
    分类模块，用于将待分类病理切片图像输入所述训练后的机器分类模型，且将所述训练后的机器分类模型输出的类型作为所述待分类病理切片图像的类型。
  7. 根据权利要求6所述的装置，其特征在于，所述处理模块包括：读取模块、提取模块、计算模块、归类模块，所述读取模块、提取模块及所述计算模块用于对对所述正常样本及所述癌症样本中的每一幅病理切片图像进行处理；
    所述读取模块具体用于读取病理切片图像含结构信息的三维图像，所述三维图像由各像素点的光子数分布构成第三维度；
    所述提取模块具体用于提取所述三维图像中由第三维度数据集决定的各像素点的相似度指标；
    所述计算模块具体用于利用所述各像素点的相似度指标计算所述病理切片 图像的相似度指标的均值，方差及信息熵；
    所述归类模块用于将所述正常样本中所有病理切片图像的相似度指标的均值、方差及信息熵分别归类为所述正常样本的相似度指标的均值集合、方差集合及信息熵集合，以作为所述正常样本的训练数据，及将所述癌症样本中所有病理切片图像的相似度指标的均值、方差及信息熵分别归类为至少一种类别的相似度指标的均值集合、方差集合及信息熵集合，以作为所述癌症样本的训练数据。
  8. 根据权利要求7所述的装置，其特征在于，若所述三维图像为荧光寿命成像，则所述提取模块具体用于：
    利用采集到的各像素点对应的时间衰减曲线进行最小二乘拟合得到各像素点的荧光寿命，将所述各像素点的荧光寿命作为所述各像素点的相似度指标；
    或者，
    利用预置的相位映射算法计算所述各像素点的荧光寿命，将所述各像素点的荧光寿命作为所述各像素点的相似度指标。
  9. 根据权利要求7所述的装置，其特征在于，若所述三维图像为拉曼成像，则所述提取模块具体用于：
    利用预置的皮尔逊互相关算法对各像素点的拉曼光谱分别进行两两互相关运算，得到所述各像素点的拉曼光谱相关系数矩阵，将所述各像素点的拉曼光谱相关系数矩阵作为所述各像素点的相似度指标。
  10. 根据权利要求6至9任意一项所述的装置，其特征在于，所述机器分类模型是支持向量机神经网络模型，或者是基于贝叶斯的线性或非线性分类器。
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