CN117649408A - Lung nodule recognition processing method based on lung CT image - Google Patents
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Abstract
The invention discloses a lung nodule recognition processing method based on a lung CT image, which belongs to the field of computing image recognition and comprises the steps of obtaining a marked lung sample image; obtaining a non-nodule image dataset and a nodule image dataset; amplifying the nodule image data set to a second number through enhancement processing, and extracting a first number of non-nodule images; mixing the second number of nodule image data with the first number of non-nodule images and then randomly sampling; inputting training data into a neural network classification model to train and adjust model parameters until model judgment parameters are converged; inputting the lung image data to be analyzed into a classification model to classify lung nodules; labeling the classification result in the lung image to be analyzed and the 3D lung nodule model for the doctor to comprehensively judge according to vital sign parameters of the patient. The method improves the recognition efficiency and recognition accuracy of the lung nodule classification recognition model, and reduces the working intensity of doctors.
Description
Technical Field
The invention belongs to the field of computer image processing, and particularly relates to a lung nodule recognition processing method based on a lung CT image.
Background
Screening diagnosis of lung nodules has been found to play an important role in the early diagnosis of lung cancer. The lung computed tomography detection result can contain information of the morphology, size, position, gray scale and the like of the lung nodule, so that the application of CT in clinical imaging detection is wider and wider, and accurate detection and segmentation of the lung nodule area from hundreds of CT images are very challenging. The lung has a complex internal structure of a bronchial tree, which is formed by repeated branches of bronchi, and contains a plurality of blood vessels, the outline of which varies with the slice, and the shape of which is also different, so that the lung nodule is easily confused with small lung nodule, and the detection and segmentation of the lung nodule consumes much effort of doctors.
Therefore, how to optimize the computer-aided diagnosis system to identify lung nodules, and further assist doctors in performing rapid diagnosis is a technical problem to be solved.
Disclosure of Invention
Aiming at the problem of small segmentation and hypofunction of the lung nodule size. The invention provides a method for acquiring a marked lung sample image, reading a nodule coordinate, acquiring a 64 multiplied by 64 area image by taking the nodule coordinate as the center, and classifying an image with an image tag value of 0 or 1 into two types for input network training; obtaining a non-nodule image dataset and a nodule image dataset; amplifying the nodule image data set to a second number through enhancement processing, and extracting a first number of non-nodule images; mixing the second number of nodule image data with the first number of non-nodule images and then randomly sampling; using 90% of the sampled data as training data for training the classification network, and using 10% of the sampled data as test data for testing the performance of the classification network; inputting training data into a neural network classification model to train and adjust model parameters until model judgment parameters are converged; inputting the lung image data to be analyzed into a classification model to classify lung nodules; labeling the classification result in the lung image to be analyzed and the 3D lung nodule model for the doctor to comprehensively judge according to vital sign parameters of the patient. By enhancing the lung CT image, the number of samples is amplified, the proportion of the samples is optimized, the recognition efficiency and recognition accuracy of the lung nodule classification recognition model are improved, and the working intensity of doctors is reduced.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a lung nodule recognition processing method based on a lung CT image, the method comprising:
s1, acquiring a marked lung sample image, reading a nodule coordinate, acquiring a 64 multiplied by 64 area image by taking the nodule coordinate as a center, and classifying the image with the image tag value of 0 or 1 into two types of input network training; obtaining a non-nodule image dataset and a nodule image dataset;
s2, amplifying the nodule image data set to a second number through enhancement processing, and extracting a first number of non-nodule images; mixing the second number of nodule image data with the first number of non-nodule images and then randomly sampling; using 90% of the sampled data as training data for training the classification network, and using 10% of the sampled data as test data for testing the performance of the classification network;
s3, inputting training data into a neural network classification model to train and adjust model parameters until model judgment parameters are converged;
s4, inputting the lung image data to be analyzed into a classification model to classify lung nodules;
and S5, labeling the classification result in the lung image to be analyzed and the 3D lung nodule model so as to comprehensively judge by a doctor according to vital sign parameters of the patient.
Further, the enhancement processing includes translation, flipping, scaling, rotation processing.
Further, the noted lung sample image is a CT image.
Further, the neural network classification model adopts a U-net network model or a transducer model which takes ResNet-50 as a skeleton network.
Further, the classification model loss function is:
in the above expression, i represents a sample, y i The label representing sample i has a positive class label of 1 and a negative class label of 0; p is p i Representing the probability that sample i is predicted to be a positive class, 1-p i Representing the probability that sample i is predicted as a negative class, N represents the total number of samples, L i Indicating the loss value of the i-th sample.
Further, before S1, the method further includes: s0, labeling the lung CT sample image according to the image mask file, obtaining a nodule center coordinate, filtering image noise after obtaining the slice thickness, window width and window level, performing neighbor interpolation sampling on the data, and stacking the sampled data to generate a 3D lung nodule slice diagram.
Further, labeling the classification result in the lung image to be analyzed and the 3D lung nodule model includes: and (3) inputting the CT image of the lung to be analyzed and single subtype pathological sections into a PLS mapping model after encoding, mapping data in the classification model into the CT image, and performing pseudo-color processing on the regions of interest of different pathological subtypes.
A computer-readable storage medium storing a computer program, wherein execution of the computer program by a processor implements a lung nodule recognition processing method based on a lung CT image.
Terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement a lung nodule recognition processing method based on a lung CT image.
The beneficial effects of the invention are as follows:
the lung CT image is enhanced, the number of samples is amplified, the proportion of the samples is optimized, the recognition efficiency and the recognition accuracy of the lung nodule classification recognition model are improved, and the working intensity of doctors is reduced;
meanwhile, the ResNet-50 is adopted as a skeleton network for downsampling, so that a network layer can be deepened, and the segmentation accuracy of the network is improved. The method prevents the model from degradation and gradient disappearance, and has the advantages of high convergence rate, reduced model data volume and the like. The output characteristic diagram of each stage is connected with the decoder of the U-Net to keep the characteristics not easy to lose.
The foregoing description is only an overview of the present invention, and is intended to be more clearly understood as the present invention, as it is embodied in the following description, and is intended to be more clearly understood as the following description of the preferred embodiments, given in detail, of the present invention, along with other objects, features and advantages of the present invention.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures.
Fig. 1 is a flowchart of a lung nodule recognition processing method of a lung CT image.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In the description of the present invention, unless explicitly stated and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, connected, detachably connected, or integrated; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
Example 1
A lung nodule recognition processing method based on a lung CT image, the method comprising:
s1, acquiring a marked lung sample image, reading a nodule coordinate, acquiring a 64 multiplied by 64 area image by taking the nodule coordinate as a center, and classifying the image with the image tag value of 0 or 1 into two types of input network training; obtaining a non-nodule image dataset and a nodule image dataset;
s2, amplifying the nodule image data set to a second number through enhancement processing, and extracting a first number of non-nodule images; mixing the second number of nodule image data with the first number of non-nodule images and then randomly sampling; using 90% of the sampled data as training data for training the classification network, and using 10% of the sampled data as test data for testing the performance of the classification network;
s3, inputting training data into a neural network classification model to train and adjust model parameters until model judgment parameters are converged;
s4, inputting the lung image data to be analyzed into a classification model to classify lung nodules;
and S5, labeling the classification result in the lung image to be analyzed and the 3D lung nodule model so as to comprehensively judge by a doctor according to vital sign parameters of the patient.
Further, the enhancement processing includes translation, flipping, scaling, rotation processing.
Further, the noted lung sample image is a CT image.
Further, the neural network classification model adopts a U-net network model or a transducer model which takes ResNet-50 as a skeleton network.
Further, the classification model loss function is:
in the above expression, i represents a sample, y i The label representing sample i has a positive class label of 1 and a negative class label of 0; p is p i Representing the probability that sample i is predicted to be a positive class, 1-p i Representing the probability that sample i is predicted as a negative class, N represents the total number of samples, L i Indicating the loss value of the i-th sample.
Further, before S1, the method further includes: s0, labeling the lung CT sample image according to the image mask file, obtaining a nodule center coordinate, filtering image noise after obtaining the slice thickness, window width and window level, performing neighbor interpolation sampling on the data, and stacking the sampled data to generate a 3D lung nodule slice diagram.
Further, labeling the classification result in the lung image to be analyzed and the 3D lung nodule model includes: and (3) inputting the CT image of the lung to be analyzed and single subtype pathological sections into a PLS mapping model after encoding, mapping data in the classification model into the CT image, and performing pseudo-color processing on the regions of interest of different pathological subtypes.
A computer-readable storage medium storing a computer program, wherein execution of the computer program by a processor implements a lung nodule recognition processing method based on a lung CT image.
Terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement a lung nodule recognition processing method based on a lung CT image.
The beneficial effects of the invention are as follows:
1. the lung CT image is enhanced, the number of samples is amplified, the proportion of the samples is optimized, the recognition efficiency and the recognition accuracy of the lung nodule classification recognition model are improved, and the working intensity of doctors is reduced;
2. the ResNet-50 is adopted as a skeleton network for downsampling, so that a network layer can be deepened, and the segmentation accuracy of the network is improved. The method prevents the model from degradation and gradient disappearance, and has the advantages of high convergence rate, reduced model data volume and the like. The output characteristic diagram of each stage is connected with the decoder of the U-Net to keep the characteristics not easy to lose.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. A lung nodule recognition processing method based on a lung CT image, the method comprising:
s1, acquiring a marked lung sample image, reading a nodule coordinate, acquiring a 64 multiplied by 64 area image by taking the nodule coordinate as a center, and classifying the image with the image tag value of 0 or 1 into two types of input network training; obtaining a non-nodule image dataset and a nodule image dataset;
s2, amplifying the nodule image data set to a second number through enhancement processing, and extracting a first number of non-nodule images; mixing the second number of nodule image data with the first number of non-nodule images and then randomly sampling; using 90% of the sampled data as training data for training the classification network, and using 10% of the sampled data as test data for testing the performance of the classification network;
s3, inputting training data into a neural network classification model to train and adjust model parameters until model judgment parameters are converged;
s4, inputting the lung image data to be analyzed into a classification model to classify lung nodules;
and S5, labeling the classification result in the lung image to be analyzed and the 3D lung nodule model so as to comprehensively judge by a doctor according to vital sign parameters of the patient.
2. The lung nodule recognition processing method based on lung CT images according to claim 1, wherein: the enhancement process includes a translation, flipping, scaling, rotation process.
3. The lung nodule recognition processing method based on lung CT images according to claim 1, wherein: the noted lung sample image is a CT image.
4. The lung nodule recognition processing method based on lung CT images according to claim 1, wherein: the neural network classification model adopts a U-net network model or a transducer model which takes ResNet-50 as a skeleton network.
5. The lung nodule recognition processing method based on lung CT images of claim 4, wherein: the classification model loss function is:
in the above expression, i represents a sample, y i The label representing sample i has a positive class label of 1 and a negative class label of 0; p is p i Representing the probability that sample i is predicted to be a positive class, 1-p i Representing the probability that sample i is predicted as a negative class, N represents the total number of samples, L i Indicating the loss value of the i-th sample.
6. The lung nodule recognition processing method based on lung CT images according to claim 1, wherein: prior to S1, the method further comprises: s0, labeling the lung CT sample image according to the image mask file, obtaining a nodule center coordinate, filtering image noise after obtaining the slice thickness, window width and window level, performing neighbor interpolation sampling on the data, and stacking the sampled data to generate a 3D lung nodule slice diagram.
7. The lung nodule recognition processing method based on lung CT images according to claim 1, wherein: labeling the classification results in the lung image to be analyzed and in the 3D lung nodule model comprises: and (3) inputting the CT image of the lung to be analyzed and single subtype pathological sections into a PLS mapping model after encoding, mapping data in the classification model into the CT image, and performing pseudo-color processing on the regions of interest of different pathological subtypes.
8. A computer-readable storage medium storing a computer program, wherein execution of the computer program by a processor implements the lung nodule recognition processing method based on a lung CT image as claimed in any one of claims 1-7.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to carry out a lung nodule recognition processing method based on a lung CT image according to any of claims 1-7.
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