CN109191472A - Based on the thymocyte image partition method for improving U-Net network - Google Patents
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Abstract
The invention discloses based on the thymocyte image partition method for improving U-Net network, comprising the following steps: UCSB breast image data set is carried out image preprocessing；In U-Net network, empty residual error module is added and pays attention to power module；U-Net network is trained according to the Training strategy of setting；Establishing includes that F1 scores, and the evaluation index of object level Dice coefficient and Hausdorff distance optimizes network by evaluation index, obtains optimal models；The cell image that need to divide is inputted into optimal models, up-samples to obtain segmentation masking-out by feature extraction and feature.The present invention creates a new cell image segmentation method, precision is lower during solving the problems, such as thymus gland Image Automatic Segmentation, improves the accuracy and efficiency of segmentation by improving a based fragmentation network.
Description
Technical field
The invention belongs to field of image processings, are related to a kind of based on the thymocyte image segmentation side for improving U-Net network
Method.
Background technique
In recent years, the disease incidence of colon cancer is higher and higher, and colorectal cancer is the cancer and woman that third is common in male
Second common cancer in female, wherein about 95% colorectal cancer is gland cancer.Under normal conditions, a typical body of gland is
It is made of the epithelial nucleus around the lumen region of formation inner tubular structure and cytoplasm.It is generated by galandular epithelium pernicious
Tumour, also referred to as gland cancer are most common cancer forms.In histopathological examination, Glands morphology is widely used in commenting
Estimate several gland cancer, including mammary gland, prostate and colon.And accurate body of gland segmentation is to obtain reliable morphology statistical information
One crucial prerequisite, these statistical informations show the invasion of tumour.Former, body of gland segmentation is by assessment biopsy sample
The problems such as pathology expert of gland structure carries out in this, but annotation there are reproducibilities limited, heavy workload by hand, time-consuming.
With pathological appearance is calculated, digitlization histology lantern slide is being widely used, and needs to analyze large-scale tissue disease
Data of science.Therefore, very high requirement is proposed to automatic division method in clinical practice, to improve segmentation efficiency and reliability,
Reduce the workload of virologist.
In the prior art, the body of gland in tissue pathological image usually is analyzed using various manual features or priori knowledge
Structure, such as based on the method for figure, polar coordinate space random field models, random polygon model etc..In recent years, deep learning is because of it
Powerful feature representation ability achieves huge success in the image recognition inter-related task of computer vision, also pushes
The development of medical image analysis.For example, U-Net achieves excellent properties in body of gland segmentation task, although U-Net be one compared with
For effective and simple model, but since the depth of model is inadequate, cause its feature representation ability limited.In order to further increase
Body of gland example segmentation performance, a depth profile sensing network for possessing specific profile loss function are suggested, and
Optimum performance is obtained in the challenge of MICCAI body of gland segmentation (Gland Segmentation, GlaS) scene.In addition, in the prior art
There are also the frame combined is supervised with side in complicated multichannel region and boundary scheme, realize that body of gland example is divided with this.
However, automatic body of gland segmentation is still a challenging task due to several key factors.Firstly, smart
It is extremely important to Morphological measurement result is extracted really to describe body of gland boundary, but since the down-sampling in neural network will lead to target
The detailed information at edge is lost, so that the characteristic pattern resolution ratio after up-sampling is not high, reduces segmentation precision.Secondly, to be split
Body of gland have different size and shapes, in particular with the increase of cancer grade, there is structure differentiation phenomenon in body of gland, increase
Segmentation difficulty.
Summary of the invention
To solve the above problems, the present invention creates new cell image point by improving a based fragmentation network
Segmentation method, precision is lower during solving the problems, such as thymus gland Image Automatic Segmentation, improves the accuracy and efficiency of segmentation.
To achieve the above object, the technical scheme is that it is a kind of based on the thymocyte image for improving U-Net network
Dividing method, comprising the following steps:
UCSB breast image data set is subjected to image preprocessing；
In U-Net network, empty residual error module is added and pays attention to power module；
U-Net network is trained according to the Training strategy of setting；
Establishing includes that F1 scores, and the evaluation index of object level Dice coefficient and Hausdorff distance is excellent by evaluation index
Change network, obtains optimal models；
The cell image that need to divide is inputted into optimal models, up-samples to obtain segmentation masking-out by feature extraction and feature.
Preferably, described image pretreatment includes rotation, cutting and the turning-over changed input picture for obtaining fixed size.
Preferably, described image pretreatment is that similarity transformation enhances method, and similarity transformation is obtained by following:
Formula is indicated with M1, constraint condition expression formula are as follows:
The minimum value for seeking M1 obtains matrix M are as follows:
Wherein, μsIt is equal to
It brings matrix M into M1 and obtains the warping function of similarity transformation:
Wherein, AiIt is only related with the set p at control point, it is obtained by following formula:
Preferably, the attention module definition is as follows:
Wherein,WithIt respectively represents and outputs and inputs, giThe gate signal that high-level contextual information provides is represented,Represent sigmoid activation primitive, ΘattIt include: linear transformation parameter And biasing
Preferably, the empty convolution kernel in the empty residual error module in traditional convolution kernel by being inserted into different scale
Zero obtains.
Preferably, the Training strategy set is training method end to end, network random cropping from original image
The region of one 464*464 exports the contour prediction mask of thymus gland as input；Training has 75 stages altogether, and every batch of 20 is opened
Image, initial learning rate be 0.001, last classification layer learning rate be 0.01,1000 learning rates of every iteration multiplied by 0.1, and
Using 0.9 momentum and the decaying of 0.0005 weight.
Preferably, the F1 scoring is defined by the formula:
Wherein, TP representative is originally used for thymus gland and is detected as thymus gland, and FP represents non-thymus gland originally but is detected as thymus gland, and FN is represented
It is originally used for thymus gland but is detected as non-thymus gland, Precision represents accuracy rate, and Recall represents recall rate.
Preferably, the object level Dice coefficient is set measuring similarity function, calculates X by following formula, Y sample it is similar
Degree:
Preferably, the Hausdorff distance is to be obtained for the distance between X in metric space, Y subset by following formula:
Beneficial effects of the present invention are as follows:
We train our model with the training set of UCSB breast data set, while in the test of the data set
It is tested on collection.By replacing the common convolution in tradition U-net model with empty convolution, performance is promoted, compared to biography
Model of uniting improves 4.8%.After increasing attention power module, the performance of model has reached highest 89.9%, has compared to basic model
Compared with much progress.
Meanwhile we also compare and analyze with several outstanding parted patterns, including SegNet, FCN-8 and
Deeplab-v3 etc., and obtained the result of assessment: only from the point of view of F1score, the property of FCN-8 model and tradition U-Net model
Can be relatively poor, reason is that the number of plies of the two models is less, not good enough to the extraction of feature；SegNet model and
The number of plies of Deeplab-v3 model is deeper, and performance is also relatively preferable, but on UCSB breast data set, is still not so good as us
Model.
The invention proposes a kind of for dividing the improvement U-Net model of thymocyte, which divides in thymocyte
In have preferable performance, have benefited from introducing empty residual error module and pay attention to power module, preferably resolve thymus gland in segmentation task
The problem of thymus gland resolution ratio caused by not of uniform size and down-sampling declines compares other medical image segmentation models, the present invention
The higher segmentation masking-out of precision can not only be generated in cell segmentation task, and splitting speed is faster, robustness is more excellent.And
This model is more general, is easy to apply to the segmentation task of other medical images by training and fine tuning.
Detailed description of the invention
The step of Fig. 1 is the thymocyte image partition method based on improvement U-Net network of embodiment of the present invention method
Flow chart；
Fig. 2 is embodiment of the present invention method based in S20 in the thymocyte image partition method for improving U-Net network
Pay attention to power module concrete operation figure.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
On the contrary, the present invention cover it is any be defined by the claims the substitution made on the essence and scope of the present invention, modification,
Equivalent method and scheme.Further, in order to make the public have a better understanding the present invention, details of the invention is retouched below
It is detailed to describe some specific detail sections in stating.The description of part without these details for a person skilled in the art
The present invention can also be understood completely.
It is technical solution of the present invention based on the thymocyte image partition method for improving U-Net network referring to Fig. 1, Fig. 1
Embodiment step flow chart, comprising the following steps:
UCSB breast image data set is carried out image preprocessing by S10；
S20 is added empty residual error module and pays attention to power module in U-Net network；
S30 is trained U-Net network according to the Training strategy of setting；
S40, establishing includes that F1 scores, and the evaluation index of object level Dice coefficient and Hausdorff distance is referred to by assessment
Mark optimization network, obtains optimal models；
The cell image that need to divide is inputted optimal models by S50, up-samples to obtain segmentation illiteracy by feature extraction and feature
Version.
In specific embodiment, the image preprocessing in S10 includes rotation, cuts and turning-over changed obtain the defeated of fixed size
Enter image, be specifically as follows similarity transformation enhancing method, similarity transformation is obtained by following:
Formula is indicated with M1, constraint condition expression formula are as follows:
The minimum value for seeking M1 obtains matrix M are as follows:
Wherein, μsIt is equal to
It brings matrix M into M1 and obtains the warping function of similarity transformation:
Wherein, AiIt is only related with the set p at control point, it is obtained by following formula:
Referring to fig. 2, it is the operation schematic diagram that power module is paid attention in S20, attention module definition is as follows:
Wherein,WithIt respectively represents and outputs and inputs, giThe gate signal that high-level contextual information provides is represented,Represent sigmoid activation primitive, ΘattIt include: linear transformation parameter And biasing
Thymus gland segmentation is a complicated task, needs very deep network just and can be carried out significant feature extraction.Cause
This, we realize that effective gradient is propagated using residual unit in network frame.Traditional residual unit can be with is defined as:
Y=f (x, Wi)+x
Wherein x and y is respectively and outputs and inputs, WiFor weight, f representative function W2(σ(W1X)), wherein σ is ReLU function.
It inputs x and f and passes through phase add operationIt is combined together.
Traditional convolutional neural networks (convolutional neural network, CNN) using maximum pond layer with
Convolutional layer in conjunction with mode increase receptive field, and maximum pond layer will lead to the loss of low-level information, thus to Accurate Segmentation
It causes to seriously affect.In order to weaken the influence of low-level information loss, other than using traditional residual unit, we mention in feature
It also added a kind of residual unit during taking: i.e. empty residual unit.Difference between empty residual sum conventional residual is
Empty residual error uses empty convolution, and empty convolution kernel is obtained by being inserted into the zero of different scale in traditional convolution kernel
's.Compared to traditional convolution operation, empty convolution can obtain biggish receptive field in the case where not increasing parameter, and obtain with it is defeated
Enter the identical characteristic pattern of size.Each 3 × 3 common convolution need to only be replaced with 3 × 3 empty convolution just by we in the present invention
Empty convolution can be merged into residual unit.During network initial down-sampling, we can also be by maximum pond layer
With the combination of residual unit because excessively will lead to the sharp increase of model parameter using empty residual unit.
To solve the problems, such as multiscale target, invention introduces empty residual error modules, but empty convolution is a kind of sparse meter
It calculates, this, which may result in, generates grid pseudomorphism.Therefore it attempts to be extracted from the high-level feature of CNN accurately with attention power module
Pixel-level attention feature, the module are able to suppress the activation of extraneous areas, can thus weaken the positioning of nontarget area.
In specific embodiment, respectively to high-level feature g and low level feature xl1 × 1 convolution operation is executed, to reduce
The port number of CNN characteristic pattern.Two kinds of characteristic patterns are merged again, and are successively normalized by ReLU function, 1 × 1 convolution, batch
(batch normalization, BN), sigmoid function and up-sampling, obtain weighted value a, finally, high-level feature with plus
Low level feature after powerIt is added and carries out upper sampling process gradually.After AG module is added, model can be made further to subtract
Few positioning to non-body of gland region, and focus more on the study of thymus structure.
The Training strategy set in S30 is training method end to end, network random cropping one from original image
The region of 464*464 exports the contour prediction mask of thymus gland as input；Training has 75 stages altogether, and every batch of 20 opens image,
Initial learning rate is 0.001, and last classification layer learning rate is 0.01, and 1000 learning rates of every iteration are used multiplied by 0.1
0.9 momentum and 0.0005 weight decaying.
In S40, F1 scoring is defined by the formula for assessing the accuracy of single thymus gland detection, F1 scoring:
Wherein, TP representative is originally used for thymus gland and is detected as thymus gland, and FP represents non-thymus gland originally but is detected as thymus gland, and FN is represented
It is originally used for thymus gland but is detected as non-thymus gland, Precision represents accuracy rate, and Recall represents recall rate.
Object level Dice coefficient is set measuring similarity function, for assess thymus gland and divide mask based on volume
Accuracy calculates X by following formula, the similarity of Y sample:
Hausdorff distance is for the distance between X in metric space, Y subset, for assessing thymus gland and segmentation mask
The similitude based on boundary, obtained by following formula:
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.
Claims (9)
1. a kind of based on the thymocyte image partition method for improving U-Net network, which comprises the following steps:
UCSB breast image data set is subjected to image preprocessing；
In U-Net network, empty residual error module is added and pays attention to power module；
U-Net network is trained according to the Training strategy of setting；
Establishing includes that F1 scores, and the evaluation index of object level Dice coefficient and Hausdorff distance optimizes net by evaluation index
Network obtains optimal models；
The cell image that need to divide is inputted into optimal models, up-samples to obtain segmentation masking-out by feature extraction and feature.
2. the method according to claim 1, wherein described image pretreatment includes that rotation, cutting and overturning become
Get the input picture of fixed size in return.
3. the method according to claim 1, wherein described image pretreatment be similarity transformation enhance method, it is similar
Transformation is obtained by following:
Formula is indicated with M1, constraint condition expression formula are as follows:
The minimum value for seeking M1 obtains matrix M are as follows:
Wherein, μsIt is equal to
It brings matrix M into M1 and obtains the warping function of similarity transformation:
Wherein, AiIt is only related with the set p at control point, it is obtained by following formula:
4. the method according to claim 1, wherein the attention module definition is as follows:
Wherein,WithIt respectively represents and outputs and inputs, giThe gate signal that high-level contextual information provides is represented,Represent sigmoid activation primitive, ΘattIt include: linear transformation parameterAnd biasing
5. the method according to claim 1, wherein empty convolution kernel in the cavity residual error module by
The zero of different scale is inserted into traditional convolution kernel to obtain.
6. the method according to claim 1, wherein the Training strategy set is training side end to end
Formula, the region of network one 464*464 of random cropping from original image export the contour prediction mask of thymus gland as input；
Training has 75 stages altogether, and every batch of 20 opens image, and initial learning rate is 0.001, and last classification layer learning rate is 0.01, often
1000 learning rates of iteration are decayed multiplied by 0.1, and using the weight of 0.9 momentum and 0.0005.
7. the method according to claim 1, wherein F1 scoring is defined by the formula:
Wherein, TP representative is originally used for thymus gland and is detected as thymus gland, and FP represents non-thymus gland originally but is detected as thymus gland, and FN is represented originally
For thymus gland but it is detected as non-thymus gland, Precision represents accuracy rate, and Recall represents recall rate.
8. the method according to claim 1, wherein the object level Dice coefficient is set measuring similarity letter
Number, calculates X by following formula, the similarity of Y sample:
9. the method according to claim 1, wherein Hausdorff distance is for X in metric space, Y
The distance between subset is obtained by following formula:
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