CN112819765A - Liver image processing method - Google Patents
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- 210000004185 liver Anatomy 0.000 title claims abstract description 30
- 238000003672 processing method Methods 0.000 title claims abstract description 15
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- 206010019663 Hepatic failure Diseases 0.000 claims description 11
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- 230000002980 postoperative effect Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000000717 retained effect Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 210000002966 serum Anatomy 0.000 description 2
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Abstract
The invention relates to the technical field of image processing, in particular to a liver image processing method, which comprises the following steps: firstly, extracting the characteristic of the image group from the CECT image of the liver part through A.K. software; setting a threshold, and removing features lower than the threshold by using a VarianceThreshold method; thirdly, performing feature dimension reduction by using a T-SNE dimension reduction algorithm; fourthly, training and testing the training set samples by utilizing a QN-S3VM semi-supervised classification algorithm; and fifthly, randomly distributing a test set and a training set for multiple times, and taking the average value of the classification accuracy of the test set and the training set. The invention converts the liver image into quantitative and high-dimensional data characteristics, and can assist doctors in liver reserve function evaluation.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a liver image processing method.
Background
Hepatocellular carcinoma (liver cancer) is one of the most common malignancies worldwide. Hepatectomy is the main means by which liver cancer patients acquire long-term survival. Liver failure is an important complication after hepatectomy, especially hemihepatectomy. Therefore, it is very important to evaluate the liver before operation to avoid the occurrence of postoperative hepatic failure as much as possible, and the accurate evaluation of the hepatic reserve function of the liver cancer patient before operation is not only beneficial to avoiding the hepatic failure and reducing the risk of operation, but also beneficial to reducing death caused by postoperative middle-and long-term hepatic insufficiency. Currently, clinical evaluation means of liver reserve function mainly include liver serum biochemical detection, Child-Pugh score, albumin-bilirubin (ALBI) score, liver function quantitative test, liver volume measurement and the like. The biochemical detection of serum cannot completely reflect liver functions, and the Child-Pugh score cannot completely reflect the prognosis of liver cancer patients. In view of the above, there is still a need to find an effective means for predicting whether or not liver failure will occur after surgery.
The image omics is a new subject direction based on quantitative imaging, and converts medical image data into quantitative and high-dimensional data characteristics through high-flux extraction of quantitative characteristics. The morphological and pathological characteristics of the tumor in the clinical image are described intuitively and quantitatively by utilizing a plurality of image characteristics, and the method has stronger prediction effect on treatment results, prognosis and tumor genetic factors. Research has shown that the characteristics of the image group are related to the pathological characteristics after the liver fibrosis and the liver cancer operation, and the non-tumor liver parenchyma of the CECT image before the operation is presumed to reflect the liver reserve function of the patient.
At present, most of evaluation means of liver reserve function depend on preoperative clinical physiological indexes, and the prognosis condition of liver cancer patients is difficult to be completely reflected. The single dependence on clinical physiological indexes and the high data accuracy of images are difficult to guarantee, so that the interpretation of semantic information is not comprehensive, and the health condition of a testee cannot be accurately judged usually. In real life, it is a very time-consuming and impractical task for a physician to collect a large amount of patient data and provide the correct label. In order to solve the above problem, a liver image processing method is required in order to realize a classification mechanism that does not require the use of a large number of correct labels.
Disclosure of Invention
It is an object of the present invention to provide a liver image processing method which overcomes some or some of the disadvantages of the prior art.
The liver image processing method comprises the following steps:
firstly, extracting the characteristic of the image group from the CECT image of the liver part through A.K. software;
setting a threshold, and removing features lower than the threshold by using a VarianceThreshold method;
thirdly, performing feature dimension reduction by using a T-SNE dimension reduction algorithm;
fourthly, training and testing the training set samples by utilizing a QN-S3VM semi-supervised classification algorithm;
and fifthly, randomly distributing a test set and a training set for multiple times, and taking the average value of the classification accuracy of the test set and the training set.
Preferably, in the second step, a threshold is set first, then the variance of the sample feature is calculated, and if the variance of the feature is greater than the threshold, the feature is retained; if the variance of the feature is less than the threshold, the feature is deleted.
Preferably, the T-SNE dimension reduction algorithm maps the point pairs in the high-dimensional space to the low-dimensional space while keeping the distribution probability of the point pairs unchanged, converts the distance into the probability distribution by using Gaussian distribution in the high-dimensional space, converts the distance into the probability distribution by using T distribution in the low-dimensional space, represents the similarity of the point pairs by adopting joint probability, and obtains the sample distribution in the low-dimensional space by optimizing the KL divergence of the distance between the two distributions.
Preferably, the specific process of the T-SNE dimension reduction algorithm is as follows:
1) and setting the high-dimensional data point as X ═ X (X)1,x2,...,xn) The low-dimensional mapping point Y is (Y)1,y2,...,yn)；
2) The KL divergence is:
in the formula pijJoint probability function, q, for high dimensional spatial sample distributionijIs the joint probability of the sample distribution in the low dimensional space;
3) gaussian data point xi，xjThe joint probability function of (a) is as follows:
wherein σ is centered at xiThe optimal sigma is obtained by performing binary search on the Gaussian variance of the vector through a preset complexity factor;
4) in a low-dimensional space, data point yi，yjThe joint probability function of (a) is:
5) and finally, continuously updating and iterating by a gradient descent method to obtain a low-dimensional mapping sample.
Preferably, the flow of the QN-S3VM semi-supervised classification algorithm is as follows:
a. all labeled data, samples with hepatic failure labeled 1, samples without hepatic failure labeled-1, were scored as 2: 1, randomly distributing a training set and a testing set in proportion;
b. set the labeled sample of the training as Sl＝{(x1,y1′),...,(xm,ym') } unlabeled sample Su＝{(xm+1,ym+1′),...,(xn,yn') }, the number of marked samples is m, the number of unmarked samples is n-m, and the number of training lumped samples is n;
c. setting an initialization parameter lambda as a minimum iteration error, and setting S as a maximum iteration number;
d. recording the iteration times as s, iterating the training set sample in the step 1, calculating an error lambda' after each iteration, and adding 1 to the value of s after each iteration is finished;
e. until λ > ═ λ' or S = S, at which point the model training ends;
f. and placing the test set into a model for testing to obtain a prediction result.
The invention provides a liver image processing method based on a T-SNE characteristic reduction and QN-S3VM semi-supervised classification algorithm, which comprises the steps of firstly extracting CECT image texture characteristics of a liver part by utilizing A.K. software, then adding clinical physiological indexes, screening out characteristics with small variance through threshold value screening, mapping high-dimensional characteristics to a low-dimensional space by utilizing a dimension reduction algorithm T-SNE, reducing characteristic dimension, filtering out redundant characteristic information, finally randomly dividing a sample subjected to an operation into a training set and a test set through a semi-supervised learning idea, training the training set sample and a large number of patients subjected to preoperative examination, obtaining a final model, and predicting the accuracy of the test set sample.
The image omics is a new subject direction based on quantitative imaging, and converts medical image data into quantitative and high-dimensional data characteristics through high-flux extraction of quantitative characteristics. The liver function evaluation method is combined with clinical physiological indexes to be used for liver reserve function evaluation, multi-mode information complementation can be realized, deeper semantic information is mined, and the purpose of more accurately judging the condition of a testee is achieved.
In addition, the traditional supervised algorithm usually needs a large number of samples for training, and since medical data is precious, the acquisition of a large number of sample data is unrealistic.
Drawings
Fig. 1 is a flowchart of a liver image processing method according to embodiment 1;
FIG. 2 is a flowchart of feature selection in embodiment 1;
FIG. 3 is a flowchart of the QN-S3VM semi-supervised classification algorithm in example 1.
Detailed Description
For a further understanding of the invention, reference should be made to the following detailed description taken in conjunction with the accompanying drawings and examples. It is to be understood that the examples are illustrative of the invention and not limiting.
Example 1
As shown in fig. 1, the present embodiment provides a liver image processing method, which includes the following steps:
firstly, extracting the characteristic of the image group from the CECT image of the liver part through A.K. software;
setting a threshold, and removing features lower than the threshold by using a VarianceThreshold method;
thirdly, performing feature dimension reduction by using a T-SNE dimension reduction algorithm;
fourthly, training and testing the training set samples by utilizing a QN-S3VM semi-supervised classification algorithm;
and fifthly, randomly distributing a test set and a training set for multiple times, and taking the average value of the classification accuracy of the test set and the training set.
In fig. 1, the part enclosed by the left dotted line is mainly subjected to feature extraction and combination of the image omics features and the physiological features through a.k. software, and the part enclosed by the right dotted line is mainly: data preprocessing is carried out firstly, feature selection, data normalization and regularization are carried out according to a threshold value method, then data dimension reduction is carried out by using a T-SNE algorithm, and finally semi-supervised classification is carried out by using QN-S3 VM.
As shown in fig. 2, in the feature selection, a threshold is first set, then the variance of the sample feature is calculated, and if the variance of the feature is greater than the threshold, the feature is retained; if the variance of the feature is less than the threshold, the feature is deleted.
In this embodiment, the T-SNE dimension reduction algorithm maps the point pairs in the high-dimensional space to the low-dimensional space while keeping the distribution probability of each point pair unchanged, converts the distance into the probability distribution in the high-dimensional space using gaussian distribution, converts the distance into the probability distribution in the low-dimensional space using T distribution, represents the similarity of the point pairs using joint probability, and obtains the sample distribution in the low-dimensional space by optimizing the KL divergence of the distance between the two distributions.
In this embodiment, the T-SNE dimension reduction algorithm converts the distance into a probability distribution using Gaussian distribution in a high-dimensional space. Under a low-dimensional space, the similarity is expressed by using t distribution, so that points with low similarity are more distant, and points with high similarity are more compact, and the classification operation is favorably carried out later. The specific flow of the T-SNE dimension reduction algorithm is as follows:
1) and setting the high-dimensional data point as X ═ X (X)1,x2,...,xn) The low-dimensional mapping point Y is (Y)1,y2,...,yn)；
2) The KL divergence is:
in the formula pijJoint probability function, q, for high dimensional spatial sample distributionijIs the joint probability of the sample distribution in the low dimensional space;
3) gaussian data point xi，xjThe joint probability function of (a) is as follows:
wherein σ is centered at xiThe optimal sigma is obtained by performing binary search on the Gaussian variance of the vector through a preset complexity factor; k is in the interval [1, n ]]Taking any value;
4) in a low-dimensional space, data point yi，yjThe joint probability function of (a) is:
5) and finally, continuously updating and iterating by a gradient descent method to obtain a low-dimensional mapping sample.
In this embodiment, as shown in fig. 3, the flow of the QN-S3VM semi-supervised classification algorithm is as follows:
a. all labeled data, samples with hepatic failure labeled 1, samples without hepatic failure labeled-1, were scored as 2: 1, randomly distributing a training set and a testing set in proportion;
b. set the labeled sample of the training as Sl＝{(x1,y1′),...,(xm,ym') } unlabeled sample Su＝{(xm+1,ym+1′),...,(xn,yn') }, the number of marked samples is m, the number of unmarked samples is n-m, and the number of training lumped samples is n;
c. setting an initialization parameter lambda as a minimum iteration error, and setting S as a maximum iteration number;
d. recording the iteration times as s, iterating the training set sample in the step 1, calculating an error lambda' after each iteration, and adding 1 to the value of s after each iteration is finished;
e. until λ > ═ λ' or S = S, at which point the model training ends;
f. and placing the test set into a model for testing to obtain a prediction result.
Firstly, feature extraction is carried out on a liver part CECT image through A.K. software, then a threshold value is adjusted, features are screened through a VarianceThreshold method, and features lower than the threshold value are eliminated; converting the similarity between the data into probability by using a T-SNE dimension reduction algorithm, converting high-dimensional data into low-dimensional data by using a mapping relation, reducing the dimension of the data, and filtering out part of redundant information; the data to be tagged is represented by 2: 1, dividing the data into a training set and a test set, adding a large amount of label-free data into the training set, training by using a QN-S3VM algorithm, placing the test data into a trained model for testing to obtain a classification result, and performing multi-aspect evaluation on the model; and finally, randomly distributing a test set and a training set for many times, and taking the average value of the classification accuracy of the test set and the training set.
The T-SNE dimension reduction algorithm is a mature unsupervised dimension reduction algorithm, and maps a point pair of a high-dimensional space to a low-dimensional space according to the idea of converting distance into probability so as to reduce characteristic dimensions. When the semi-supervised model is trained, the initial minimum iteration error and the maximum iteration times are set. And when any one of the conditions is met in the iteration process, the model training is finished, and the model training can be used for predicting the test sample.
The present invention and its embodiments have been described above schematically, without limitation, and what is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. Therefore, if the person skilled in the art receives the teaching, without departing from the spirit of the invention, the person skilled in the art shall not inventively design the similar structural modes and embodiments to the technical solution, but shall fall within the scope of the invention.
Claims (5)
1. A liver image processing method characterized by: the method comprises the following steps:
firstly, extracting the characteristic of the image group from the CECT image of the liver part through A.K. software;
setting a threshold, and removing features lower than the threshold by using a VarianceThreshold method;
thirdly, performing feature dimension reduction by using a T-SNE dimension reduction algorithm;
fourthly, training and testing the training set samples by utilizing a QN-S3VM semi-supervised classification algorithm;
and fifthly, randomly distributing a test set and a training set for multiple times, and taking the average value of the classification accuracy of the test set and the training set.
2. A liver image processing method according to claim 1, characterized in that: in the second step, firstly setting a threshold, then calculating the variance of the sample characteristics, and if the variance of the characteristics is greater than the threshold, keeping the characteristics; if the variance of the feature is less than the threshold, the feature is deleted.
3. A liver image processing method according to claim 1, characterized in that: the T-SNE dimension reduction algorithm is characterized in that point pairs of a high-dimensional space are mapped to a low-dimensional space, the distribution probability of the point pairs is kept unchanged, the distance is converted into probability distribution in the high-dimensional space by using Gaussian distribution, the distance is converted into probability distribution in the low-dimensional space by using T distribution, the similarity of corresponding points is represented by adopting joint probability, and the sample distribution of the low-dimensional space is obtained by optimizing the KL divergence of the distance between the two distributions.
4. A liver image processing method according to claim 3, characterized in that: the specific flow of the T-SNE dimension reduction algorithm is as follows:
1) and setting the high-dimensional data point as X ═ X (X)1,x2,...,xn) The low-dimensional mapping point Y is (Y)1,y2,...,yn)；
2) The KL divergence is:
in the formula pijJoint probability function, q, for high dimensional spatial sample distributionijIs the joint probability of the sample distribution in the low dimensional space;
3) gaussian data point xi，xjThe joint probability function of (a) is as follows:
wherein σ is centered at xiThe optimal sigma is obtained by performing binary search on the Gaussian variance of the vector through a preset complexity factor;
4) in a low-dimensional space, data point yi，yjThe joint probability function of (a) is:
5) and finally, continuously updating and iterating by a gradient descent method to obtain a low-dimensional mapping sample.
5. A liver image processing method according to claim 1, characterized in that: the QN-S3VM semi-supervised classification algorithm comprises the following steps:
a. all labeled data, samples with hepatic failure labeled 1, samples without hepatic failure labeled-1, were scored as 2: 1, randomly distributing a training set and a testing set in proportion;
b. set the labeled sample of the training as Sl＝{(x1,y1′),...,(xm,ym') } unlabeled sample Su＝{(xm+1,ym+1′),...,(xn,yn') }, the number of marked samples is m, the number of unmarked samples is n-m, and the number of training lumped samples is n;
c. setting an initialization parameter lambda as a minimum iteration error, and setting S as a maximum iteration number;
d. recording the iteration times as s, iterating the training set sample in the step 1, calculating an error lambda' after each iteration, and adding 1 to the value of s after each iteration is finished;
e. until λ > ═ λ' or S = S, at which point the model training ends;
f. and placing the test set into a model for testing to obtain a prediction result.
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| CN114694831A (en) * | 2022-04-01 | 2022-07-01 | 南方医科大学南方医院 | Risk level determination method and device, computer equipment and storage medium |
| CN115132352A (en) * | 2022-07-04 | 2022-09-30 | 西南交通大学 | Method for predicting curative effect of HIFU ablation hysteromyoma based on machine learning algorithm |
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| CN114694831A (en) * | 2022-04-01 | 2022-07-01 | 南方医科大学南方医院 | Risk level determination method and device, computer equipment and storage medium |
| CN115132352A (en) * | 2022-07-04 | 2022-09-30 | 西南交通大学 | Method for predicting curative effect of HIFU ablation hysteromyoma based on machine learning algorithm |
| CN117011242A (en) * | 2023-07-10 | 2023-11-07 | 珠海市人民医院 | Method and system for predicting hepatic encephalopathy after internal portal bypass operation through jugular vein |
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