CN110120055A - Fundus fluorescein angiography image based on deep learning is without perfusion area automatic division method - Google Patents

Fundus fluorescein angiography image based on deep learning is without perfusion area automatic division method Download PDF

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CN110120055A
CN110120055A CN201910294122.9A CN201910294122A CN110120055A CN 110120055 A CN110120055 A CN 110120055A CN 201910294122 A CN201910294122 A CN 201910294122A CN 110120055 A CN110120055 A CN 110120055A
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叶娟
吴健
金凯
尤堃
潘相吉
陆逸飞
刘志芳
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Zhejiang University ZJU
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Abstract

The invention discloses a kind of fundus fluorescein angiography image based on deep learning is without perfusion area automatic division method.The present invention is trained the convolutional neural networks of building without perfusion area eyeground contrastographic picture eyeground contrastographic picture for doctor's manual segmentation mark, meet the final output value of convolutional neural networks that doctor marks as a result, so as to carry out automatic segmentation identification eyeground without perfusion area to diabetic retinopathy using trained convolutional neural networks.Method of the invention passes through the eyeground contrastographic picture for having no perfusion area position mark, realizes the feature needed for learning in training example eyeground contrastographic picture library automatically using deep learning and carries out semantic segmentation.Continue to optimize the parameter of convolutional neural networks in the training process, extract data characteristics, thus adjuvant clinical use in when carrying out Fundus laser treatment to diabetic retinopathy, identify no perfusion area in need for the treatment of, accurately assist Fundus laser.

Description

Fundus fluorescein angiography image based on deep learning is without perfusion area automatic division method
Technical field
The invention belongs to image processing techniques neighborhoods, and in particular to a kind of fundus fluorescein angiography image based on deep learning Without perfusion area automatic division method.
Background technique
Diabetic retinopathy (DR) is insulin metabolism exception, causes ocular tissue, nerve and blood vessel microcirculation change, The nutrition of eye and the damage of visual function are caused, is the most common diabetic complication.The whole world has more than 6,000 ten thousand DR trouble at present Person, illness initial stage has no obvious abnormal symptom, but eventually results in visual loss, has become one of four big diseases causing blindnesses.Cause This, the early discovery of DR, early treatment are extremely important, closely related with the eyesight of patient's prognosis.
Fundus laser is the most important treatment means of current DR.But it is needed experienced when Fundus laser is treated at present Retinopathy doctor identifies many places to real-time and precise referring to the energy of fundus fluorescein angiography (FFA) Lai Dingwei lesion and setting laser The eyeground DR in need for the treatment of lesion is not available on FFA image there are certain difficulty and accurately assists Fundus laser without perfusion area.DR Computer-aided diagnosis (CADx) system significantly reduce the diagosis pressure of doctor, improve work efficiency, it is most important that The influence of factor and individual subjective factor when can reduce diagnosis, improves the accuracy of clinical diagnosis.The CADx system of DR is also at present Can not intelligent recognition need the lesion of laser therapy, also fail to combine closely auxiliary diagnosis and treatment, still have greatly improved Space.Realize the DR lesion that intelligent recognition needs Fundus laser to treat, it is necessary to which precisely being marked by oculist needs in FFA image Then no perfusion area to be treated is trained and learns to the FFA image of mark by constructing convolutional neural networks (CNN). Construct based on FFA picture depth learn without perfusion area identifying system, accurately identification needs the DR lesion of laser therapy, is reality The key technology of existing DR Brilliant Eyes bottom Laser navigation system, has urgent clinical demand in DR diagnosis and treatment.
Summary of the invention
In order to solve the problems in background technique, the present invention provides a kind of fundus fluorescein angiography figure based on deep learning As no perfusion area automatic division method, this method can make full use of the information in FFA image, to realize that semantic segmentation identifies DR without perfusion area.
The technical solution adopted by the invention is as follows:
The present invention the following steps are included:
Step 1: collecting and segmentation marks fundus fluorescein angiography image, be containing no perfusion by fundus fluorescein angiography image tagged Area, without no perfusion area and the image of three classes containing laser spot, no perfusion area image will be contained and be put into data set；
Step 2: by being pre-processed containing no perfusion area image in step 1 data set, by by pretreated containing no filling Infuse the data of area's image construction tranining database；The pretreatment is flat to image denoising is successively carried out containing no perfusion area image Cunning, picture superposition, image drop sampling and pixel normalized；
Step 3: the training dataset after being expanded after being expanded to the tranining database data in step 2, amplification Method is using Image Reversal and the random method that Gaussian noise is added；
Step 4: mark without perfusion area contour line containing no perfusion area image to what training data after amplification was concentrated, use Unrestrained fill of water will be converted to binary segmentation image containing no perfusion area image after marking contour line；
Step 5: building convolutional neural networks；
Step 6: using the convolutional neural networks of step 4 treated training dataset training step 5, according to setting when training The parameter of fixed learning rate adjustment convolutional neural networks, thus the volume for no perfusion area semantic segmentation after repeatedly being trained Product neural network；
Step 7: last in convolutional neural networks by the convolutional neural networks in image input step 6 to be split after training The class probability of each pixel in image to be split is calculated by softmax function for one layer of output valve, and then realizes The semantic segmentation of image perfusion area to be split.
In the step 1, fundus fluorescein angiography image mainly by forming containing laser spot and without two class image of laser spot, In the image containing laser spot be divided into containing no perfusion area image and without no perfusion area image.
The convolutional neural networks building process of the step 5 is as follows:
Convolutional neural networks mainly by input layer, four up-sampling modules, four down sample modules, output convolution module, Output convolutional layer is sequentially connected with data pass order to be formed, and input layer inputs first up-sampling module, four up-sampling moulds Block is sequentially connected, and the 4th up-sampling module inputs first down sample module, and four down sample modules are sequentially connected, and the 4th Down sample module is connected to output convolutional layer through output convolution module output；
Up-sampling module includes two convolution modules and maximum pond layer, and two convolution modules are sequentially connected output to maximum Pond layer；Down sample module includes two convolution modules and down-sampling layer, and two convolution modules are sequentially connected output to down-sampling Layer；
Wherein, second convolution mould of second convolution module and the 4th down sample module of first up-sampling module Fusion has attention mechanism, second convolution module of second up-sampling module and the second of third down sample module between block Fusion has attention mechanism between a convolution module, and third up-samples second convolution module and second down-sampling mould of module Fusion has an attention mechanism between second convolution module of block, second convolution module of the 4th up-sampling module with first Fusion has attention mechanism between second convolution module of down sample module；
The attention mechanism include sequentially connected input layer, the linear elementary layer of rectification, 1*1 convolutional layer, Sigmoid function category layer and resampling layer, input layer are made of two 1*1 convolutional layers, two 1*1 convolutional layers of input layer Input is respectively that the output of second convolution module in corresponding up-sampling module is rolled up with second in corresponding down sample module The output of volume module, two 1*1 convolutional layers of input layer are exported through ReLU activation primitive layer to one-dimensional convolutional layer after being added, one-dimensional Convolutional layer is exported through Sigmoid activation primitive layer to resampling layer, the output of resampling layer and in corresponding down sample module The output that the output of two convolution modules is multiplied as attention mechanism, the output of each attention mechanism and corresponding down sample module Output after the output splicing of middle down-sampling layer as corresponding down sample module.
The convolution module and output convolution module by convolutional layer, batch normalization layer and the linear elementary layer of rectification according to Secondary connection composition.
The learning rate that sets is 0.1 when the step 5 training, and training round is 500 wheels, learning rate respectively 250 wheels with Decay at 400 wheels, attenuation rate 0.1；Trained learning rate is less than or equal to the preceding learning rate once trained every time.
Beneficial effects of the present invention
The present invention can apply to data volume relatively small database and be realized certainly after data amplification by deep learning It is dynamic to learn required feature from tranining database and carry out discriminant classification, the data for judgement are constantly corrected in the training process Feature and adjustment convolutional neural networks parameter, so that the sensibility and specificity in clinical application is improved, with training example The accuracy and reliability of the increased number of FFA image, semantic segmentation also will further improve.
Detailed description of the invention
Fig. 1 is full convolutional neural networks model structure of the invention.
Fig. 2 is attention mechanism of the invention.
Specific embodiment:
The present invention will be further described with reference to the accompanying drawings and examples.
The core of semantic segmentation method of the present invention is: for comprising doctor divide mark containing no perfusion area FFA image, Multilayer convolutional neural networks are established, the training based on FFA image to convolutional neural networks makes the final output of convolutional neural networks Semantic segmentation result meet doctor mark as a result, so as to be carried out using trained convolutional neural networks to no perfusion area Automatic segmentation identification.
Specific embodiment:
Based on the DR of deep learning without perfusion area intelligent identification Method the following steps are included:
Step 1: acquisition and segmentation mark fundus fluorescein angiography image
The FFA image of acquisition comes from 2nd Affiliated Hospital Zhejiang University School of Medicine Eye Center, and the FFA image of acquisition comes from 2nd Affiliated Hospital Zhejiang University School of Medicine Eye Center comes from 67 in 25 months of in August, 2016 in September, 2018 74 of patient, age at 28 years old to 84 years old, pass through confocal Fundus angiography instrument (the Heidelberg retina of Heidelberg Angiograph, HRA) implement Fundus fluorescein (fundus fluoreseein angiography, FFA).By Two oculists shoot, and eye fundus image resolution ratio is 768 × 768 pixels.Before shooting eyeground radiography, using mydriatic and Fluorescein sodium contrast agent has serious refracting media problem that cannot take eye fundus image, then excludes patient to study it at this Outside.Without perfusion area by five veteran oculists, according to diabetic retinopathy clinical guidelines (Diabetic Retinopathy PPP-updated 2016) guide mark.Blind is used to expert diagnosis group, they can not obtain The data of deep learning prediction.
Step 2: being pre-processed to containing no perfusion area image: by being carried out containing no perfusion area image in step 1 data set Smooth image denoising, picture superposition, image drop sampling and the normalized pretreatment of pixel are successively carried out, by by pre- The data containing no perfusion area image construction tranining database of reason.
Step 3: being carried out using overturning and the random method that Gaussian noise is added to the tranining database data in step 2 Amplification.
Step 4: mark without perfusion area contour line containing no perfusion area image to what training data after amplification was concentrated, use Unrestrained fill of water will be converted to binary segmentation image containing no perfusion area image after marking contour line
Step 5: building convolutional neural networks；
Convolutional neural networks framework has used a kind of full convolution mind of the attention mechanism for no perfusion area semantic segmentation technology Through network model, model structure is as shown in Figure 1.Input picture having a size of 512*512 passes through convolution module, i.e., 3*3 volumes twice Product-batch normalization (batch normalization, BN)-rectification linear unit (rectified linear unit, ReLU after) operating, 1/2 length and width dimensions are down-sampled to using maximum pond layer (maxpooling).After four aforesaid operations Obtain final feature.Enter the up-sampling stage later, in the up-sampling stage, feature is same with the down-sampling stage after up-sampling Size characteristic input attention mechanism generate weight distribution after feature, latter two merging features after input a convolution module, The up-sampling stage includes four aforesaid operations, after obtaining result identical with original image size using a convolution module, is passed through Convolution kernel is that the convolutional layer of 1*1 and softmax function obtain two Classification Semantics segmentation results.
For attention mechanism of the invention as shown in Fig. 2, x represents the characteristic pattern before down-sampling, m represents gate signal (gating Signal), be the next stage feature of x, having a size of x by it is down-sampled it is primary after size, the two swashs after being added by ReLU Function living；Sigmoid activation primitive is recycled to obtain characteristic pattern weight port number dimensionality reduction to 1 using 1*1 convolution later, and Its size resampling (resample) is characterized to the size of figure x.The weight and x are carried out across connection (skip Connection), each channel multiplication obtains the feature x ' after weighting.Attention mechanism can emphasize knowledge of the model to key area Not.
Wherein, convolutional neural networks receive FFA contrastographic picture as input, the i.e. image of training data concentration.Full convolution Full articulamentum is replaced with up-sampling and is used for semantic segmentation by neural network.The feature of extraction is reduced to output ruler by up-sampling It is very little, so that the classification results of each pixel in original image are obtained, to obtain segmentation result.Attention mechanism is from human vision Inspiration is obtained in attention mechanism, for the mankind when observing things, often observation pays attention to regional area according to demand.Attention mechanism is logical It crosses weight distribution and simulates the process, using bottom (front layer of forward direction transmitting) feature by noticing that function obtains weight.Convolutional layer (convolution) it for extracting characteristics of image, plays a decisive role to model recognition effect.ReLU will be shown as activation primitive It writes feature and passes through model, filtering useless feature.
Step 6: training convolutional neural networks
Nerve net in the convolutional neural networks is repeatedly trained using the correspondence semantic segmentation training example FFA image Network framework, according to the learning rate 0.1 of setting when training, training round is 500 wheels, and learning rate declines at 250 wheels and 400 wheels respectively Subtract, attenuation rate 0.1.Using neural network configuration parameters described in SGD algorithm optimization, to obtain the multiple of no perfusion area identification Convolutional neural networks after training；
Preferably, use intersection entropy function as model loss function when training, use SGD algorithm as optimizer, Middle momentum is 0.9, and initial learning rate is 0.1, and weight decays to 0.0001；Cross entropy cost function has nonnegativity, works as reality When output valve and desired value are close, cost function is close to 0.Its expression formula are as follows:
Wherein, yiFor the desired output of i-th of neuron, aiFor its real output value, n is the neuron for participating in calculating Total number
Preferably, the inertia of the stochastic gradient descent algorithm simulation object of which movement based on momentum, it is certain when optimizing update More new direction before retaining in degree, simultaneously also by the more new direction that this study fine tuning is final, to increase study Stability, and there is certain ability for getting rid of local optimum.Its expression formula are as follows:
Δxt=m* Δ xt-1-α*gt
Wherein Δ xtWith Δ xt-1The displacement at respectively t and t-1 moment updates, and m, that is, momentum, α are learning rate, gtFor t moment Gradient.
Preferably, when training convolutional neural networks, learning rate is reduced with model training stage, this example is using 0.1 Initial learning rate, training round are 500 wheels, and learning rate is decayed at 250 wheels and 400 wheels respectively, attenuation rate 0.1.
Step 7: image to be split is calculated by softmax function in the output valve of the last layer in convolutional neural networks In each pixel class probability, take the maximum value of channel dimension to index to obtain no perfusion area semantic segmentation result.
This method from feature needed for training data focusing study and carries out semantic point automatically by deep learning, realization It cuts, continues to optimize in training process for sentencing another characteristic and parameter.Early period test in, this method used 332 width by Oculist's mark is trained without perfusion area FFA image, and it includes the eye fundus image without perfusion area that test set, which includes 60, this Convolutional neural networks described in invention are after training, and semantic segmentation is without perfusion area registration up to 65.24%.More than DR based on deep learning divides identifying system without perfusion area automatically, can be applied to hospital clinical, tele-medicine and auxiliary and controls The fields such as treatment.

Claims (5)

1. the fundus fluorescein angiography image based on deep learning is without perfusion area automatic division method, which is characterized in that including following Step:
Step 1: collect and segmentation mark fundus fluorescein angiography image, by fundus fluorescein angiography image tagged be containing no perfusion area, Without no perfusion area and the image of three classes containing laser spot, no perfusion area image will be contained and be put into data set；
Step 2: by being pre-processed containing no perfusion area image in step 1 data set, by by pretreated containing no perfusion area The data of image construction tranining database；The pretreatment is image denoising is smooth, schemes to successively carrying out containing no perfusion area image Image contrast enhancing, image drop sampling and pixel normalized；
Step 3: the training dataset after being expanded after being expanded to the tranining database data in step 2, amplification method Using Image Reversal and the random method that Gaussian noise is added；
Step 4: mark without perfusion area contour line containing no perfusion area image to what training data after amplification was concentrated, it is unrestrained using water It fills and will be converted to binary segmentation image containing no perfusion area image after marking contour line；
Step 5: building convolutional neural networks；
Step 6: using the convolutional neural networks of step 4 treated training dataset training step 5, according to setting when training Learning rate adjusts the parameter of convolutional neural networks, thus the convolution mind for no perfusion area semantic segmentation after repeatedly being trained Through network；
Step 7: by the convolutional neural networks in image input step 6 to be split after training, the last layer in convolutional neural networks Output valve the class probability of each pixel in image to be split is calculated by softmax function, and then realize to point Cut the semantic segmentation of image perfusion area.
2. a kind of fundus fluorescein angiography image based on deep learning according to claim 1 is without the perfusion area side of segmentation automatically Method, which is characterized in that in the step 1, fundus fluorescein angiography image is mainly by containing laser spot and without two class image of laser spot Composition, wherein the image containing laser spot is divided into containing no perfusion area image and without no perfusion area image.
3. a kind of fundus fluorescein angiography image based on deep learning according to claim 1 is without the perfusion area side of segmentation automatically Method, which is characterized in that the convolutional neural networks building process of the step 5 is as follows:
Convolutional neural networks are mainly by input layer, four up-sampling modules, four down sample modules, output convolution module, output Convolutional layer is sequentially connected with data pass order and is formed, input layer input first up-sampling module, four up-sampling modules according to Secondary connection, the 4th up-sampling module input first down sample module, and four down sample modules are sequentially connected, adopt under the 4th Egf block is connected to output convolutional layer through output convolution module output；
Up-sampling module includes two convolution modules and maximum pond layer, and two convolution modules are sequentially connected output to maximum pond Layer；Down sample module includes two convolution modules and down-sampling layer, and two convolution modules are sequentially connected output to down-sampling layer；
Wherein, first up-sampling module second convolution module and the 4th down sample module second convolution module it Between fusion have an attention mechanism, second convolution module of second up-sampling module and second of third down sample module roll up Fusion has an attention mechanism between volume module, and third up-samples second convolution module and second down sample module of module Fusion has an attention mechanism between second convolution module, second convolution module of the 4th up-sampling module with first under adopt Fusion has attention mechanism between second convolution module of egf block；
Each attention mechanism includes sequentially connected input layer, the linear elementary layer of rectification, 1*1 convolutional layer, Sigmoid letter Number classification layer and resampling layer, input layer are made of two 1*1 convolutional layers, the input difference of two 1*1 convolutional layers of input layer For output and second convolution module in corresponding down sample module of second convolution module in corresponding up-sampling module Output, two 1*1 convolutional layers of input layer are exported through ReLU activation primitive layer to one-dimensional convolutional layer, one-dimensional convolutional layer warp after being added Sigmoid activation primitive layer is exported to resampling layer, the output of resampling layer and second convolution in corresponding down sample module The output of module is multiplied output as attention mechanism, the output of each attention mechanism and down-sampling in corresponding down sample module Output after the output splicing of layer as corresponding down sample module.
4. a kind of fundus fluorescein angiography image based on deep learning according to claim 3 is without the perfusion area side of segmentation automatically Method, which is characterized in that the convolution module and output convolution module is linear single by convolutional layer, batch normalization layer and rectification First layer is sequentially connected composition.
5. a kind of fundus fluorescein angiography image based on deep learning according to claim 1 is without the perfusion area side of segmentation automatically Method, which is characterized in that the learning rate that sets is 0.1 when the step 5 training, and training round is 500 to take turns, and learning rate exists respectively Decay at 250 wheels and 400 wheels, attenuation rate 0.1；Trained learning rate is less than or equal to the preceding learning rate once trained every time.
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