CN114596285A - Multitask medical image enhancement method based on generation countermeasure network - Google Patents
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Abstract
The invention discloses a multitask medical image enhancement method based on a generation countermeasure network, which is used for realizing super-resolution and fusion tasks of medical images and comprises the following steps: step 1, constructing a training data set and preprocessing the data set; step 2, forming a generation countermeasure network model by using a dynamic convolution and a residual dense network; step 3, setting the hyper-parameters and the loss functions of the network model, and optimizing the loss functions; step 4, loseInputting the preprocessed training data set to a generated confrontation network model, and training a network to obtain a trained generated confrontation network model; step 5, inputting a test data set into a network to obtain a fused super-resolution medical image; step 6, using mutual information QmiAnd Q designed based on structural similarityyangAnd evaluating the quality of the fused super-resolution medical image. The method of the invention can realize the super-resolution and fusion tasks of the medical images in a network.
Description
Technical Field
The invention relates to the technical field of computer vision and image processing, in particular to a multitask medical image enhancement method based on a generation countermeasure network.
Background
With the continuous development of medical imaging technology, various medical images of different modalities are widely applied to the fields of clinical disease diagnosis, auxiliary surgery, health examination and detection, and the like. Common medical images include Magnetic Resonance Images (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT). Different modality medical images respond with different body structure information due to different imaging modes. The single modality image cannot fully describe the focus information, which is not beneficial to the diagnosis of doctors. In order to solve the contradiction between the clinical requirement and the imaging technology, the medical image fusion technology generates a fusion image containing multi-modal complementary information by inheriting medical image information of different modalities.
Since medical images are imaged with radiation, the higher the radiation level, the sharper the image obtained. In order to ensure that the patient receives a smaller radiation dose, the resolution of most medical source images is limited, resulting in a lower resolution of the multi-modal medical fusion image. In order to improve the resolution of the fused image, an image super-resolution method is generally adopted in advance, and then a high-resolution multi-modal medical fused image is obtained by various fusion methods. This results in multiple steps and multiple network models required to obtain a high resolution multi-modality medical fusion image, which greatly increases labor costs.
Disclosure of Invention
In order to solve the above problems, the present invention provides a multitask medical image enhancement method based on generation of a countermeasure network.
In order to achieve the purpose, the invention is realized by the following technical scheme:
the invention relates to a multitask medical image enhancement method based on a generation countermeasure network, which comprises the following steps:
step 1, constructing a training data set, and carrying out size unification and data augmentation operation on the training data set;
step 2, forming a generation countermeasure network model by using a dynamic convolution and a residual dense network;
step 3, setting the hyper-parameters and the loss functions of the network model, and optimizing the loss functions;
step 4, inputting the preprocessed training data set to a generated confrontation network model, and training a network to obtain a trained generated confrontation network model;
step 5, inputting a test data set into a network to obtain a fused super-resolution medical image;
step 6, using mutual information QmiAnd Q designed based on structural similarityyangAnd evaluating the quality of the fused super-resolution medical image.
The invention is further improved in that: the generation countermeasure network formed in step 2 includes a generation model G and two discriminant modelsAndthe generation model comprises two encoders and three decoders, the encoders comprise dynamic convolution layers, a dense residual error network and an up-sampling module, the decoders and the discrimination model are composed of different convolution layers, the dynamic convolution layers perform global pooling on input features and input a full connection layer and ReLU activation, finally, the weights of all convolution kernels are obtained through Softmax layer activation, the weights and all convolution kernels are subjected to linear weighting to obtain final convolution kernels, and the dense residual error network comprises five convolution layer link activation layers.
The invention is further improved in that: the hyper-parameters in the step 3 comprise batch size, initial learning rate, iteration times and a learning rate attenuation strategy.
The invention is further improved in that: the loss function in the step 3 comprises a super-resolution loss function and a fusion loss function, wherein the super-resolution loss function is expressed by the following mathematical expression:
in the formula (1), α represents a ratio between two types of loss, N represents all pixels of an image, and IsrRepresentation of super-resolution image, IhrRepresenting a high resolution image, psi (g) representing features obtained by inputting the image into a pre-trained VGG16 network;
the fusion loss function is composed of the countermeasure loss and the content loss, and specifically comprises the following steps:
the 1 st and 2 nd terms of the formula (2) represent the mode m1And mode m2Loss function of { lambda }1,λ2Beta, gamma is a balance parameter,andto use the content loss function according to different modality images,andrespectively representing generator G and discriminatorAndthe loss of antagonism between, defined as follows:
the invention is further improved in that: for PET images, the content loss function is expressed asThe formula is as follows:
wherein IPETAs a PET image, ImIs another modality image.
The invention is further improved in that: for MRI images or CT images, the content loss function is expressed asThe Laplace characteristic of the source image is constrained, and the mathematical formula is as follows:
wherein Laplace (·) denotes Laplace transform, IMRI/CTRepresenting an MRI or CT image.
The invention is further improved in that: the discriminatorAndthe antagonism loss of (1) adopts the loss function of a WGAN-GP discriminator, and the formula is as follows:
The invention is further improved in that: the specific operation of the step 4 is as follows:
step 4.1, inputting the original data training set into the forward propagation of the encoder and the super-resolution decoder, and training the two branches by using a super-resolution loss function;
and 4.2, extracting and assigning the encoder parameters trained in the step 4.1 to an encoder of the fusion network, freezing the parameters of the encoder, and training the network model by using the fusion loss function to obtain the trained model.
The invention has the beneficial effects that:
1. the invention adopts the generation of the confrontation network model, uses the dynamic convolution and the dense residual error network to furthest ensure the information transmission, reduce the problems of gradient dispersion or gradient explosion and the like, links the shallow layer characteristic with the deep layer characteristic and ensures the network performance.
2. The invention also optimizes the network performance by using the technologies of parameter extraction, parameter freezing, parameter fine adjustment and the like, thereby obtaining a better super-resolution multi-modal medical fusion image.
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FIG. 1 is a flow chart of the present invention.
Fig. 2 is an overall network model.
Fig. 3 is a schematic diagram of an encoder in a network.
Fig. 4 is a schematic diagram of a decoder in a network.
FIG. 5 is a schematic diagram of the fusion results.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It is obvious that the described embodiments are only a part of the implementations of the present invention, and not all implementations, and all other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention are within the protection scope of the present invention without any inventive work.
As shown in fig. 1-5, the present invention is a multitask medical image enhancement method based on generation of countermeasure network, comprising the following steps:
step 1, constructing a training data set, and processing the training data set as follows:
selecting training samples from the training data set as an original training set, wherein the sizes of images in the training samples are all 256 multiplied by 256, and performing image enhancement operation on the images, wherein the adopted image enhancement operation comprises the following steps: turning over the upper part and the lower part, rotating by 90 degrees, rotating by 180 degrees, rotating by 270 degrees, rotating by 90 degrees after turning over the upper part and the lower part, rotating by 180 degrees after turning over the upper part and the lower part, and rotating by 270 degrees after turning over the upper part and the lower part;
step 2, forming a generation countermeasure network model by using a dynamic convolution and a residual dense network;
the generation countermeasure network comprises a generation model G and two discriminant modelsAndwherein the generative model comprises two encoding modules and three decoding modules; the coding module comprises a dynamic convolution layer, a dense residual error network and an up-sampling module; the decoding module and the discriminant model both include different convolutional layers. The dynamic convolution changes an original fixed convolution kernel into a convolution kernel which can adaptively change attention according to input; and globally pooling input features, inputting the input features into a full connection layer and a ReLU (return link) layer for activation, finally obtaining the weight of each convolution kernel through Softmax layer activation, and linearly weighting the weight and each convolution kernel to obtain the final convolution kernel.
The dense residual error network comprises five convolutional layer link activation layers, and all the layers are connected on the premise of ensuring the maximum information transmission between the layers in the network, namely the input of each layer is the output of all the previous layers. On the basis, in order to reduce the problem of gradient dispersion or gradient explosion, residual error linkage is introduced, and shallow features and deep features are linked for ensuring the network performance.
Step 3, setting the batch size, the initial learning rate, the iteration times, the learning rate attenuation strategy and the loss function of the network model, and optimizing the loss function, wherein the loss function comprises a super-resolution loss function and a fusion loss function;
step 4, generating a confrontation network model by training, adopting an AdamW optimizer to train, and setting the initial learning rate to be 2 multiplied by 10-4The network counts 200 epochs, and the specific training steps are as follows:
step 4.1, inputting the original data training set into forward propagation of an Encoder Encoder and a super-resolution decoder DecoderSR, and training two branches by using a super-resolution loss function;
the super-resolution loss function formula is as follows:
in the formula (1), α represents a ratio between two types of loss, N represents all pixels of an image, and IsrRepresentation of super-resolution image, IhrRepresenting a high resolution image, psi (g) representing features obtained by inputting the image into a pre-trained VGG16 network;
and 4.2, extracting and assigning the encoder parameters trained in the step 4.1 to an encoder of the fusion network, freezing the parameters of the encoder, and training the network model by using the fusion loss function to obtain the trained model.
The fusion loss function is composed of the countermeasure loss and the content loss, and specifically comprises the following steps:
the 1 st and 2 nd terms of the formula (2) represent the mode m1And mode m2Loss function of { lambda }1,λ2Beta, gamma is a balance parameter,andrespectively representing generator G and discriminatorAndthe loss of antagonism between, defined as follows:
andin order to adopt content loss functions according to different modality images, the images are divided into PET images, MRI images and CT images, aiming at the PET images, in order to keep the metabolic information of the PET images, a mean square error loss function is adopted, and the expression is as follows:
wherein IPETAs a PET image, ImIs another modality image.
For an MRI image or a CT image, in order to maintain texture information and contours in a source image, the Laplace characteristic of the source image is constrained, and the mathematical formula is as follows:
wherein Laplace (·) denotes Laplace transform, IMRI/CTRepresenting an MRI or CT image.
Andthe antagonistic loss of (2) is defined by the loss function of the WGAN-GP discriminator as follows:
Step 5, inputting a test data set into the network obtained by training in the step 4.2 to obtain a fused super-resolution medical image;
step 6, using mutual information QmiAnd Q designed based on structural similarityyangAnd evaluating the quality of the fused super-resolution medical image.
The data set used in the experiment is obtained by downloading http:// www.med.harvard.edu/AANLIB/home. html of a website, and in the test result, only the network model obtained by training in the step 4.2 is used as a test model. The experimental verification shows that the three pictures in the first row in the figure are respectively the T1 and T2 modalities of MRI images and the FDG modality of PET images, as shown in fig. 5. The two images in the second row are the fusion results of the PET image with the MRI images of the two different modalities. Q of the fusion resultmiAnd QyangThe values of (A) are shown in Table 1:
TABLE 1 mutual information Q of fusion resultsmiValue and QyangValue of
Through the verification of the figure 5 and the table 1, the model of the invention can better recover the details of the image edge and the texture, more completely retain the semantic information and the edge texture of the two source images, and can obtain a better super-resolution multi-modal fusion image.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A multitask medical image enhancement method based on a generation countermeasure network is characterized by comprising the following steps: the method comprises the following steps:
step 1, constructing a training data set, and carrying out size unification and data augmentation operation on the training data set;
step 2, forming a generation countermeasure network model by using a dynamic convolution and a residual dense network;
step 3, setting the hyper-parameters and the loss functions of the network model, and optimizing the loss functions;
step 4, inputting the preprocessed training data set to a generated confrontation network model, and training a network to obtain a trained generated confrontation network model;
step 5, inputting a test data set into a network to obtain a fused super-resolution medical image;
step 6, using mutual information QmiAnd Q designed based on structural similarityyangEvaluating the quality of the fused super-resolution medical image;
the generation countermeasure network formed in step 2 includes a generation model G and two discriminant modelsAndthe generation model comprises two encoders and three decoders, the encoders comprise dynamic convolution layers, a dense residual error network and an up-sampling module, the dynamic convolution layers perform global pooling on input features and input a full connection layer and ReLU activation, finally the weights of all convolution kernels are obtained through Softmax layer activation, the weights and all convolution kernels are subjected to linear weighting to obtain final convolution kernels, and the dense residual error network comprises five convolution layer link activation layers.
2. The multitask medical image enhancement method based on the generation countermeasure network as claimed in claim 1, wherein: the hyper-parameters in the step 3 comprise batch size, initial learning rate, iteration times and a learning rate attenuation strategy.
3. The multitask medical image enhancement method based on the generation countermeasure network as claimed in claim 1, wherein: the loss function in the step 3 comprises a super-resolution loss function and a fusion loss function, wherein the super-resolution loss function is expressed by the following mathematical expression:
in the formula (1), α represents a ratio between two types of loss, N represents all pixels of an image, and IsrSuper-resolution image, I, representing network outputhrRepresenting the original high resolution reference image, psi (g) representing the features obtained by inputting the image into a pre-trained VGG16 network;
the fusion loss function is composed of the countermeasure loss and the content loss, and specifically comprises the following steps:
the 1 st and 2 nd terms of the formula (2) represent the mode m1And mode m2Loss function of { lambda }1,λ2Beta, gamma is a balance parameter,andto use the content loss function according to different modality images,andrespectively representing generator G and discriminatorAndthe loss of antagonism between, defined as follows:
5. The multitask medical image enhancement method based on the generation countermeasure network according to claim 3, characterized in that: for MRI images or CT images, the content loss function is expressed asThe Laplace characteristic of the source image is constrained, and the expression is as follows:
wherein Laplace (·) denotes Laplace transform, IMRI/CTRepresenting an MRI or CT image.
6. The multitask medical image enhancement method based on the generation countermeasure network according to claim 3, characterized in that: the discriminatorAndthe antagonism loss of (1) adopts the loss function of a WGAN-GP discriminator, and the formula is as follows:
7. The multitask medical image enhancement method based on the generation countermeasure network according to claim 3, characterized in that: the specific operation of the step 4 is as follows:
step 4.1, inputting the original data training set into the forward propagation of the encoder and the super-resolution decoder, and training the two branches by using a super-resolution loss function;
and 4.2, extracting and assigning the encoder parameters trained in the step 4.1 to an encoder of the fusion network, freezing the parameters of the encoder, and training the network model by using the fusion loss function to obtain the trained model.
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CN111178499A (en) * | 2019-12-10 | 2020-05-19 | 西安交通大学 | Medical image super-resolution method based on generation countermeasure network improvement |
WO2021056969A1 (en) * | 2019-09-29 | 2021-04-01 | 中国科学院长春光学精密机械与物理研究所 | Super-resolution image reconstruction method and device |
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CN111178499A (en) * | 2019-12-10 | 2020-05-19 | 西安交通大学 | Medical image super-resolution method based on generation countermeasure network improvement |
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