US 2021/0200988 A1 · METHOD AND EQUIPMENT FOR CLASSIFYING HEPATOCELLULAR CARCINOMA IMAGES BY COMBINING COMPUTER VISION FEATURES AND RADIOMICS FEATURES

Abstract

The present disclosure discloses a method and equipment for classifying hepatocellular carcinoma images by combining computer vision features and radiomics features, wherein the method comprising: 1) collecting eligible clinical images of patients and preprocessing the collected images; 2) extracting computer vision features from a segmented image of a hepatic tumor region; 3) extracting the manual radiomics features from the segmented image of the hepatic tumor region; 4) by combining the computer vision features and the radiomics features, screening by univariate filtering and then by LASSO regression; 5) using the features resulted from screening and clinical features together for modeling by a multivariable logistic regression model, and using the Akaike information criterion (AIC) to search backward and select clinical features suitable for the best model, so as to implement the prediction of hepatocellular carcinoma pathological grading.

A method for classifying hepatocellular carcinoma images by combining computer vision features and radiomics features, comprising:

In Step (1), a hepatocellular carcinoma image is obtained and preprocessed, wherein the preprocessing comprises:
segmenting a hepatic tumor region in the hepatocellular carcinoma image, re-sampling volume pixels of a segmented image of the hepatic tumor region into a space of a fixed size, normalizing a grey scale thereof, and labeling a corresponding pathological grading result;
In Step (2), computer vision features are extracted from the image of the hepatic tumor region segmented in Step (1), wherein the computer vision features comprise LoG filter features, LBP features, HOG features, and haar-like features;
In Step (3), radiomics features are extracted from the image of the hepatic tumor region segmented in Step (1), wherein the radiomics features comprise morphological features, grey scale features, texture features, and wavelet features;
In Step (4), the computer vision features extracted in Step (2) and the radiomics features extracted in Step (3) are jointed together, and screening is performed on features in a merged feature set X={X1, X2, . . . , Xn} resulted from the joint, wherein n represents a number of feature vectors in the set, Xi represents a ith feature vector in the merged feature set (Xi={xi1, xi2, . . . , xim}), xi represents an ith element in Xi, and m represents a number of elements in Xi;
wherein the screen comprises:
In Step (4.1), firstly, single-valued features, features having variances less than a preset threshold, features irrelevant to pathological grading, and redundant features are excluded by univariate filtering;
wherein the relationship intensity between features and pathological grading is measured through mutual information calculations, so that the features having mutual information values lower than the preset threshold are referred to as irrelevant to the pathological grading, and the mentioned calculation formula for mutual information is:
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wherein, Xi represents an ith feature vector in the merged feature set, Y represents a label, p(x, y) is a joint probability density function of X and Y, and p(x) and p(y) are, respectively, marginal probability density functions of X and Y;
The redundant features are those having correlation coefficients greater than 0.9, and the calculation formula for a correlation coefficient is:
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wherein, ρ is a calculated result of the correlation coefficient, xik represents a kth element in the ith feature vector in the merged feature set, Xjk represents a kth element in jth feature vector in the merged feature set, and xi and xj respectively represent mean values of all elements in an ith feature vector and jth feature vector in the merged feature set;
In Step (4.2), the feature dimension reduction of a Lasso regression model is adopted to train those unhelpful feature parameters to be 0 in order to obtain a sparse solution;
and a cost function of the mentioned Lasso regression is:
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wherein, m is a number of samples, n is a number of features, x(i) represents all feature values of the ith sample, Y represents a label value of the ith sample, w is a parameter vector of the length n, ∥w1∥ represents an L1 norm of the parameter vector w, b represents a constant coefficient, and λ is a parameter for controlling the regression complexity, wherein a best value of λ is selected by taking the binomial deviation as a criterion and then iterated by a 10-fold cross validation method to apply a λ value of the least binomial deviation in the Lasso regression model;
and a predict score is obtained by multiplying the LASSO-selected features by their respective coefficients and then summing up the products;
In Step (5), after clinical features are obtained from the hepatic tumor region image and all the clinical features are deleted one by one, a multivariable logistic regression is adopted, by combining the image with all feature matrices resulted from screening in Step (4), to get a classifier for pathological grading of hepatocellular carcinoma for different combinations;
the Akaike information criterion (AIC) is used as a criterion to search backward a most proper combination thereof, and then the influence of binomial deviation and the number of variables during selection are put into the comprehensive consideration so as to select a model of the lowest AIC score as the final classifier for pathological grading of hepatocellular carcinoma which may input the best combined feature matrix;
and the AIC is defined as:
AIC=−2 ln(L)+2k
wherein L is the maximum likelihood under this model, and k is a number of model variables;
In Step (6), a hepatocellular carcinoma image is acquired to go through the pathological grading of hepatocellular carcinoma and is preprocessed according to Step (1) to segment an image of a hepatic tumor region from which features of the same type as that of the best combined feature matrix said in Step (5) are extracted as a feature matrix to be graded;
and the feature matrix to be graded is input into the classifier for pathological grading of hepatocellular carcinoma obtained in Step (5) to output a pathological grading result of hepatocellular carcinoma.

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Method and equipment for classifying hepatocellular carcinoma images by combining computer vision features and radiomics features

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Method and equipment for classifying hepatocellular carcinoma images by combining computer vision features and radiomics features

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Abstract

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