CN109285157A - Myocardium of left ventricle dividing method, device and computer readable storage medium - Google Patents
Myocardium of left ventricle dividing method, device and computer readable storage medium Download PDFInfo
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
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- G06F18/24—Classification techniques
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
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- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10132—Ultrasound image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30048—Heart; Cardiac
Abstract
A kind of myocardium of left ventricle dividing method, device and computer readable storage medium, wherein the myocardium of left ventricle dividing method includes: acquisition cardiac image；For the cardiac image, iteration executes the extraction of n times down-sampling process of convolution and fisrt feature information；If completing the iterative process that n times extract fisrt feature information, based on the fisrt feature information that n-th is extracted, the extraction of n times up-sampling process of convolution and second feature information is executed for the output object iteration of n-th down-sampling process of convolution；If completing the iterative process that n times extract second feature information, the automatic segmentation of myocardium of left ventricle is carried out to cardiac image based on preparatory trained classifier.Technical solution provided by the present application can effectively improve the efficiency of myocardium of left ventricle segmentation.
Description
Technical field
This application involves fields of biomedicine more particularly to a kind of myocardium of left ventricle dividing method, device and computer can
Read storage medium.
Background technique
Myocardium of left ventricle segmentation is the key step of Analysis of Cardiac Functions, the heart functions such as myocardium of left ventricle wall thickness, ejection amount point
Analysis parameter is based on the accurate segmentation of myocardium of left ventricle.Myocardium of left ventricle is similar with background gray scale, papillary muscle, girder in left ventricle
Belong to same substance Deng with cardiac muscle, there is also artifacts.Therefore, myocardium of left ventricle segmentation is a difficult and important task.
Currently, the method for myocardium of left ventricle segmentation mainly uses magnetic resonance imaging (Magnetic Resonance
Imaging, MRI) technology acquisition cardiac image, it is manual to cardiac image by the medical worker or medical expert that pass through Special Training
Carry out the dividing processing of myocardium of left ventricle.Segmentation is very high to expertise and skill requirement by hand, and time-consuming.
Summary of the invention
The application provides a kind of myocardium of left ventricle dividing method, device and computer readable storage medium, can be used to improve
The efficiency of myocardium of left ventricle segmentation.
The application first aspect provides a kind of myocardium of left ventricle dividing method, comprising:
Obtain cardiac image；
Down-sampling process of convolution is carried out to the cardiac image；
Extract fisrt feature information, wherein the fisrt feature information is the output of the last down-sampling process of convolution
The characteristic information of object；
If the iterative process that n times extract fisrt feature information is not completed, based on the fisrt feature information pair currently extracted
The output object of the last down-sampling process of convolution carries out down-sampling process of convolution, and it is special to execute the extraction first for iteration later
The step of reference ceases；
It is right based on the fisrt feature information that n-th is extracted if completing the iterative process that n times extract fisrt feature information
The output object of n-th down-sampling process of convolution carries out up-sampling process of convolution；
Extract second feature information, wherein the second feature information is the output of the last up-sampling process of convolution
The characteristic information of object；
If the iterative process that n times extract second feature information is not completed, based on the second feature information currently extracted
Up-sampling process of convolution is carried out to the output object of the last up-sampling process of convolution, iteration executes the extraction second later
The step of characteristic information；
If completing the iterative process that n times extract second feature information, mentioned based on preparatory trained classifier and n-th
The second feature information taken carries out the segmentation of myocardium of left ventricle to the output object of the last up-sampling process of convolution；
Wherein, the N is not less than 2.
The application second aspect provides a kind of myocardium of left ventricle segmenting device, comprising:
Acquiring unit, fisrt feature extraction unit, down-sampling convolution processing unit, second feature extraction unit, up-sampling
Convolution processing unit and cutting unit；
The acquiring unit is used for: obtaining cardiac image；
The down-sampling convolution processing unit is used for: to described in triggering after cardiac image progress down-sampling process of convolution
Fisrt feature extraction unit；When not completing the iterative process of n times extraction fisrt feature information, it is based on presently described fisrt feature
The fisrt feature information that extraction unit extracts adopt to the output object of described down-sampling convolution processing unit the last time
Sample process of convolution triggers the fisrt feature extraction unit later；
The fisrt feature extraction unit is used for: extracting fisrt feature information, wherein the fisrt feature information is nearest
The characteristic information of the output object of down-sampling process of convolution；
The up-sampling convolution processing unit is used for: when completing the iterative process of n times extraction fisrt feature information, being based on
The fisrt feature information that the fisrt feature extraction unit n-th is extracted exports the down-sampling convolution processing unit n-th
Object carry out up-sampling process of convolution, trigger the second feature extraction unit later；Second feature is extracted in unfinished n times
When the iterative process of information, the second feature information currently extracted based on the second feature extraction unit is to the up-sampling
Convolution processing unit the last time object of output carries out up-sampling process of convolution, triggers the second feature later and extracts list
Member；
The second feature extraction unit is used for: extracting second feature information, wherein the second feature information is described
Up-sample the characteristic information of the last output object of convolution processing unit；
Cutting unit is used for: when completing the iterative process of n times extraction second feature information, based on trained point in advance
The second feature information that class device and n-th are extracted carries out the left ventricle heart to the output object of the last up-sampling process of convolution
The segmentation of flesh；
Wherein, the N is not less than 2.
The application third aspect provides a kind of myocardium of left ventricle segmenting device, comprising: memory, processor and is stored in institute
The computer program that can be run on memory and on the processor is stated, the processor executes real when the computer program
The myocardium of left ventricle dividing method that existing above-mentioned the application first aspect provides.
The application fourth aspect provides a kind of computer readable storage medium, is stored thereon with computer program, the meter
When calculation machine program is executed by processor, the myocardium of left ventricle dividing method that above-mentioned the application first aspect provides is realized.
Therefore, on the one hand, the characteristic information (such as second feature information) that application scheme passes through extraction cardiac image
And input preparatory trained classifier and identified, the automatic segmentation to myocardium of left ventricle in cardiac image is realized with this, by
Then pass through the segmentation that the dirty image of machine automatic centering carries out myocardium of left ventricle, accordingly, with respect to it is traditional by medical worker or
The method of medical expert's manual segmentation, application scheme can effectively improve the efficiency of myocardium of left ventricle segmentation；On the other hand, by
In application scheme input classifier second feature information be by multiple feature extraction, down-sampling process of convolution and on
Sampling process of convolution obtains, and therefore, second feature information can preferably characterize the feature of deeper in cardiac image, to make
The result for obtaining myocardium of left ventricle segmentation is more accurate.
Detailed description of the invention
Fig. 1-a is myocardium of left ventricle dividing method one embodiment flow diagram provided by the present application；
Fig. 1-b is a kind of Dense schematic network structure provided by the present application；
Fig. 2 is under a kind of application scenarios provided by the present application to realize that the network structure of myocardium of left ventricle dividing method is shown
It is intended to；
Fig. 3 is under application scenarios shown in Fig. 2, to obtaining after wherein a cardiac image is split for test patient
Segmentation result schematic diagram；
Fig. 4 is the segmentation result of Fig. 3 and the contrast schematic diagram of heart labeled data；
Fig. 5 is the segmentation result of Fig. 3 and the linear analysis schematic diagram of heart labeled data；
Fig. 6 is myocardium of left ventricle segmenting device one embodiment structural schematic diagram provided by the present application；
Fig. 7 is another example structure schematic diagram of myocardium of left ventricle segmenting device provided by the present application.
Specific embodiment
To enable present invention purpose, feature, advantage more obvious and understandable, below in conjunction with the application
Attached drawing in embodiment, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described reality
Applying example is only some embodiments of the present application, and not all embodiments.Based on the embodiment in the application, those skilled in the art
Member's every other embodiment obtained without making creative work, shall fall in the protection scope of this application.
As shown in Fig. 1-a, a kind of myocardium of left ventricle dividing method includes: in the embodiment of the present application
Step 101 obtains cardiac image；
In a kind of application scenarios, step 101, which can show themselves in that, passes through magnetic resonance imaging
(MagneticResonanceImaging, MRI) technology obtains cardiac image, and the cardiac image got at this time is MRI figure
Picture.Be illustrated below to MRI technique: MRI technique is a kind of technology that body structures' image is obtained by magnetic field, tool
There is the advantages of hurtless measure, therefore patient can be by good protection when being checked.In the present embodiment, can pass through
The cardiac image of MRI technique acquisition human body.
In another application scenarios, cardiac image can also be obtained by ultrasonic diagnostic mode (such as B ultrasound).Alternatively,
(such as importing) cardiac image to be split can also be obtained from existing cardiac image data library, herein without limitation.
Step 102 carries out down-sampling process of convolution to above-mentioned cardiac image；
In a step 102, to above-mentioned cardiac image (primitive cardiac image or the cardiac image after normalized) into
Row down-sampling process of convolution.
Specifically, step 102 includes: the characteristic information extracted in above-mentioned cardiac image, the characteristic information pair based on extraction
Above-mentioned cardiac image carries out down-sampling process of convolution.
Optionally, the characteristic information in above-mentioned cardiac image is extracted by a process of convolution, which is applied
Formula can indicate are as follows:
Wherein, i, j are the location of pixels of image, and I, K respectively indicate image and convolution kernel, m, and n is respectively the width of convolution kernel
With height.
Alternatively, the spy in above-mentioned cardiac image can also be extracted based on Dense network or other image characteristics extraction algorithms
Reference breath, herein without limitation.
Since the format of cardiac image that gets may not all the same (such as the cardiod diagram got based on MRI technique
As there are two types of different-formats: 176*132,132*176), in order to facilitate the training of network, the parameter of redundancy is reduced, can obtained
After getting cardiac image, the normalized of size is carried out to the cardiac image got, so that the heart after normalized
The size of image is uniform sizes.Optionally, step 101 is later and step 102 can also include: before to the heart got
Image carries out the normalized of picture size, obtains the cardiac image of pre-set dimension.Then step 102 can specific manifestation are as follows: right
The cardiac image of above-mentioned pre-set dimension carries out down-sampling process of convolution.
Specifically, pre-set dimension for example can be set to 128*128.It is of course also possible to size is preset as other sizes, this
Place is without limitation.
Step 103 extracts fisrt feature information；
Wherein, above-mentioned fisrt feature information is the characteristic information of the output object of the last down-sampling process of convolution.
In the embodiment of the present application, the output of the last down-sampling process of convolution can be extracted based on image feature extraction techniques
Characteristic information (i.e. fisrt feature information) in object.
Optionally, fisrt feature information is extracted based on Dense network in the embodiment of the present application.Specifically, the Dense network
Structural schematic diagram can as shown in Fig. 1-b, Dense network include convolutional layer and Elu nonlinear activation function, work as input object
After the processing of convolutional layer and Elu nonlinear function (processing shows as " convolution+Elu " in Fig. 1-b), obtained output is needed
It to be superimposed with input, i.e., each layer of input, can be with table if this process function representation from the output of all layers of front
It is shown as: xl=Hl([x0,x1,...,xl-1]).Wherein, [x0,x1,...,xl-1] indicate 0 to l-1 layers superposition exported, HlIt indicates
One nonlinear transformation.By using Dense network, the transmitting of feature is strengthened, characteristic information can more efficiently be utilized,
Gradient disappearance is alleviated, and reduces number of parameters to a certain extent.It is identical that Dense network inputs export picture size.This
Dense network has selected Elu nonlinear activation function, and the positive value characteristic of Elu nonlinear activation function alleviates gradient disappearance and asks
Topic, compared to more traditional relu activation primitive, negative value reduces computation complexity, meets the requirement of zero averaging, reduces meter
Calculate deviation.
Certainly, in step 103, fisrt feature can also be extracted based on other neural networks or image characteristics extraction algorithm
Information, herein without limitation.
If step 104 does not complete the iterative process that n times extract fisrt feature information, special based on first currently extracted
Reference, which is ceased, carries out down-sampling process of convolution to the output object of the last down-sampling process of convolution；
At step 104, the fisrt feature information based on step 103 extraction is to the defeated of the last down-sampling process of convolution
Object carries out down-sampling process of convolution out, by the resolution ratio of downscaled images, to extract in image in the next steps
The characteristic information of deeper.
At step 104, can extract by the output object of the last down-sampling process of convolution and currently first is special
It levies information input down-sampling layer (can be regarded as pond layer) and carries out down-sampling process of convolution, the output of the down-sampling layer is adopted under being
The output object of sample process of convolution.
In the case where being carried out based on output object of the fisrt feature information currently extracted to the last down-sampling process of convolution
After sampling process of convolution, return step 103 executes step 103 with iteration.By the iterative process, the heart can be gradually extracted
The characteristic information of dirty image mid-deep strata.
Wherein, above-mentioned N is the preset value not less than 2.Optionally, N takes 4.
If step 105 completes the iterative process that n times extract fisrt feature information, the fisrt feature extracted based on n-th
Information carries out up-sampling process of convolution to the output object of the last down-sampling process of convolution；
Since in the iterative process for extracting fisrt feature information, image is compressed after down-sampling process of convolution, therefore,
In the embodiment of the present application, after the iterative process for completing n times extraction fisrt feature information, start to go back compressed image
The process of original, this reduction can be regarded as the process reverse operating process of aforementioned compression.
Specifically, when completing the iterative process of n times extraction fisrt feature information, the fisrt feature based on n-th extraction
Information carries out up-sampling process of convolution to the output object of the last (namely n-th) down-sampling process of convolution, so as to gradually
The also resolution ratio of original image.
In step 105, the output object and n-th of the last down-sampling process of convolution can be extracted first is special
Sign information input up-sampling layer carries out up-sampling process of convolution, and the output of the up-sampling layer is when time up-sampling process of convolution
Export object.
Step 106 extracts second feature information；
Wherein, above-mentioned second feature information is the characteristic information of the output object of the last up-sampling process of convolution.
Optionally, second feature information is extracted based on Dense network in the embodiment of the present application.Specifically, about the Dense
The description of network is referred to the description in step 103, and details are not described herein again.
Certainly, in step 106, second feature can also be extracted based on other neural networks or image characteristics extraction algorithm
Information, herein without limitation.
If step 107 does not complete the iterative process that n times extract second feature information, based on second currently extracted
Characteristic information carries out up-sampling process of convolution to the output object of the last up-sampling process of convolution, later return step 106；
In the embodiment of the present application, when the iterative process for not completing n times extraction second feature information (is abbreviated as not in Fig. 1-a
Complete iterative process) when, show that current compressed cardiac image also needs to continue to restore, executes step 107 at this time.By this
Iterative process can gradually restore cardiac image.
In step 107, the second feature information currently extracted and the last time can be up-sampled into process of convolution
Output object input up-sampling layer carries out up-sampling process of convolution, and the output of the up-sampling layer is when time up-sampling process of convolution
Output object.
If step 108 completes the iterative process that n times extract second feature information, based on preparatory trained classifier
The second feature information extracted with n-th carries out myocardium of left ventricle to the output object of the last up-sampling process of convolution
Segmentation；
When the iterative process for completing n times extraction second feature information, show currently to mention for the feature of above-mentioned cardiac image
Process is taken to complete, the output pair of the second feature information and the last up-sampling process of convolution of at this time extracting n-th
As trained classifier (such as softmax classifier) carries out myocardium of left ventricle in advance for (cardiac image after restoring) input
Segmentation, that is, isolate the myocardium of left ventricle and background information in the output object.Specifically, extracted based on n-th second
The output object and above-mentioned classifier of characteristic information, the last up-sampling process of convolution, can will be each in the output object
A pixel classifications are foreground information (such as myocardium of left ventricle) or background information, to realize the left ventricle heart in cardiac image
Flesh and background separation.
It should be noted that for (being referred to as below in the embodiment of the present application to divide the network of myocardium of left ventricle automatically
It can be for segmentation network, such as the segmentation network by the aforementioned Dense network referred to, down-sampling layer, up-sampling layer and classification
Device etc.), it can be obtained by way of training in advance.It in practical applications, can be multiple to above-mentioned point of training by obtaining
The cardiac image for cutting network is trained above-mentioned segmentation network, and can optimize the segmentation network based on Adam optimization algorithm.
It is realized specifically, being referred to prior art based on the process that Adam optimization algorithm optimizes the segmentation network, herein not
It repeats again.Further, Dice coefficient also can be used to assess the accuracy of above-mentioned segmentation network, formula can behave as following public affairs
Formula:
Wherein, D indicates that Dice coefficient, this coefficient measure segmentation result, p by comparing the similarity of divided areaiGeneration
Table segmentation result, giRepresent the segmentation result of mark.Dice coefficient is in section 0~1, is worth the bigger essence for indicating segmentation result
Exactness is higher.
It is previously noted that in step 103 and step 106 fisrt feature information and second can be extracted based on Dense network
Characteristic information.Under this application scenarios, following constraint condition can be set: 1, in the iteration mistake of said extracted fisrt feature information
Cheng Zhong, (n+1)th time the convolution kernel number for extracting Dense network used in fisrt feature information is that n-th extracts fisrt feature
One times of the convolution kernel number of Dense network used in information；And in the iterative process of said extracted second feature information
In, (n+1)th time the convolution kernel number for extracting Dense network used in second feature information is that n-th extracts second feature letter
The half of the convolution kernel number of Dense network used in ceasing；And n-th is extracted used in fisrt feature information
The convolution kernel number of Dense network is equal to the 1st convolution kernel number for extracting Dense network used in second feature information,
Wherein, n ∈ [1, N).
Therefore, on the one hand, by extracting the characteristic information of cardiac image, (such as second feature is believed in the embodiment of the present application
Breath) and input in advance trained classifier identified, the automatic segmentation to myocardium of left ventricle in cardiac image is realized with this,
The segmentation that myocardium of left ventricle is carried out by then passing through the dirty image of machine automatic centering, accordingly, with respect to traditional by medical worker
Or the method for medical expert's manual segmentation, application scheme can effectively improve the efficiency of myocardium of left ventricle segmentation；On the other hand,
Due in application scheme input classifier second feature information be by multiple feature extraction, down-sampling process of convolution and
Up-sampling process of convolution obtains, and therefore, second feature information can preferably characterize the feature of deeper in cardiac image, thus
So that the result of myocardium of left ventricle segmentation is more accurate.
To facilitate a better understanding of the myocardium of left ventricle dividing method in Fig. 1-a illustrated embodiment, specifically answered with one below
Above-mentioned myocardium of left ventricle dividing method is described with scene.Schematic network structure in this application scene can be such as Fig. 2
It is shown, from Figure 2 it can be seen that the segmentation network in this application scene includes that compression extraction feature and decompressing image restore two portions
Point, two parts are full symmetric, to guarantee that the image after Image Segmentation Methods Based on Features and original image are in the same size.Cardiac image is through Dense
Input compression extraction feature part carries out after network (structure and associated description that are referred to Dense network in Fig. 1-b) processing
Processing.
From Figure 2 it can be seen that compression extraction feature part and decompressing image recovered part include four sections of processing, for compression
Extraction feature part, each section (is referred in Fig. 1-b the structure of Dense network and related by down-sampling layer and Dense network
Description) composition, gradually to extract the characteristic information of image deeper.Similarly, equally include for decompressing image recovered part
Four sections of processing (i.e. aforementioned N takes 4), each section is made of up-sampling layer and Dense network, gradually to go back original image.
Optionally, it compresses in extraction feature part, every section of convolution kernel size for handling used Dense network is respectively
5*5,5*5,5*5 and 5*5, every section of convolution kernel number for handling used Dense network is respectively 32,64,128,256, pressure
The picture size for inputting Dense network in contracting extraction feature part for the first time is 128*128, and compresses the output of extraction feature part
Image size is 8*8.
Correspondingly, every section of convolution kernel size for handling used Dense network is distinguished in decompressing image recovered part
For 5*5,5*5,5*5 and 5*5；Every section of convolution kernel number for handling used Dense network is respectively 256,128,64,32,
The picture size for inputting Dense network for the first time is 128*128, and the image size for compressing the output of extraction feature part is 8*8.Solution
The picture size for inputting Dense network in compression image recovered part for the first time is 8*8, and compresses the figure of extraction feature part output
As size is 128*128.
After decompressing image recovered part has been handled, by softmax points of the output input of decompressing image recovered part
Class device, by softmax classifier to cardiac image carry out myocardium of left ventricle segmentation, that is, realize to myocardium of left ventricle in image with
The separation (i.e. output segmentation result) of background.
Fig. 3 is under application scenarios shown in Fig. 2, to obtaining after wherein a cardiac image is split for test patient
Segmentation result schematic diagram, the automatic segmentation of myocardium of left ventricle it can be seen that, can be realized by application scheme by Fig. 3.
Fig. 4 is the segmentation result of Fig. 3 and the contrast schematic diagram of heart labeled data, and Fig. 5 is the segmentation result and heart of Fig. 3
The linear analysis schematic diagram of labeled data can be seen that in conjunction with Fig. 4 and Fig. 5 and divide the left ventricle obtained based on application scheme
There are correlations with heart labeled data for myocardium meat.
Fig. 6 provides a kind of myocardium of left ventricle segmenting device for the embodiment of the present application.As shown in fig. 6, the myocardium of left ventricle point
It cuts device and specifically includes that acquiring unit 301, fisrt feature extraction unit 302, down-sampling convolution processing unit 303, second feature
Extraction unit 304, up-sampling convolution processing unit 305 and cutting unit 306.
Acquiring unit 301 is used for: obtaining cardiac image；
Down-sampling convolution processing unit 303 is used for: carrying out down-sampling convolution to the cardiac image that acquiring unit 301 is got
Fisrt feature extraction unit 302 is triggered after processing；When not completing n times and extracting the iterative process of fisrt feature information, based on working as
The fisrt feature information that preceding fisrt feature extraction unit 302 the extracts output the last to down-sampling convolution processing unit 303
Object carries out down-sampling process of convolution, triggers fisrt feature extraction unit 302 later；
Fisrt feature extraction unit 302 is used for: extracting fisrt feature information, wherein above-mentioned fisrt feature information is nearest
The characteristic information of the output object of down-sampling process of convolution；
Up-sampling convolution processing unit 305 is used for: when completing n times and extracting the iterative process of fisrt feature information, based on the
The fisrt feature information that one feature extraction unit, 302 n-th is extracted, to pair of 303 n-th of down-sampling convolution processing unit output
As current input object carries out up-sampling process of convolution, second feature extraction unit 304 is triggered later；It is extracted in unfinished n times
When the iterative process of second feature information, the second feature information currently extracted based on second feature extraction unit 304 is to upper
The object of the last output of sampling convolution processing unit 305 carries out up-sampling process of convolution, triggers second feature later and extracts
Unit 304；
Second feature extraction unit 304 is used for: extracting second feature information, wherein the second feature information is above to adopt
The characteristic information of the last output object of sample convolution processing unit 305；
Cutting unit 306 is used for: when completing n times and extracting the iterative process of second feature information, based on training in advance
Classifier and the second feature information extracted of n-th, the left heart is carried out to the output object of the last up-sampling process of convolution
The segmentation of room cardiac muscle；
Wherein, above-mentioned N is not less than 2, it is preferable that N takes 4.
Optionally, fisrt feature extraction unit 302 is specifically used for: extracting fisrt feature information based on Dense network；Second
Feature extraction unit 304 is specifically used for: extracting second feature information based on Dense network.
Optionally, for above-mentioned cardiac image, 302 (n+1)th extraction fisrt feature information institutes of fisrt feature extraction unit
The convolution kernel number of the Dense network used is the convolution kernel that n-th extracts Dense network used in fisrt feature information
Several one times；And the convolution of Dense network used in second feature extraction unit 304 (n+1)th times extraction second feature information
Core number is the half for the convolution kernel number that n-th extracts Dense network used in second feature information；And first
The convolution kernel number that 302 n-th of feature extraction unit extracts Dense network used in fisrt feature information is equal to second feature
The convolution kernel number of Dense network used in extraction unit 304 the 1st time extraction second feature information；Wherein, n ∈ [1, N).
Optionally, myocardium of left ventricle segmenting device further include: normalization unit, for what is got to acquiring unit 301
Cardiac image carries out the normalized of picture size, obtains the cardiac image of pre-set dimension.Down-sampling convolution processing unit 303
It is specifically used for: down-sampling process of convolution is carried out to the cardiac image that above-mentioned normalization unit obtains.
It should be noted that the myocardium of left ventricle segmenting device can be used for realizing the left ventricle that above method embodiment provides
Myocardium dividing method.In the exemplary myocardium of left ventricle segmenting device of Fig. 6, the division of each functional module is merely illustrative of, real
It can according to need in the application of border, such as the convenient of realization of configuration requirement or software of corresponding hardware considers, and will be above-mentioned
Function distribution is completed by different functional modules, i.e., the internal structure of myocardium of left ventricle segmenting device is divided into different functions
Module, to complete all or part of the functions described above.Moreover, in practical applications, the corresponding function in the present embodiment
Energy module can be by corresponding hardware realization, can also execute corresponding software by corresponding hardware and complete.This specification mentions
The each embodiment supplied can all apply foregoing description principle, repeat no more below.
Therefore the embodiment of the present application is by extracting the characteristic information (such as second feature information) of cardiac image and inputting
Preparatory trained classifier is identified, the automatic segmentation to myocardium of left ventricle in cardiac image is realized with this, due to being logical
The segmentation that the dirty image of machine automatic centering carries out myocardium of left ventricle is crossed, accordingly, with respect to traditional special by medical worker or medicine
The method of family's manual segmentation, application scheme can effectively improve the efficiency of myocardium of left ventricle segmentation；On the other hand, due to this Shen
The second feature information that classifier please be inputted in scheme is by multiple feature extraction, down-sampling process of convolution and up-sampling volume
Product processing obtains, and therefore, second feature information can preferably characterize the feature of deeper in cardiac image, so that the left heart
The result of room cardiac muscle segmentation is more accurate.
The embodiment of the present application provides a kind of myocardium of left ventricle segmenting device, referring to Fig. 7, the myocardium of left ventricle segmenting device
Include:
Memory 41, processor 42 and it is stored in the computer program that can be run on memory 41 and on processor 42,
When processor 42 executes the computer program, myocardium of left ventricle dividing method described in preceding method embodiment is realized.
Further, the myocardium of left ventricle segmenting device further include:
At least one input equipment 43 and at least one output equipment 44.
Above-mentioned memory 41, processor 42, input equipment 43 and output equipment 44, are connected by bus 45.
Wherein, input equipment 43 and output equipment 44 concretely antenna.
Memory 41 can be high random access memory body (RAM, Random Access Memory) memory, can also
For non-labile memory (non-volatile memory), such as magnetic disk storage.Memory 41 can for storing one group
Program code is executed, processor 42 is coupled with memory 41.
Further, the embodiment of the present application also provides a kind of computer readable storage medium, the computer-readable storages
Medium can be in the myocardium of left ventricle segmenting device being set in the various embodiments described above, which can be with
It is the memory in aforementioned embodiment illustrated in fig. 7.It is stored with computer program on the computer readable storage medium, the program quilt
Power distribution method described in preceding method embodiment is realized when processor executes.Further, which can store Jie
Matter can also be that USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), RAM, magnetic or disk etc. are each
Kind can store the medium of program code.
In several embodiments provided herein, it should be understood that disclosed device and method can pass through it
Its mode is realized.For example, the apparatus embodiments described above are merely exemplary, for example, the division of the module, only
Only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple module or components can be tied
Another system is closed or is desirably integrated into, or some features can be ignored or not executed.Another point, it is shown or discussed
Mutual coupling, direct-coupling or communication connection can be through some interfaces, the INDIRECT COUPLING or logical of device or module
Letter connection can be electrical property, mechanical or other forms.
The module as illustrated by the separation member may or may not be physically separated, aobvious as module
The component shown may or may not be physical module, it can and it is in one place, or may be distributed over multiple
On network module.Some or all of the modules therein can be selected to realize the mesh of this embodiment scheme according to the actual needs
's.
It, can also be in addition, can integrate in a processing module in each functional module in each embodiment of the application
It is that modules physically exist alone, can also be integrated in two or more modules in a module.Above-mentioned integrated mould
Block both can take the form of hardware realization, can also be realized in the form of software function module.
If the integrated module is realized in the form of software function module and sells or use as independent product
When, it can store in a computer readable storage medium.Based on this understanding, the technical solution of the application is substantially
The all or part of the part that contributes to existing technology or the technical solution can be in the form of software products in other words
It embodies, which is stored in a readable storage medium storing program for executing, including some instructions are used so that a meter
It calculates machine equipment (can be personal computer, server or the network equipment etc.) and executes each embodiment the method for the application
All or part of the steps.And readable storage medium storing program for executing above-mentioned includes: USB flash disk, mobile hard disk, ROM, RAM, magnetic or disk etc.
The various media that can store program code.
It should be noted that for the various method embodiments described above, describing for simplicity, therefore, it is stated as a series of
Combination of actions, but those skilled in the art should understand that, the application is not limited by the described action sequence because
According to the application, certain steps can use other sequences or carry out simultaneously.Secondly, those skilled in the art should also know
It knows, the embodiments described in the specification are all preferred embodiments, and related actions and modules might not all be this Shen
It please be necessary.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, there is no the portion being described in detail in some embodiment
Point, it may refer to the associated description of other embodiments.
The above are retouching to myocardium of left ventricle dividing method provided herein, device and computer readable storage medium
It states, for those skilled in the art, according to the thought of the embodiment of the present application, can in specific embodiments and applications
There is change place, to sum up, the contents of this specification should not be construed as limiting the present application.
Claims (10)
1. a kind of myocardium of left ventricle dividing method characterized by comprising
Obtain cardiac image；
Down-sampling process of convolution is carried out to the cardiac image；
Extract fisrt feature information, wherein the fisrt feature information is the output object of the last down-sampling process of convolution
Characteristic information；
If the iterative process that n times extract fisrt feature information is not completed, based on the fisrt feature information currently extracted to nearest
The output object of down-sampling process of convolution carries out down-sampling process of convolution, and iteration executes the extraction fisrt feature letter later
The step of breath；
If completing the iterative process that n times extract fisrt feature information, based on the fisrt feature information that n-th is extracted, to n-th
The output object of down-sampling process of convolution carries out up-sampling process of convolution；
Extract second feature information, wherein the second feature information is the output object of the last up-sampling process of convolution
Characteristic information；
If the iterative process that n times extract second feature information is not completed, based on the second feature information currently extracted to most
The output object of nearly primary up-sampling process of convolution carries out up-sampling process of convolution, and iteration executes the extraction second feature later
The step of information；
If completing the iterative process that n times extract second feature information, extracted based on preparatory trained classifier and n-th
Second feature information carries out the segmentation of myocardium of left ventricle to the output object of the last up-sampling process of convolution；
Wherein, the N is not less than 2.
2. myocardium of left ventricle dividing method according to claim 1, which is characterized in that the extraction fisrt feature information
Are as follows: fisrt feature information is extracted based on Dense network；
The extraction second feature information are as follows: second feature information is extracted based on Dense network.
3. myocardium of left ventricle dividing method according to claim 2, which is characterized in that
In the iterative process for extracting fisrt feature information, Dense net used in (n+1)th extraction fisrt feature information
The convolution kernel number of network is one times of the convolution kernel number that n-th extracts Dense network used in fisrt feature information；
And in the iterative process for extracting second feature information, used in (n+1)th extraction second feature information
The convolution kernel number of Dense network is the two of the convolution kernel number that n-th extracts Dense network used in second feature information
/ mono-；
And n-th extracts equal to the 1st time the second spy of extraction of convolution kernel number of Dense network used in fisrt feature information
Reference ceases the convolution kernel number of used Dense network；
Wherein, n ∈ [1, N).
4. myocardium of left ventricle dividing method according to any one of claims 1 to 3, which is characterized in that the acquisition heart
Image, later further include:
The normalized that picture size is carried out to the cardiac image got, obtains the cardiac image of pre-set dimension；
It is described that down-sampling process of convolution is carried out to the cardiac image are as follows:
Down-sampling process of convolution is carried out to the cardiac image of the pre-set dimension.
5. myocardium of left ventricle dividing method according to any one of claims 1 to 3, which is characterized in that the N takes 4.
6. a kind of myocardium of left ventricle segmenting device characterized by comprising acquiring unit, fisrt feature extraction unit, down-sampling
Convolution processing unit, second feature extraction unit, up-sampling convolution processing unit and cutting unit；
The acquiring unit is used for: obtaining cardiac image；
The down-sampling convolution processing unit is used for: triggering described first after carrying out down-sampling process of convolution to the cardiac image
Feature extraction unit；When not completing the iterative process of n times extraction fisrt feature information, extracted based on presently described fisrt feature
The fisrt feature information that unit extracts carries out down-sampling volume to the output object of described down-sampling convolution processing unit the last time
Product processing, triggers the fisrt feature extraction unit later；
The fisrt feature extraction unit is used for: extracting fisrt feature information, wherein the fisrt feature information is the last time
The characteristic information of the output object of down-sampling process of convolution；
The up-sampling convolution processing unit is used for: when completing the iterative process of n times extraction fisrt feature information, based on described
The fisrt feature information that fisrt feature extraction unit n-th is extracted, to pair of down-sampling convolution processing unit n-th output
As carrying out up-sampling process of convolution, the second feature extraction unit is triggered later；Second feature information is extracted in unfinished n times
Iterative process when, the second feature information currently extracted based on the second feature extraction unit is to the up-sampling convolution
Processing unit the last time object of output carries out up-sampling process of convolution, triggers the second feature extraction unit later；
The second feature extraction unit is used for: extracting second feature information, wherein the second feature information is to adopt on described
The characteristic information of the last output object of sample convolution processing unit；
Cutting unit is used for: when completing the iterative process of n times extraction second feature information, based on preparatory trained classifier
The second feature information extracted with n-th carries out myocardium of left ventricle to the output object of the last up-sampling process of convolution
Segmentation；
Wherein, the N is not less than 2.
7. myocardium of left ventricle segmenting device according to claim 6, which is characterized in that the fisrt feature extraction unit tool
Body is used for: extracting fisrt feature information based on Dense network；
The second feature extraction unit is specifically used for: extracting second feature information based on Dense network.
8. myocardium of left ventricle segmenting device according to claim 7, which is characterized in that it is directed to the cardiac image, it is described
Fisrt feature extraction unit (n+1)th time the convolution kernel number for extracting Dense network used in fisrt feature information mentions for n-th
Take one times of the convolution kernel number of Dense network used in fisrt feature information；
And the convolution kernel of Dense network used in the second feature extraction unit (n+1)th time extraction second feature information
Number is the half for the convolution kernel number that n-th extracts Dense network used in second feature information；
And the fisrt feature extraction unit n-th extracts the convolution kernel number of Dense network used in fisrt feature information
The convolution kernel number of Dense network used in second feature information is extracted equal to the second feature extraction unit the 1st time；
Wherein, n ∈ [1, N).
9. a kind of myocardium of left ventricle segmenting device characterized by comprising memory, processor and be stored in the memory
Computer program that is upper and can running on the processor, the processor realize such as right when executing the computer program
It is required that method described in any one of 1 to 5.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
When being executed by processor, method described in any one of claim 1 to 5 is realized.
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| PCT/CN2019/078892 WO2020019740A1 (en) | 2018-07-24 | 2019-03-20 | Left ventricle myocardium segmentation method and device, and computer readable storage medium |
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