CN108564567A - A kind of ultrahigh resolution pathological image cancerous region method for visualizing - Google Patents
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Abstract
The present invention provides a kind of ultrahigh resolution pathological image cancerous region method for visualizing, first, the tissue regions of ultrahigh resolution pathological image to be measured are identified using threshold value optimal algorithm, and have overlappingly extraction slice from tissue regions, achieve the purpose that rejecting extraneous background reduces calculation amount and avoid introducing noise.Secondly, the deep neural network model for the slice of extraction being inputted persistence is predicted, will predict that the probability of every obtained slice constitutes probability matrix Mp.Then, it is based on probability matrix generating probability probe image H, trains a disaggregated model to carry out full figure prediction from H extraction features.Finally, it is based on full figure prediction result, probability probe image H is visualized as the expression of cancerous region probability and cancerous region profile is expressed.Cancerous region method for visualizing provided by the invention will greatly mitigate the work load of pathologist, improve accuracy rate of diagnosis for assisting pathologist to carry out analysis and diagnosis to pathological section.
Description
Technical field
The present invention relates to ultrahigh resolution pathological image cancerous region method for visualizing fields, more particularly, to one kind
Ultrahigh resolution pathological image cancerous region method for visualizing.
Background technology
Pathological basic medicine task is to obtain medical diagnosis on disease as a result, to instruct patient by studying pathological material
Treatment.Due to the high characteristic of the resolution ratio of the whole slide image of complexity and pathology number of pathology itself, disease
Reason doctor is taken time and effort to the analysis work of pathological image and accuracy rate is low.Existing Applied Computer Techniques assist pathological image
The related work achievement of medical diagnosis mostly concentrates on the non-super high-resolution medical image analysis of small size, and faces superelevation
The diagnosis of resolution ratio pathological image and analysis task, often computation complexity is high, and diagnostic result can not be expressed intuitively, fails from this
Mitigate pathologist work load in matter.The present invention is using segmentation thought, greatly using the slice extracting method based on piecemeal
Reduce computation complexity；Probability probe image is built using the mapping method of slice probability to probability matrix, and is based on probability
Probe image carries out full figure prediction using disaggregated model, improves canceration judging nicety rate；Further, the visualization side of use
Method obtains the expression of cancerous region probability and the positioning of cancer area is realized in the expression of cancerous region profile, and cancerous issue is enable intuitively to show,
The diagnosis efficiency for greatly improving pathologist alleviates the work load of pathologist.
Invention content
The present invention provides a kind of ultrahigh resolution pathological image cancerous region method for visualizing, and this method can be realized to superelevation
Resolution ratio pathological image carries out the positioning of cancer area and visualization.
In order to reach above-mentioned technique effect, technical scheme is as follows：
A kind of ultrahigh resolution pathological image cancerous region method for visualizing, includes the following steps：
S1：The slice extraction of interest region：Described image is divided by group using the threshold value optimal algorithm based on pixel RGB values
Tissue region and extraneous background region are split tissue regions and extract slice；
S2：Model prediction and probability matrix structure：To biography before the small slice input convolutional neural networks model is carried out
The canceration probability for obtaining every small slice is broadcast, gained probability, which is pressed default update method, builds probability matrix；
S3：Full figure is predicted：Full figure prediction is carried out to ultrahigh resolution pathological image based on probability probe image, is divided into sun
Property and negative two classes；
S4：Prediction result visualizes：Based on full figure prediction result, probability probe image is mapped to obtain cancerous region probability
Expression and the expression of cancerous region profile.
Further, the detailed process of the step S1 is：
Optimal threshold is obtained using the threshold value optimal algorithm of pixel RGB values, based on optimal threshold to ultrahigh resolution pathology
Image is detected, and is identified tissue regions and is drawn tissue regions profile boundary rectangle；It is starting with the upper left corner of boundary rectangle
Coordinate has the slice that overlappingly interception resolution ratio is 256*256 to preserve to input rank.
Further, in the step S2, if temperature figure resolution ratio corresponds on sample medical image pyramid
Level-K, structure Level-K correspond to the complete zero initial matrix Mp of two dimension of dimension, will slice input deep neural network model into
Row propagated forward obtains corresponding prediction probability, and the centre coordinate which is mapped to Level-K with corresponding slice is to index more
The respective element of new matrix Mp.
Further, in the step S3, a disaggregated model is trained from training set probability probe image extraction feature, from
The feature input disaggregated model for the probability probe image extraction same type that test set generates is predicted, by ultrahigh resolution disease
Reason image is divided into positive and negative two classes.
Further, in the step S4, probability probe image H is subjected to transparency process with threshold value A lpha, then with it is right
The pathological image of the Level-K answered is overlapped processing and generates new images, obtains the expression of cancerous region probability；By probability probe figure
Contour detecting is carried out after carrying out binaryzation and corrosion and filling as H, profile coordinate information is mapped to corresponding Level-K's
The expression of cancerous region profile is obtained after pathological image.
Further, the slice for having overlapping is intercepted and is preserved, including sliding window interception and tissue regions judge：It utilizes
The resolution information of sample to be tested image, sliding window, which adaptively has with corresponding step-length, overlappingly intercepts test sample；To being intercepted
Optimal pixel RGB threshold value of the slice based on gained, judge biopsy tissues region accounting whether be more than 30%, if so, should
Slice, which preserves and scans next slice, to be judged；If it is not, then directly scanning next slice repeats above-mentioned judgment step.
Further, the identification tissue regions and tissue regions profile boundary rectangle is drawn, process of realizing is based on point
Block method：If ultrahigh resolution pathological image is equably divided into stem portion, tissue area is identified to these parts of images respectively
Domain simultaneously draws tissue regions profile boundary rectangle.
Further, the probability probe image generated from test set, is generated based on probability matrix, with probability matrix
Each element in Mp is that two-dimensional matrix Mp is extended for three-dimensional matrice Mp1 by expansion factor, is based on Mp1 generating probability probe images
H, wherein color are deeper, show that the region suffers from that cancer probability is higher, and color is more shallow, and it is lower to show that cancer probability is suffered from the region.
Compared with prior art, the advantageous effect of technical solution of the present invention is：
1, the interest region slice extraction scheme based on piecemeal that the present invention uses, if pathological image is divided into cadre
Point, respectively to various pieces identification interest extracted region slice, traditional slice extracting method is compared, the present invention is to non-region of interest
Domain has carried out more effective rejecting, improves the operation efficiency of entire positioning and method for visualizing；
2, the slice that the present invention uses arrives the mapping method of probability matrix, by coordinate mapping relations, during prediction
Probability matrix is automatically updated, realizes the one-to-one correspondence of position and probability between pathological image corresponding region and canceration probability；
3, the slave probability probe image extraction feature that the present invention uses trains the scheme of a grader progress full figure prediction,
Compared to the existing classification based on small slice, pathological image is effectively divided into positive and feminine gender by the present invention, improves prediction
Accuracy rate；
4, cancerous region method for visualizing provided by the invention expresses prediction result visual representation for cancerous region probability
It is expressed with cancerous region profile, for assisting pathologist to carry out analysis and diagnosis to pathological section, dramatically reduces pathology
The work load of doctor improves accuracy rate of diagnosis.
Description of the drawings
Fig. 1 is the method for the present invention flow chart；
Fig. 2 is to be improved to the slice extracting method schematic diagram based on piecemeal from simplicity slice extracting method；
The areas Tu3Wei Ai position and method for visualizing schematic diagram.
Specific implementation mode
The attached figures are only used for illustrative purposes and cannot be understood as limitating the patent；
In order to more preferably illustrate that the present embodiment, the certain components of attached drawing have omission, zoom in or out, actual product is not represented
Size；
To those skilled in the art, it is to be appreciated that certain known features and its explanation, which may be omitted, in attached drawing
's.
The following further describes the technical solution of the present invention with reference to the accompanying drawings and examples.
Embodiment 1
As shown in 1-3, a kind of ultrahigh resolution pathological image cancerous region method for visualizing includes the following steps：
S1：The slice extraction of interest region, group is divided into using the threshold value optimal algorithm based on pixel RGB values by described image
Tissue region and extraneous background region are split tissue regions and extract slice.
S2：Model prediction and probability matrix structure, to biography before the small slice input convolutional neural networks model is carried out
The canceration probability for obtaining every small slice is broadcast, all probability are constituted into probability matrix.
S3：Full figure is predicted, is carried out full figure prediction to ultrahigh resolution pathological image based on probability probe image, is divided into sun
Property and negative two classes.
S4：Prediction result visualizes, and is based on full figure prediction result, probability probe image is mapped to obtain cancerous region probability
Expression and the expression of cancerous region profile.
Above-mentioned steps specific implementation is as follows：
S1：The slice extraction of interest region
The slice extraction of interest region includes the following steps：
(1) full figure is scanned using the threshold value optimal algorithm based on pixel RGB values, 2 groups of optimal RGB threshold values is calculated, point
Not Wei RGB bottom thresholds and RGB upper thresholds, all pixels in the threshold interval will all be determined as tissue regions；
(2) image averaging is divided into 4 parts, quadrant where recording various pieces respectively simultaneously calculates the part and original image
Pixel coordinate mapping relations, be based respectively on optimal threshold and each section image be divided into interest region and background area, and point
Interest region contour boundary rectangle is not drawn；
(3) according to the resolution information of sample to be tested image, size is the sliding window of 256*256 pixels with boundary rectangle
The upper left corner be origin coordinates, adaptively slided with corresponding step-length, sweep test sample image, be based on optimal RGB thresholds
Value judges whether sliding window inner tissue region accounting is more than 30%, if so, by image in window and the window center root
The coordinate for mapping to artwork according to quadrant mapping relations is preserved as a Patch to input rank；If it is not, then scanning next window
Mouthful.
S2：Model prediction and probability matrix structure
Pathological image resolution ratio to be measured is 20480*27904, the corresponding resolution ratio of working level Level-5, Level-5
For 640*872.
Model prediction and probability matrix structure comprise the steps of：
(1) the two-dimentional full null matrix Mp that structure dimension is 640*872；
(2) using 16 Patch of input rank as a Batch, each Batch is standardized,
Input persistence deep neural network model carry out propagated forward, obtain every Patch canceration probability, by the probability with
The respective element that the centre coordinate that corresponding slice maps to Level-5 is index upgrade matrix Mp.
S3：Full figure is predicted
From training set probability probe image extract feature include：Cancer area accounting, cancer area longest axis, canceration probability are more than
0.9 pixel number, cancer area Maximum Area, mean value, variance, degree of skewness and kurtosis, cancer area perimeter maximum value, mean value, variance, partially
Gradient and kurtosis, cancer area profile eccentricity maximum value, mean value, variance, degree of skewness and kurtosis, cancer area profile ductility maximum value,
Mean value, variance, degree of skewness and kurtosis, at the same in cancerous region and its minimal convex polygon the maximum value of pixel ratio, mean value,
Variance, degree of skewness and kurtosis.By these features for training a support vector machines, and the persistence model is for predicting.
The feature that same type is extracted from probability probe image H inputs the support vector cassification model progress of the persistence
Ultrahigh resolution pathological image is divided into positive and negative two classes by prediction.
S4：Prediction result visualizes
Full figure prediction result is the positive, and visualization includes following two steps：
(1) probability probe image H is subjected to transparency process with transparency threshold Alpha=0.7, then with it is corresponding
The pathological image superposition of Level-5 generates new images, obtains the expression of cancerous region probability；
(2) probability probe image H is converted into gray-scale map H1, binary image H2 is generated based on gray-scale map H1, to H2 into
Contour detecting is carried out after row corrosion and filling processing, profile coordinate information is mapped into corresponding Level-5 with outline form
Pathological image obtain cancerous region profile expression.
The same or similar label correspond to the same or similar components；
Position relationship described in attached drawing is used to only for illustration, should not be understood as the limitation to this patent；
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
All any modification, equivalent and improvement etc., should be included in the claims in the present invention made by within the spirit and principle of invention
Protection domain within.
Claims (8)
1. a kind of ultrahigh resolution pathological image cancerous region method for visualizing, which is characterized in that include the following steps：
S1：The slice extraction of interest region：Described image is divided by tissue area using the threshold value optimal algorithm based on pixel RGB values
Domain and extraneous background region are split tissue regions and extract slice；
S2：Model prediction and probability matrix structure：The small slice input convolutional neural networks model is carried out propagated forward to obtain
Gained probability is pressed default update method and builds probability matrix by the canceration probability for obtaining every small slice；
S3：Full figure is predicted：Full figure prediction is carried out to ultrahigh resolution pathological image based on probability probe image, be divided into it is positive and
Negative two classes；
S4：Prediction result visualizes：Based on full figure prediction result, probability probe image is mapped to obtain the expression of cancerous region probability
And cancerous region profile expression.
2. ultrahigh resolution pathological image cancerous region method for visualizing according to claim 1, which is characterized in that described
The detailed process of step S1 is：
Optimal threshold is obtained using the threshold value optimal algorithm of pixel RGB values, based on optimal threshold to ultrahigh resolution pathological image
It is detected, identifies tissue regions and draws tissue regions profile boundary rectangle；Using the upper left corner of boundary rectangle as origin coordinates,
There is the slice that overlappingly interception resolution ratio is 256*256 to preserve to input rank.
3. ultrahigh resolution pathological image cancerous region method for visualizing according to claim 2, which is characterized in that described
In step S2, if temperature figure resolution ratio corresponds to the Level-K on sample medical image pyramid, structure Level-K corresponds to dimension
It is general to be carried out the corresponding prediction of propagated forward acquisition by the complete zero initial matrix Mp of two dimension of degree for slice input deep neural network model
The probability is mapped to the centre coordinate of Level-K as the respective element of index upgrade matrix Mp by rate using corresponding slice.
4. ultrahigh resolution pathological image cancerous region method for visualizing according to claim 3, which is characterized in that described
In step S3, a disaggregated model is trained from training set probability probe image extraction feature, the probability probe generated from test set
The feature input disaggregated model of image zooming-out same type is predicted, ultrahigh resolution pathological image is divided into positive and cloudy
Two classes of property.
5. ultrahigh resolution pathological image cancerous region method for visualizing according to claim 4, which is characterized in that described
In step S4, probability probe image H is subjected to transparency process with threshold value A lpha, then with the pathological image of corresponding Level-K
It is overlapped processing and generates new images, obtain the expression of cancerous region probability；Probability probe image H is subjected to binaryzation and corrosion
With contour detecting is carried out after filling, obtain canceration area after profile coordinate information to be mapped to the pathological image of corresponding Level-K
Domain profile expression.
6. ultrahigh resolution pathological image cancerous region method for visualizing according to claim 5, which is characterized in that described
The slice for having overlapping intercept and preserve, including sliding window interception and tissue regions judge：Utilize the resolution ratio of sample to be tested image
Information, sliding window, which adaptively has with corresponding step-length, overlappingly intercepts test sample；To the slice that is intercepted based on the optimal of gained
Pixel RGB threshold values, judge whether biopsy tissues region accounting is more than 30%, if so, the slice is preserved and scans next
Slice is judged；If it is not, then directly scanning next slice repeats above-mentioned judgment step.
7. ultrahigh resolution pathological image cancerous region method for visualizing according to claim 6, which is characterized in that described
Identification tissue regions and draw tissue regions profile boundary rectangle, realize process be based on method of partition：By ultrahigh resolution disease
If managing image uniform Ground Split into stem portion, tissue regions are identified to these parts of images respectively and are drawn outside tissue regions profile
Connect rectangle.
8. ultrahigh resolution pathological image cancerous region method for visualizing according to claim 7, which is characterized in that described
Slave test set generate probability probe image, based on probability matrix generate, with each element in probability matrix Mp be expand
Two-dimensional matrix Mp is extended for three-dimensional matrice Mp1 by the factor, is based on Mp1 generating probability probe image H, wherein color is deeper, shows
The region suffers from that cancer probability is higher, and color is more shallow, and it is lower to show that cancer probability is suffered from the region.
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| CN110232410A (en) * | 2019-05-30 | 2019-09-13 | 清华大学 | The organ-tissue Pathologic image analysis method and analytical equipment of multi-layer |
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