CN109190567A - Abnormal cervical cells automatic testing method based on depth convolutional neural networks - Google Patents
Abnormal cervical cells automatic testing method based on depth convolutional neural networks Download PDFInfo
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Abstract
The present invention discloses a kind of abnormal cervical cells automatic testing method based on depth convolutional neural networks, mainly comprises the steps that the artificial mark of (1) TCT cervical cell picture；(2) segmentation of cell picture has been marked；(3) more classification of cell after dividing；(4) training of the cell picture of all categories in migration models；(5) to segmentation, identification, the positioning of cytological map piece to be detected；(6) for the dividing processing again and identification of abnormal cell group.Cell after segmentation is divided into 9 classes by abnormal cervical cells automatic testing method of the present invention, is trained using transfer learning mode, obtains being fitted preferable more disaggregated models.The model can be identified, screened and be accurately positioned to unlabelled source cervical cell picture, and do dividing processing again and identification to the abnormal cell detected, improve the accuracy rate of detection.The present invention has preferable auxiliaring effect in cervical cell pathological diagnosis field.
Description
Technical field
The invention belongs to cervical cell pathological diagnosis fields, and in particular to a kind of based on the different of depth convolutional neural networks
Normal cervical cell automatic testing method.
Background technique
Cervical carcinoma is high-incidence in recent years, has become the social concern for threatening women life.Current effective diagnosis of cervical cancer
Method is cervical smear pathologic finding, and this method is paid a home visit after needing pathologist to find sick cell by micro- sem observation
It is disconnected.This aspect needs to expend a large amount of manpower and material resources, on the other hand the accuracy of diagnosis be easy by doctor's subjective factor or
The influence of visual fatigue.Therefore the cervical cell pathological diagnosis technology of automation becomes more and more important.
Existing cervical cell identification technology is usually based on fine segmentation and feature extraction, however due to adopting in microscope
The overlapping of the cell image of collection, presence that is irregular, dyeing the problems such as uneven bring very big be stranded to the fine segmentation of cell
Difficulty, while can there are problems that validity feature can not be extracted during feature extraction or excessively introduce invalid feature, Wu Faqu
Obtain good effect.
For existing abnormal cervical cells automatic measurement technique, negative, two classification of the positive is mostly used to do identification inspection greatly
It surveys, since the similitude and cell mass of abnormal cervical cells and basal cell are difficult to discrimination property, classifying quality is caused to be paid no attention to
Think.And current cell classification is mainly all directed to individual cells, and excessive processing is not done for cell mass, leads to cell mass
Detection effect it is undesirable.Meanwhile existing abnormal cervical cells automatic identifying method mainly all with classification based on, it is desirable that input
It is the picture after dividing processing, the initial acquisition sample of cervical cell cannot be handled directly, abnormal palace cannot be reached
The direct purpose of the identification and positioning of neck cell.
Summary of the invention
In view of the deficiencies of the prior art, the present invention proposes a kind of abnormal cervical cells based on depth convolutional neural networks certainly
Dynamic detection method.
The technical solution adopted by the present invention to solve the technical problems mainly includes the following steps:
Step 1: the source cervical cell image of acquisition manually being marked, obtaining includes abnormal cervical cells labeling position information
File.
Step 2: processing is split according to mark gained file and source cervical cell image；
1) cell mass is divided using threshold method first: being carried out pair according to the location information in the location information of segmentation and mark file
Than, judge whether to belong to abnormal cervical cells group, after be partitioned into the location information 299 ' 299 cervical cell group, threshold value point
It is as follows to cut formula:
, (1)
Wherein,Indicate the gray value of pixel in target image,Indicate the gray value of pixel in source images,Indicate taken threshold value.
2) discrete cellular is partitioned into using Adaptive Thresholding: determines the position of nucleus first, cell nuclear location is believed
Location information in breath and mark file compares, and judges whether to belong to abnormal cell, then be divided centered on nucleus
The cell picture of 299 ' 299 sizes out.
Step 3: combination cell pathology and computer technology relevant knowledge are classified the cell after segmentation, carefully
Born of the same parents group mainly includes that negative cervical cell mass and positive cervical cell roll into a ball two parts；Discrete cellular is broadly divided into negative single squamous
Cell, negative single basal cell, neutral grain, negative overlapping squamous cell, negative overlapping basal cell, impurity and positive uterine neck
Cell.
Step 4: various types of cells picture obtained above being overturn, the processing such as contrast variation, expand the sample of data
Amount, balance it is each it is different classes of in cell image quantity.
Step 5: the convolutional neural networks model trained in advance on ImageNet being modified, institute in reserving model
There is the parameter of convolutional layer, modify last full articulamentum, using cross entropy loss function, formula is expressed as follows shown
, (2)
Wherein,Indicate sample size,Expression parameter set,Indicate required loss function,Indicate sampleLabel,Indicate sampleHypothesis function.
Using stochastic gradient descent optimization algorithm, specific formula is expressed as follows:
, (3)
Wherein,Indicate required parameter,Indicate loss functionGradient,Indicate learning rate, neural network
Using dropout mode, for neural network unit, it is temporarily abandoned from network according to certain probability, it can be effective
Prevent over-fitting.Transfer learning mode is used simultaneously, and the various types of cells picture that above-mentioned steps are obtained inputs modified convolution
Neural network model is trained, and is stored to preferable depth convolutional neural networks model is finally fitted.
Step 6: in the convolutional neural networks model after any one source cervical cell picture not marked input is finely tuned,
It can correctly identify the position of positive cervical cell and be marked, next to the positive cervical cell region marked
Screening is compared according to location information, processing is filtered for the cell for having repetition to cover, guarantees source cervical cell picture
The accuracy of middle positive cervical cell positioning.
Step 7: the abnormal cell recognized for convolutional network model or abnormal cell group, for further processing.First
Determine whether for cell mass, it is then current without any processing if discrete cellular；Adaptive Thresholding is then used if cell mass
It processes, it is first determined then the position of nucleus in cell mass is carried out centered on nucleus using watershed algorithm slender
The segmentation of born of the same parents.It is unicellular according to what is obtained, the calculating of the parameters such as nucleus size, shape, color and karyoplasmic ratio is carried out, into one
It walks and determines cell generic.
Beneficial effects of the present invention:
The present invention has abandoned traditional feminine gender, positive two classification methods, combination cell pathology and computer technology relevant knowledge,
Cell after segmentation cell is done into more classification processings, has higher discrimination compared to two classification methods.Due to medical data sheet
Body is difficult to obtain, and profound neural network framework is trained to need a large amount of tape label data, so the present invention is using migration
Mode of learning, being one for the model modification after ImageNet is trained in advance can satisfy the polytypic network of cervical cell
Model, and network model according to available data can train the preferably more disaggregated models of degree of fitting according to this.More classification moulds
Type can directly identify the source cervical cell picture not marked, detect wherein positive cell and positioned.For volume
The abnormal cell group that product network detects, makees further dividing processing, and calculate relevant parameter and identified, is further promoted
The accuracy of testing result.The present invention has preferable auxiliaring effect in cervical cell pathological diagnosis field.
Detailed description of the invention
Fig. 1 is source cervical cell image labeling cutting procedure figure；
Fig. 2 is cell classification image, is always divided into 9 classes, is respectively: negative cervical cell mass, positive cervical cell group, negative single
A squamous cell, negative single basal cell, feminine gender overlapping squamous cell, feminine gender overlapping basal cell, neutrophil leucocyte are miscellaneous
Matter, positive cervical cell；
Fig. 3 is the training detection schematic diagram of depth convolutional neural networks model.
Specific embodiment
Below in conjunction with attached drawing, the present invention is further described.
As shown, the cervical cell automatic testing method based on depth convolutional neural networks, the specific implementation steps are as follows:
Step 1: as shown in Figure 1, carrying out artificial mark abnormal cervical cells to the source cervical cell image of acquisition, obtaining comprising different
The file of normal cell labeling position information.
Step 2: as shown in Figure 1, being split processing according to mark gained file and source cervical cell image；
1) cell mass is divided using threshold method first, is carried out pair according to the location information in the location information of segmentation and mark file
Than, judge whether to belong to abnormal cell, after be partitioned into the location information 299 ' 299 cervical cell group, Threshold segmentation formula
It is as follows:
, (4)
Wherein,Indicate the gray value of pixel in target image,Indicate the gray value of pixel in source images,Indicate taken threshold value.
2) discrete cellular is divided using Adaptive Thresholding, it is first determined the location information of nucleus, by cell nuclear location
Location information in information and mark file compares, and judges whether to belong to abnormal cell, is then divided centered on nucleus
Cut out the cell picture of 299 ' 299 sizes.
Step 3: the cell after segmentation being classified, cell mass mainly includes negative cervical cell mass and positive uterine neck
Cell mass two parts；Discrete cellular is broadly divided into negative single squamous cell, negative single basal cell, neutral grain, negative weight
Folded squamous cell, negative overlapping basal cell, impurity and positive cervical cell.Cell classification image is as shown in Figure 2.
Step 4: various types of cells picture obtained above being overturn, the processing such as contrast variation, expand the sample of data
Amount, balance it is each it is different classes of in cell image quantity.
Step 5: as shown in the pre-training process of Fig. 3, convolutional neural networks mould that will be trained in advance on ImageNet
Type is modified, and the parameter of all convolutional layers in reserving model modifies last full articulamentum, using cross entropy loss function,
Shown in formula is expressed as follows
, (5)
Wherein,Indicate sample size,Expression parameter set,Indicate required loss function,Indicate sampleLabel,Indicate sampleHypothesis function.
Using stochastic gradient descent optimization algorithm, specific formula is expressed as follows:
, (6)
Wherein,Indicate required parameter,Indicate loss functionGradient,Indicate learning rate, neural network
Using dropout mode, for neural network unit, it is temporarily abandoned from network according to certain probability, it can be effective
Prevent over-fitting.As shown in the training process of Fig. 3, while using transfer learning mode, all kinds of uterine neck that above-mentioned steps are obtained
Cell picture inputs modified convolutional neural networks model and is trained, and to being finally fitted preferable convolutional neural networks mould
Type is stored.
Step 6: as shown in Fig. 3 detection process, after any one source cervical cell picture input fine tuning to be detected
In convolutional neural networks model, it can correctly identify the position of abnormal cervical cells and be marked, next to label
Screening is compared according to location information in abnormal cervical cells region out, and the cell for having repetition to cover is filtered, and protects
The accuracy that abnormal cervical cells position in the cervical cell picture of card source.
Step 7: the abnormal cell recognized for convolutional network model or abnormal cell group, for further processing.First
Determine whether for cell mass, it is then current without any processing if discrete cellular；Adaptive Thresholding is then used if cell mass
It processes, it is first determined then the position of nucleus in cell mass is carried out centered on nucleus using watershed algorithm slender
The segmentation of born of the same parents.Unicellular, the calculating of the parameters such as progress nucleus size, shape, color and karyoplasmic ratio is obtained according to segmentation,
Further determine that cell generic.
Claims (6)
1. the abnormal cervical cells automatic testing method based on depth convolutional neural networks, it is characterised in that include the following steps:
Step 1: the source cervical cell image of acquisition is manually marked；
Step 2: source cervical cell image being split according to mark gained information；
Step 3: classifying to the cell after segmentation；
Step 4: various types of cells picture obtained above is pre-processed；
Step 5: using transfer learning mode, the pre-training model after the input change of treated cell is trained；
Step 6: a cervical cell picture input model in source to be detected can be correctly detected into out abnormal cell position；
Step 7: to abnormal cell, group makees further dividing processing, unicellular carry out nucleus size, the shape obtained to segmentation
The calculating of the parameters such as shape, color and karyoplasmic ratio further determines that cell generic.
2. the abnormal cervical cells automatic testing method according to claim 1 based on depth convolutional neural networks, special
Sign is: for source cervical cell picture, obtaining cell mass using Threshold segmentation, Adaptive Thresholding obtains discrete cellular, tool
Body formula is as follows:
, (1)
Wherein,Indicate the gray value of pixel in target image,Indicate the gray value of pixel in source images,Indicate taken threshold value.
3. the abnormal cervical cells automatic testing method according to claim 1 based on depth convolutional neural networks, special
Sign is: combination cell pathology and computer technology relevant knowledge do more classification processings, cell to the cell image after segmentation
Group is mainly cervical cell group, including negative and positive two parts；Discrete cellular be broadly divided into negative single squamous cell,
Negative single basal cell, neutral grain, negative overlapping squamous cell, negative overlapping basal cell, impurity and positive cervical cell.
4. the abnormal cervical cells automatic testing method according to claim 1 based on depth convolutional neural networks, special
Sign is: in the training process of convolutional neural networks, using dropout method, and although there is certain extension in the training time,
Effectively prevent over-fitting.
5. the abnormal cervical cells automatic testing method according to claim 1 based on depth convolutional neural networks, special
Sign is: by inputting any one unlabelled source cervical cell picture, which can correctly identify different
The band of position of normal cervical cell is simultaneously marked, and the cell compartment for having repetition to cover is filtered, and guarantees abnormal palace
The correct identification and accurate positionin of neck cell.
6. the abnormal cervical cells automatic testing method according to claim 1 based on depth convolutional neural networks, special
Sign is: further dividing processing is done, by joining to obtained unicellular do by the abnormal cervical cells group obtained for detection
Number calculates, and further improves the accuracy of testing result.
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