WO2022047625A1 - Procédé et système de traitement des images, et support d'enregistrement lisible par ordinateur - Google Patents

Procédé et système de traitement des images, et support d'enregistrement lisible par ordinateur Download PDF

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WO2022047625A1
WO2022047625A1 PCT/CN2020/112870 CN2020112870W WO2022047625A1 WO 2022047625 A1 WO2022047625 A1 WO 2022047625A1 CN 2020112870 W CN2020112870 W CN 2020112870W WO 2022047625 A1 WO2022047625 A1 WO 2022047625A1
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layer
network
discriminator
generator
image
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Chinese (zh)
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郑海荣
刘新
张娜
胡战利
薛恒志
梁栋
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深圳先进技术研究院
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • G06N3/04—Architecture, e.g. interconnection topology

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  • the present application relates to the technical field of image processing, and in particular, to an image processing method, system and storage medium for convolutional neural networks.
  • Magnetic resonance imaging (MRI) scanning is currently a non-invasive high-resolution imaging technique that plays an important role in current clinical diagnosis and scientific research, as it can reveal the three-dimensional, internal details and structures of human tissues and organs.
  • noise can easily affect the quality of the image, especially when high speed and high resolution are required, local blurred areas (such as white, shadow, distortion, etc.) will be generated on the image.
  • Noise in MRI not only reduces imaging quality but also reduces clinical diagnostic accuracy; it also negatively affects the reliability of subsequent analysis tasks such as registration, segmentation, and detection, such as the formation of several Slice (pixel block or pixel strip) as the sampling input of the processing model.
  • the slice partially or completely covers the blurred area, the sampling slice cannot be used as input data.
  • the data for the under-sampling part needs to be filled by the processing model according to other sampling data. or perfect. Therefore, effective noise reduction algorithms are necessary for further magnetic resonance analysis, and have important scientific significance and application prospects in the field of medical diagnosis.
  • 3D magnetic resonance image denoising technology based on multi-channel residual learning of convolutional neural network
  • a 10-layer convolutional neural network layer is designed, and the architecture of VGG network is adopted to reduce noise and residual learning strategy.
  • This deep learning method exhibits robust denoising performance on both the maximum peak signal-to-noise ratio and the global structural similarity index evaluation metrics.
  • noise suppression and image structure preservation it retains more details of the image and effectively removes the noise of 3D magnetic resonance images.
  • a convolutional neural network technique for magnetic resonance image denoising is designed under the framework of deep learning of a convolutional neural network, which uses a set of convolutions to separate image features from noise.
  • the network adopts an encoder-decoder structure, which not only preserves the salient features of the image, but also ignores unnecessary features.
  • the network is trained end-to-end using a residual learning scheme.
  • the performance of the proposed CNN is qualitatively and quantitatively tested on one simulated and four real magnetic resonance datasets. Extensive experimental results show that the network can effectively remove noise from MRI images without losing key image details.
  • the image denoised by the convolutional neural network is prone to lose the edge details of the image, and also has defects such as excessive network parameters, huge consumption of computing resources and slow processing speed.
  • the present application provides an image processing method, which adopts the following technical solutions:
  • An image processing method comprising:
  • a generator training step that extracts noisy image data from the training set as input images to train generator network parameters by reducing the cycle consistency loss function so that the output images of the generator network are the same as the noise-free images in the training set. The difference is reduced; the generator network includes an attention mechanism module for enhancing the contrast of edge details of the input image;
  • the discriminator training step the parameters of the discriminator network are trained by reducing the discriminator loss function by inputting the output image of the trained generator network and the noise-free image respectively to the discriminator network, so that the output of the discriminator network indicates the discriminator network. whether the input to the generator network is the output image of the trained generator network or an indication of the noise-free image;
  • the generator training step and the discriminator training step are repeated with different noise images to obtain the final generator network parameters and discriminator network parameters by minimizing the cycle consistency loss function and the discriminator loss function.
  • a multi-layer deep convolutional neural network adversarial network model is constructed.
  • the parameters are reduced to 1/1000 or even 1/10,000, which greatly reduces the calculation factor.
  • the complexity of the image processing speed is accelerated, and the implementability is provided from the technical level.
  • the attention mechanism to extract the feature map for the detailed information such as edges in the image and introduce the deconvolution process during the convolution process, the edge detail information in the image is preserved, so as to improve the mapping relationship between the noisy image and the real image , reducing image distortion and loss of image edge features.
  • the adversarial network can denoise the noisy 3D MRI images and achieve high-quality MR images that meet the diagnostic requirements of doctors.
  • the quality of the denoised image is better than the image obtained after denoising with the original convolutional neural network.
  • the application provides an image processing system, which includes:
  • the memory stores computer-readable codes, and executes the above-mentioned image processing method when the computer-readable codes are executed by the processor.
  • the above image processing method is presented in the form of computer readable code and stored in the memory, and when the system processor runs the computer readable code in the memory, the steps of the above image processing method are executed to obtain improved image processing. speed and optimize the effect of image edge information.
  • the present application provides a computer storage medium, which stores computer-readable codes, and executes the above-mentioned image processing method when the computer-readable codes are executed.
  • the above image processing method is presented in the form of computer readable code and stored on a computer storage medium, and when the processor runs the computer code on the medium, the steps of the above image processing method are executed to obtain an enhanced image Process speed and optimize the effect of image edge information.
  • the present application includes at least one of the following beneficial technical effects:
  • the application adopts an adversarial network generated based on the attention mechanism, which solves the problem of serious loss of edge information features in the target image due to the decline of the original generation adversarial network mapping ability;
  • the mean square error loss is introduced into the least squares loss function, which solves the problem of losing image details due to the over-smoothing of the image caused by the noise reduction of the CNN network, and avoids the disadvantage of over-smoothing the image caused by a single adversarial loss function. More image edge detail is preserved.
  • FIG. 1 is a flowchart of an image processing method according to an embodiment of the present application.
  • Figure 2 is an architectural diagram of a generator network according to an embodiment of the present application.
  • FIG. 3 is an architectural diagram of an attention mechanism module according to an embodiment of the present application.
  • FIG. 4 is an architectural diagram of a discriminator network according to an embodiment of the present application.
  • FIG. 5 is a flowchart of an image processing method according to another embodiment of the present application.
  • an image processing method includes the following steps:
  • the generator training step extracting noise image data from the training set as input images to train the generator network parameters by reducing the cycle consistency loss function, so that the output image of the generator network and the training set are noise-free Differences in images are reduced.
  • the generator network includes an attention mechanism module for enhancing the contrast of edge details of the input image.
  • the cycle consistency loss function may represent the degree of difference between the output image of the generator network and the noise-free image based on the discriminator network parameters.
  • S20, the discriminator training step, the output image and the noise-free image of the trained generator network are respectively input to the discriminator network to train the parameters of the discriminator network by reducing the loss function of the discriminator, so that the output of the discriminator network indicates Whether the input to the discriminator network is the output image of the trained generator network or an indication of the noise-free image.
  • a discriminator loss function may represent how well the output image of the generator network corresponds to the noise-free image.
  • the implementation principle of the above image processing method is as follows: by introducing a new convolutional neural network system architecture, the traditional training strategy is replaced by the adversarial network method, and the noisy image is used as input to allow artificially generated detailed information through deep learning to fill in due to noise. resulting image defects.
  • the adversarial network uses two convolutional neural network systems, namely: a so-called “generator”, which is a type of generator network; and a so-called “discriminator” network, which is For assessing the quality of images with magnified contrast.
  • the "discriminator” receives as input the noise image and the real image, and outputs a number such as -1 or 1.
  • the "discriminator” considers the noisy image to correspond to the original real image content (enhancing contrast). If the output is -1, the “discriminator” considers the noisy image to be the boosted output of the generator network.
  • the goal of training the generator is to maximize the output of the "discriminator” so that it becomes as realistic as possible.
  • the “discriminator” is trained to accurately distinguish between the original enhanced contrast content and the boosted content. The two networks alternate training steps to compete with each other and obtain the best parameters.
  • the image processing method is described in detail by taking 3D MRI human body image data as an example, wherein the training set contains a noisy image x, contains several samples ⁇ x 1 , x 2 , x 3 ,..., x n ⁇ and a noise-free image y contains several The sample ⁇ y 1 , y 2 , y 3 ,...,y n ⁇ , the noise-free image y can be understood as the real image obtained by removing the noise from the noise image.
  • the noise image sample x 1 is first decomposed or sliced to obtain several sub-band images as input data, and the first convolutional layer in the network constructed by the generator extracts the input data to obtain the feature representation, and the feature representation is usually is the feature matrix.
  • the features extracted here may be contrast, resolution, grayscale, and the like.
  • the correlation between the sub-band images can be established in the coding area, and then the attention mechanism can insert or superimpose the edge information of the image into the deconvolution of the decoding area through the correlation between the sub-band images. layer, which ultimately enables the output image to retain more edge information.
  • the first layer obtained by the feature matrix pooling operation is grouped into one layer, and finally the first layer of activation function is obtained by continuous pooling of the nonlinear enhancement operator.
  • the first layer of attention mechanism module is the self-attention map obtained by extracting the feature map from the input data to capture the multiple features in x 1 that can reflect the image edge detail information, and then transform and combine them.
  • the first layer of activation function layer is used as input data for feature extraction to obtain feature representation to obtain the second layer of convolutional layer.
  • the convolution operation of the second layer is roughly the same as that of the first layer, except that the number of convolution kernels is increased and more features are extracted.
  • the second layer of attention mechanism module dimension is the self-attention map obtained by extracting features from the first layer of convolution layer, the first layer of batch normalization layer and the first layer of activation function layer, respectively, for transformation and combination. Same as above, complete the third and fourth layers of convolution to obtain the fourth activation function layer and obtain the self-attention map of the third layer of attention mechanism module and the fourth layer of attention mechanism module, respectively.
  • the activation function layer, batch normalization layer and deconvolution layer are obtained sequentially based on the fourth layer activation function layer by layer-by-layer decoding relative to the convolution reverse operation, and then the fourth layer
  • the deconvolution layer is logically superposed with the self-attention map of the fourth layer attention mechanism module to obtain the fourth layer deconvolution layer of edge enhancement.
  • the deconvolution operation is performed relative to the third layer convolution with the enhanced fourth layer deconvolution layer as the input, as above, and the third layer, the second layer, and the first layer deconvolution layer of edge enhancement are completed in turn, Output image x 11 after fitting the edge-enhanced first deconvolution layer.
  • the above-mentioned output image x 11 filtered by the generator and the real image y 1 are respectively input into the discriminator, and each input is sequentially processed by four layers of convolution filtering, and then activated after being connected to the hidden layer by the first fully connected layer.
  • the function layer is processed nonlinearly, and finally the fully connected layer and the activation function layer with a unit of 1 are output to complete a round of iteration.
  • the next iteration can repeat the above steps with x 2 as the input image.
  • the generator network and the discriminator network can be trained alternately.
  • alternate training is performed in the order of generator training, discriminator training, generator training, and discriminator training steps, where one generator training step and one discriminator training step are referred to as sequential iterations.
  • the generator training step and the discriminator training step are exchanged in order, that is, the training is performed alternately in the order of the discriminator training step, the generator training step, the discriminator training step, and the generator training step, wherein one of the discriminators The training step and one generator training step are called successive iterations.
  • Both the discriminator network and the generator network can take the form of a convolutional neural network, and both have various parameters of the convolutional application network.
  • the parameters of the generator network may include the weights of the filters of each convolutional layer, the paranoia of each activation function layer, and the reinforcement parameters of each attention mechanism module;
  • the parameters of the discriminator network may include the paranoia of each activation function layer, the The weights of the filters of the convolutional layers and the degradation parameters of the fully connected layers.
  • the parameters of the generator network and the parameters of the discriminator network can be preset or randomly given values.
  • the training of the generator network is based on the training results of the discriminator network (that is, the training results of the parameters of the gradienter network), and the training of the discriminator network requires the use of the generator network.
  • the output image, so the training of the discriminator network is based on the training results of the generator network (that is, the training results of the parameters of the generator network), this way is called "adversarial", that is, the generator network and the discriminator network fight against each other.
  • This approach allows two adversarial networks to compete and continuously improve on each iteration based on the better and better results of the other network to train with better and better parameters.
  • the cycle consistency loss function in the generator training step can be obtained from the generator loss function and the discriminator loss function.
  • the cycle consistency loss function can be composed of two parts, where the first part is based on the mean square error output between the output image of the generator network and the noise-free image, and the second part is based on the output image of the generator network through all output of the discriminator network.
  • a mean squared error loss is added to the cycle consistency loss function to avoid the disadvantage of smooth image transition caused by a single adversarial loss function, thereby preserving more details of the image.
  • an MRI noise reduction network based on the least squares loss function generation anti-network based on 3D attention mechanism can be added to use 3D attention least squares for high-noise MRI images
  • Generative Adversarial Networks filter the image to arrive at a medical image that can be diagnosed by a doctor.
  • the least squares adversarial loss in order to improve the mapping ability between noisy images and real images and the training process of the network, can be expressed as:
  • G is the generator, where L LSGAN (G) represents the loss function of the generator, L LSGAN (D) is the loss function of the discriminator, and P x (x) and P y (y) represent the noise data and real label data, respectively Distribution; x represents noise data, y represents real label data, G(x) is the result of the generator output when noise image data is used as input, D(G(x)) is the discriminator when G(x) is used as input The probability of the output, G(y) is the probability of the discriminator output when the real label data is used as input, and IE represents the loss calculation function.
  • the mean square error function is:
  • d, w, and h are the depth, width and height of the extracted feature map, respectively;
  • L 3D a-LSGAN ⁇ 1 L mse + ⁇ 2 L LSGAN (G)
  • ⁇ 1 and ⁇ 2 are empirical parameters used to balance different ratios, which are set values; according to experience, we set ⁇ 1 and ⁇ 2 to be 1 and 0.0001, respectively.
  • the generator construction step is to construct a generator of a multi-layer deep convolutional neural network based on the U-Net network structure, the generator includes a skip-connected encoder-decoder network, and the skip connection structure of the U-Net network structure is added with self-
  • the attention mechanism is used to transfer the edge detail image information of the encoded region to the corresponding decoding region.
  • the edge detail image information here may refer to edge information, detail information and the like of an image. After processing the noisy image through this step, the details of some darker areas can also be clearly seen, such as the outline of the organ, the folds of the folds, the distribution network of the trachea, etc., which helps doctors to make correct analysis and diagnosis.
  • the number of parameters is reduced to one thousandth or even ten thousandths. One of them, thereby greatly reducing the complexity of the calculation factor, speeding up the image processing speed, and providing practicability from the technical level.
  • the discriminator construction step constructs the discriminator of the multi-layer deep convolutional neural network based on the generator network.
  • the MRI denoising network based on the least squares generative anti-network of 3D attention mechanism is as follows: the generator includes an encoding network formed by a multi-layer convolutional architecture, The decoding network and multi-layer self-attention mechanism module formed by the product architecture; each layer of convolution architecture corresponds to one layer of deconvolution architecture and one layer of attention mechanism module;
  • Each layer of convolutional architecture includes: convolutional layer, batch normalization layer and activation function layer;
  • Each layer of deconvolution architecture includes: deconvolution layer, batch normalization layer and activation function layer.
  • each layer of attention mechanism module may include: a first feature map extracted based on a convolutional layer of a corresponding layer convolutional architecture, a batch normalization layer extracted based on a corresponding layered convolutional architecture The second feature map of and the third feature map extracted based on the activation function layer of the corresponding layer convolution architecture.
  • the attention map can be obtained by transposing the third feature map and multiplying the second feature map by the softmax activation function; and then multiplying the first feature map and the attention map to obtain the self-attention feature map.
  • the image After being processed by the attention mechanism module, the image can obtain more detailed information and pass it to the decoding area through skip connections.
  • the above-mentioned first feature map, second feature map, and third feature map are related to parameters such as the length, width, and number of feature channels of the image.
  • the step of obtaining the output image in the generator training step S10 may include:
  • the convolution step during the convolution operation of each layer, the image data with noise in the training set is randomly cut into pieces and then used as input for feature extraction to obtain a convolution layer; the convolution layer is pooled to obtain a batch normalization layer, and the batch The normalization layer obtains the activation function layer through nonlinear combination of functions;
  • the deconvolution step in the deconvolution operation of each layer, the deconvolution layer is added to the self-attention feature map obtained by the self-attention mechanism module corresponding to the layer, and then a pooling operation is performed to obtain a batch normalization layer , and then activate the batch normalization layer through the activation function layer and output it;
  • the output image is obtained after all layers of convolution and deconvolution are completed.
  • the discriminator may comprise a convolutional architecture and fully connected layers with the same number of layers as the generator;
  • Each layer of convolutional architecture includes: convolution layer, batch normalization layer and activation function layer.
  • the step of obtaining the output indication in the discriminator training step S20 may include:
  • the convolution step taking the enhanced image output by the generator into blocks and then using it as an input to perform feature extraction to obtain a convolutional layer; pooling the convolutional layer to obtain a batch normalization layer, and performing a non-decoding process on the batch normalization layer through a function Linear combination to obtain activation function layer;
  • connection step is to non-linearly combine the features obtained by completing the convolution operations of all layers through the fully connected layer, and when the loss function of the discriminator is close to 1, it is determined that the input of the discriminator network is the generated after training.
  • the step of constructing the discriminator and before the training of the generator it may further include:
  • an encoder-decoder network similar to U-net network with skip connections is used as the generator network. All convolutions are performed by a 3D convolutional layer that processes 3D data, in which an attention mechanism is added to the skip connection part to transfer the detailed image information of the encoding area to the corresponding decoding area, so that the decoding network can transfer the detailed image information to the corresponding decoding area. back to the image.
  • the generator network contains a total of 8 layers, including 4 convolutional layers and 4 deconvolutional layers. Each layer contains a 3D convolutional layer, a batch normalization layer, and an activation function layer.
  • the size of the used convolution kernels is all 3 ⁇ 3 ⁇ 3 pixels, and the number of convolution kernels can be, for example, 32, 64, 128, 256, 128, 64, and 32.
  • the convolution strides are all 1 pixel.
  • the discriminator consists of four convolutional layers and two fully connected layers (including a first fully connected layer and a second fully connected layer). Each convolutional layer is followed by a batch normalization layer and a LeakeyRelu activation function layer. After the four-layer convolutional layer is connected to the first fully connected layer, the output unit of the first fully connected layer is 1024 values, followed by the LeakeyRelu activation function. The second fully connected layer is a fully connected layer with an output unit of 1 value and a LeakeyRelu activation function layer.
  • the discriminator and generator use the same convolution kernel size, both 3 ⁇ 3 ⁇ 3 pixels, and the number of convolution kernels in each layer is 32, 64, 128, 256.
  • the first convolutional layer outputs 32 feature maps
  • the second convolutional layer outputs 64 feature maps
  • the third convolutional layer outputs 128 feature maps
  • the fourth convolutional layer outputs 256 feature maps.
  • the MRI image with noise and the MRI image without noise are randomly divided into 3D pixel blocks and the corresponding noise-free MRI image is used as the input and label of the adversarial network for training, and the attention mechanism considers the difference between the blocks. Correlated information and can pass the important information of the coding region (convolutional layer) to the corresponding decoding part (deconvolutional layer) through skip connections.
  • the training of network parameters is completed, the training network is obtained, and the mapping relationship G from the MRI image with noise to the MRI image without noise is obtained at the same time.
  • denoise the noisy MRI images through the trained 3D attention least squares generative adversarial network to obtain denoised images that meet the doctor's diagnostic requirements.
  • the 3D attention least squares generative adversarial network can denoise the noisy 3D MRI images and achieve high-quality MR images that meet the diagnostic requirements of doctors. Edge details in the image can also be preserved by using an attention mechanism during encoding and decoding.
  • the method according to the present application can also be applied to image noise reduction in the fields of 3D SPECT images, low-dose 3D CT images, and low-count 3DPET after appropriate transformation.
  • the present application also provides an image processing system for a convolutional neural network, comprising: a processor and a memory; the memory stores computer-readable codes, and the processor executes the aforementioned image processing method when running the computer-readable codes any of the.
  • the implementation principle of the image processing system is as follows: the above-mentioned image processing method is presented in the form of computer-readable code and stored in the memory, and when the system processor runs the computer-readable code in the memory, the steps of the above-mentioned image processing method are executed to obtain Improve image processing speed and optimize the effect of image edge information.
  • the present application also provides a computer storage medium storing computer-readable codes, and the processor executes any one of the above image processing methods when running the computer-readable codes.
  • the implementation principle of the computer storage medium is as follows: the above-mentioned image processing method is presented in the form of computer-readable codes and stored on the computer storage medium, and when the processor runs the computer code on the medium, the steps of the above-mentioned image processing method are executed to Get the effect of speeding up image processing and optimizing image edge information.

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Abstract

Procédé et système de traitement des images, et support d'enregistrement lisible par ordinateur. Le procédé comprend une étape d'apprentissage de générateur consistant à : entrer une image de bruit pour apprendre des paramètres de réseau au moyen d'une fonction de perte, de façon à réduire la différence entre une image de sortie et une image sans bruit (S10) ; une étape d'apprentissage de discriminateur : entrer respectivement l'image de sortie et l'image sans bruit d'un réseau générateur entraîné dans un réseau discriminateur pour apprendre des paramètres de réseau par réduction d'une fonction de perte, de sorte qu'une sortie indique si une entrée est l'image de sortie ou l'image sans bruit du réseau générateur entraîné (S20) ; et répéter les étapes d'apprentissage de générateur et de discriminateur en utilisant différentes images de bruit de façon à obtenir les paramètres finaux des réseaux générateur et discriminateur en réduisant au minimum la fonction de perte (S30). Le procédé présente les effets d'accélération du traitement des images et d'optimisation des informations de bord d'image.
PCT/CN2020/112870 2020-09-01 2020-09-01 Procédé et système de traitement des images, et support d'enregistrement lisible par ordinateur WO2022047625A1 (fr)

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