CN109507406A - A kind of cellular morphology intelligent extract method, device and analysis method - Google Patents
A kind of cellular morphology intelligent extract method, device and analysis method Download PDFInfo
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Abstract
The present invention provides a kind of cellular morphology intelligent extract methods characterized by comprising determines nuclear area in blood sample；Determine the region to be split comprising the nuclear area；And the image in the region to be split is inputted into first nerves network model, the cell boundaries comprising the nuclear area are determined by the first nerves network model.Cell is extracted from blood automatically by the method realization of artificial intelligence, provides corresponding cell morphology image for the quantity of the various types of cells in subsequent statistical blood.Existing artificial range estimation is replaced using artificial intelligence, can significantly improve working efficiency, also, artificial intelligence, under the support of a large amount of sample data, accuracy and consistency with higher improve accurately and effectively data foundation for diagnosis disease.
Description
Technical field
The present invention relates to field of medical image processing, and in particular to a kind of cellular morphology intelligent extract method, extraction element
And analysis method.
Background technique
The quantity of various types of cells and form can provide very valuable information in blood for diagnosis, such as sentence
When some serious disease (such as leukaemia) of breaking, the quantity of certain specific cells in blood will appear apparent difference,
Therefore, there is good diagnostic value by the statistics to various types of cells quantity in blood.It is each in existing statistics blood
The method of class cell quantity is with the naked eye to go to count and judge one by one the number amount and type of various types of cells under the microscope, in this way
Method it is not only time-consuming and laborious, workload is very heavy, and the professional people that the statistical work needs to have suitable medical knowledge
Member, the workload of each staff has just been further aggravated in this, in addition, since the growth change procedure of cell is one continuous
Process, thus be difficult accurately to judge its type by the form of cell, this will also result in different staff and has one
Fixed judgement difference.It is therefore, existing that by the method that manually counts, there are time-consuming and laborious and accuracy and consistency be not high.
Summary of the invention
In view of this, the embodiment of the present application be dedicated to providing a kind of cellular morphology intelligent extract method, extract equipment and point
Analysis method solves the problems, such as above-mentioned manually to count time-consuming and laborious and accuracy and consistency is not high.
According to an aspect of the present invention, a kind of cellular morphology intelligent extract method that one embodiment of the invention provides, comprising:
Determine the nuclear area in blood sample image；Determine the region to be split comprising the nuclear area；And it will be described
The image in region to be split inputs first nerves network model, determines to include the cell by the first nerves network model
The cell boundaries of core region.
In one embodiment, the establishment step of the first nerves network model include: will be comprising the nuclear area
First sample image and the second sample image of corresponding determining cell boundaries the first nerves network is added as sample
In the training set of model.
In one embodiment, the first sample image and/or the second sample image are obtained by way of manually demarcating.
In one embodiment, the region to be split is centered on the center of gravity of the nuclear area.
In one embodiment, the big 10%-15% of area of nuclear area described in the area ratio in the region to be split.
In one embodiment, the nuclear area in the determining blood sample image includes: that selection meets pre-set color
The continuum of condition is the nuclear area.
In one embodiment, the continuum for meeting pre-set color condition include: the continuum it is red, green,
Blue three Color Channel component values are respectively in preset threshold range.
In one embodiment, the nuclear area in the determining blood sample image includes: the company for selecting same color
Continuous region；Judge whether the continuum meets pre-set color condition；When the continuum meets the pre-set color item
When part, judge whether the area of the continuum is greater than the first area threshold and is less than second area threshold value；And when described
When the area of continuum is greater than the first area threshold and is less than second area threshold value, choosing the continuum is the cell
Core region.
In a further embodiment, the nuclear area in the determining blood sample image further include: when the company
When the area in continuous region is greater than or equal to the second area threshold value, the continuum is divided into multiple nuclear areas
Domain.
It is in one embodiment, described that the continuum is divided into multiple nuclear areas includes: by equal ratios
Example reduces the image of the continuum, and the continuum is divided into multiple nuclear areas.
In a further embodiment, the nuclear area in the determining blood sample image further include: when the company
When the area in continuous region is less than or equal to first area threshold, judgement includes the default third area threshold of the continuum
Meet the pre-set color condition with the presence or absence of other within the scope of value and area is respectively less than or equal to first area threshold
Continuum；If it exists other meet the pre-set color condition and area be respectively less than or equal to first area threshold company
Continuous region will meet the pre-set color condition and area respectively less than or equal to institute within the scope of the default third area threshold
All continuums for stating the first area threshold are merged into the nuclear area.
In one embodiment, the nuclear area in the determining blood sample image further include: with present viewing field region
The direction on orthogonal two boundaries be respectively that reference axis establishes coordinate system；When in the determining nuclear area at least
There are a points when in the reference axis, abandons the subsequent processing the nuclear area.
In one embodiment, the nuclear area in the determining blood sample image includes: by present viewing field region
Image inputs third nerve network model, determined in the present viewing field region by the third nerve network model described in
Nuclear area.
In one embodiment, the present viewing field region includes that blood sample on glass slide scatters the middle section at direction both ends
Part；Preferably, the width of the middle section part is that the blood sample scatters the 1/3 of width.
According to another aspect of the present invention, one embodiment of the invention provides a kind of cellular morphology intelligent analysis method, packet
It includes: choosing the present viewing field region in blood sample image；The cell compartment image including individual cells image is extracted, wherein institute
Stating individual cells image includes the nuclear area and cytosolic domain；And it is cell compartment image input second is refreshing
Through network model, the type of individual cells is identified by the nervus opticus network model；Wherein, extract includes individual cells figure
The cell compartment image of picture is using described in any item methods as above.
In one embodiment, the present viewing field region chosen in blood sample image includes: successively to choose multiple views
One in wild region is used as the present viewing field region.
In one embodiment, length of the overlapping region of two adjacent area of visual field on the moving direction of the visual field is big
In pre-set length threshold；Preferably, the pre-set length threshold is 4-5 microns.
In one embodiment, one successively chosen in multiple area of visual field wraps as the present viewing field region
It includes: coordinate label is carried out to the image of the multiple area of visual field, the multiple visual field is successively chosen according to coordinate label
One in region is used as the present viewing field region.
In one embodiment, it is described by the cell compartment image input nervus opticus network model include: delete segmentation
Other images in the cell compartment image out in addition to individual cells image will delete the described thin of other images
Born of the same parents' area image inputs the nervus opticus network model.
In one embodiment, the method also includes: multiple cell compartment images of coincidence are merged into an institute
State cell compartment image.
In one embodiment, the establishment step of the nervus opticus network model include: will be comprising the nuclear area
First sample image input first nerves network model, by the first nerves network model determine include the nucleus
The cell boundaries in region, to obtain determining the second sample image of cell boundaries；By second sample image and corresponding thin
Born of the same parents' type is added in the training set of the nervus opticus network model as sample.
In one embodiment, described to be added described for second sample image and corresponding cell type as sample
It include: its in deletion second sample image in addition to the individual cells image in the training set of two neural network models
His image, second sample image and corresponding cell type that will delete other images are as sample addition described the
In the training set of two neural network models.
In one embodiment, described to be added described for second sample image and corresponding cell type as sample
It include: the individual cells marked with different colors in second sample image in the training set of two neural network models
The nervus opticus is added using second sample image for marking color and corresponding cell type as sample in area image
In the training set of network model.
In one embodiment, the type that individual cells are identified by the nervus opticus network model includes: difference
The value of the confidence that individual cells are various cell types is calculated, and determines the type of the individual cells according to the value of the confidence.
In one embodiment, the type that the individual cells are determined according to the value of the confidence includes: that maximum ought set
When letter value is greater than default confidence threshold, determine that the individual cells are type corresponding to the maximum the value of the confidence.
In one embodiment, the type that the individual cells are determined according to the value of the confidence includes: that maximum ought set
When letter value is less than or equal to default confidence threshold, maximum at least two type of the value of the confidence and corresponding the value of the confidence are marked.
In one embodiment, described to mark maximum at least two type of the value of the confidence and corresponding the value of the confidence includes: use
Different colours mark maximum at least two type of the value of the confidence and corresponding the value of the confidence.
According to another aspect of the present invention, a kind of cellular morphology intelligent extraction device that one embodiment of the invention provides, packet
Include: nucleus determining module is configured to determine the nuclear area in blood sample image；Area determination module to be split, structure
It makes to determine the region to be split for including the nuclear area；And cell boundaries determining module, it is configured to described wait divide
The image input first nerves network model for cutting region, determines to include the nuclear area by the first nerves network model
The cell boundaries in domain.
In one embodiment, the region to be split is centered on the center of gravity of the nuclear area.
In one embodiment, the big 10%-15% of area of nuclear area described in the area ratio in the region to be split.
In one embodiment, the nucleus determining module is further configured to: choosing the company for meeting pre-set color condition
Continuous region is the nuclear area.
In one embodiment, the nucleus determining module is further configured to: the red, green, blue three of the continuum
A Color Channel component value is respectively in preset threshold range.
In one embodiment, the nucleus determining module includes: selection unit, is configured to the continuous of selection same color
Region；First judging unit is configured to judge that the continuum demonstration meets pre-set color condition；Second judgment unit, structure
It makes to judge whether the area of the continuum is greater than the first face when the continuum meets the pre-set color condition
It accumulates threshold value and is less than second area threshold value；And nucleus selection unit, it is configured to be greater than the when the area of the continuum
One area threshold and be less than second area threshold value when, choose the continuum be the present viewing field region in the cell
Core region.
In a further embodiment, the second judgment unit is further configured to: when the area of the continuum
When more than or equal to the second area threshold value, the continuum is divided into multiple nuclear areas.
In one embodiment, the nucleus determining module is further configured to: by continuum described in scaled down
The continuum is divided into multiple nuclear areas by the image in domain.
In a further embodiment, the nucleus determining module further comprises: third judging unit is configured to work as
When the area of the continuum is respectively less than or is equal to first area threshold, judgement comprising the continuum default the
Meet the pre-set color condition with the presence or absence of other within the scope of three area thresholds and area is respectively less than or is equal to first face
The continuum of product threshold value；Other meet the pre-set color condition if it exists and area is respectively less than or is equal to first area
The continuum of threshold value, by within the scope of the default third area threshold meet the pre-set color condition and area is respectively less than
Or the nuclear area is merged into equal to all continuums of first area threshold.
In one embodiment, the nucleus determining module is further configured to: with being mutually perpendicular to for present viewing field region
The direction on two boundaries be respectively that reference axis establishes coordinate system；When in the determining nuclear area at least exist a point
When in the reference axis, the subsequent processing the nuclear area is abandoned.
In one embodiment, the nucleus determining module is further configured to: by the image in the present viewing field region
Third nerve network model is inputted, the cell in the present viewing field region is determined by the third nerve network model
Core region.
In one embodiment, the present viewing field region includes that blood sample on glass slide scatters the middle section at direction both ends
Part；Preferably, the width of the middle section part is that the blood sample scatters the 1/3 of width.
According to another aspect of the present invention, a kind of cell type analytical equipment that one embodiment of the invention provides, comprising: choosing
Modulus block is configured to choose the present viewing field region in blood sample image；Cell compartment extraction module, being configured to extraction includes
The cell compartment image of individual cells image, wherein the individual cells image includes the nuclear area and cytoplasm district
Domain；And cell type identification module, it is configured to the cell compartment image inputting nervus opticus network model, by described
The type of nervus opticus network model identification individual cells；Wherein, cell compartment extraction module includes cell boundaries determining module.
In one embodiment, described device further include: first nerves network model establishes module, and being configured to will be comprising described
The first sample image of nuclear area and the second sample image of corresponding determining cell boundaries are added described the as sample
In the training set of one neural network model.
In one embodiment, the first sample image and/or the second sample image are obtained by way of manually demarcating.
In one embodiment, the module of choosing is further configured to: successively choosing a work in multiple area of visual field
For the present viewing field region.
In one embodiment, length of the overlapping region of two adjacent area of visual field on the moving direction of the visual field is big
In pre-set length threshold；Preferably, the pre-set length threshold is 4-5 microns.
In one embodiment, the selection module is further configured to: being sat to the image of the multiple area of visual field
Mark label, one successively chosen in the multiple area of visual field according to coordinate label are used as the present viewing field region.
In one embodiment, the cell type identification module is further configured to: deleting the cellular regions being partitioned into
Other images in area image in addition to the individual cells image will delete the cell compartment image of other images
Input the nervus opticus network model.
In one embodiment, described device further include: duplicate checking module is configured to the multiple cell compartment figures that will be overlapped
As merging into the cell compartment image.
In one embodiment, described device further include: nervus opticus network model establishes module, and being configured to will be comprising described
The first sample image of nuclear area inputs first nerves network model, includes by the first nerves network model determination
The cell boundaries of the nuclear area, to extract the second sample image for determining cell boundaries；By second sample image
And corresponding cell type is added in the training set of the nervus opticus network model as sample.
In one embodiment, the nervus opticus network model is established module and is further configured to: deleting second sample
Other images in this image in addition to the individual cells image will delete second sample image of other images
And corresponding cell type is added in the training set of the nervus opticus network model as sample.
In one embodiment, the nervus opticus network model is established module and is further configured to: with different color marks
The individual cells area image in second sample image is infused, second sample image and correspondence of color will be marked
Cell type be added as sample in the training set of the nervus opticus network model.
In one embodiment, the cell type identification module includes: the value of the confidence computing unit, is configured to calculate single
A cell is the value of the confidence of various cell types, and the type of the individual cells is determined according to the value of the confidence.
In one embodiment, the cell type identification module further comprises: type determining units, is configured to when maximum
The value of the confidence when being greater than default confidence threshold, determine that the individual cells are type corresponding to the maximum the value of the confidence.
In one embodiment, the cell type identification module further comprises: the value of the confidence marking unit, being configured to ought be most
When big the value of the confidence is less than or equal to default confidence threshold, maximum at least two type of the value of the confidence and corresponding confidence are marked
Value.
In one embodiment, the value of the confidence marking unit is further configured to: marking the value of the confidence most with different colours
Big at least two types and corresponding the value of the confidence.
According to another aspect of the present invention, a kind of electronic equipment that one embodiment of the invention provides, comprising: processor；It deposits
Reservoir；And the computer program instructions of storage in the memory, the computer program instructions are by the processor
The processor is made to execute as above described in any item methods when operation.
According to another aspect of the present invention, a kind of computer program product that one embodiment of the invention provides, including calculate
Machine program instruction, the computer program instructions execute the processor described in any one as above
Method.
According to another aspect of the present invention, a kind of computer readable storage medium that one embodiment of the invention provides, thereon
The step of being stored with computer program, as above any one the method realized when the computer program is executed by processor.
Cellular morphology intelligent extract method provided by the embodiments of the present application by determining nuclear area, and passes through nerve
Network model determines that the cell boundaries comprising the nuclear area extract the form of individual cells, and knows for subsequent cell type
Corresponding cell morphology image is not provided.Artificial intelligence is realized through the above steps and replaces artificial segmentation, greatly improved
The working efficiency of analysis, and being learnt by a large amount of real data, can step up analysis accuracy and result one
Cause property provides reliable data for diagnosis and supports.
Detailed description of the invention
Fig. 1 show the schematic diagram of the blood sample image of analysis of the embodiment of the present invention.
Fig. 2 show a kind of flow chart of cellular morphology intelligent extract method of one embodiment of the invention offer.
Fig. 3 show another embodiment of the present invention provides a kind of cellular morphology intelligent extract method flow chart.
Fig. 4 show the flow chart of the determination nuclear area method of one embodiment of the invention offer.
Fig. 5 show another embodiment of the present invention provides a kind of cellular morphology intelligent extract method flow chart.
Fig. 6 show the flow chart of the determination cellular morphology intelligent analysis method of one embodiment of the invention offer.
Fig. 7 show the flow chart of the identification cell type method of one embodiment of the invention offer.
Fig. 8 show another embodiment of the present invention provides a kind of cellular morphology intelligent analysis method flow chart.
Fig. 9 show a kind of flow chart of Establishment of Neural Model method of one embodiment of the invention offer.
Figure 10 show another embodiment of the present invention provides a kind of Establishment of Neural Model method flow chart.
Figure 11 show another embodiment of the present invention provides a kind of Establishment of Neural Model method flow chart.
Figure 12 show another embodiment of the present invention provides a kind of Establishment of Neural Model method flow chart.
Figure 13 show another embodiment of the present invention provides a kind of cellular morphology intelligent analysis method flow chart.
Figure 14 show a kind of structural schematic diagram of cellular morphology intelligent extraction device of one embodiment of the invention offer.
Figure 15 show a kind of structural schematic diagram of cellular morphology intellectual analysis device of one embodiment of the invention offer.
Figure 16 show another embodiment of the present invention provides a kind of cellular morphology intellectual analysis device structural schematic diagram.
Figure 17 show another embodiment of the present invention provides a kind of cellular morphology intellectual analysis device structural schematic diagram.
Figure 18 show another embodiment of the present invention provides a kind of cellular morphology intellectual analysis device structural schematic diagram.
Figure 19 show the structural schematic diagram of the electronic equipment of one embodiment of the invention offer.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Site preparation description, it is clear that the described embodiment is only a part of the embodiment of the present invention, instead of all the embodiments.Based on this
Embodiment in invention, every other reality obtained by those of ordinary skill in the art without making creative efforts
Example is applied, shall fall within the protection scope of the present invention.
Blood cell is roughly divided into three classes: red blood cell, leucocyte and blood platelet, and wherein leucocyte is again by lymphocyte, list
Nucleus, neutrophil leucocyte (being subdivided into band form neutrophilic granulocyte and neutrophilic segmented granulocyte), eosinophil, basophilic
Property granulocyte five subclass composition.It is white thin for different subclass in biological analysis field, especially clinical examination analysis field
Born of the same parents carry out accurately classification for diagnosing and research has very important effect, and the variation of each subclass leucocyte content exists
Clinically it is used as the important indicator of diagnosing and treating.For example, acute bacterial infections will cause neutrophilic leukocytosis, it is parasitic
Insect infection and anaphylactia etc. will cause eosinophilia.
Currently, white blood cell count(WBC) method is usually to estimate counting method, range estimation counting method is to be counted on tally with microscopy
Several methods can directly be counted without processing, will first can also be counted again after haemocyte nuclear targeting with coloring agent.
However, range estimation counting method needs the personnel of profession to complete, and therefore, heavy workload, low efficiency, and result is inaccurate, consistent
Property is not high.
For the disadvantages mentioned above for solving range estimation counting method, the embodiment of the present application provides a kind of cellular morphology intelligent extraction side
Method, extraction element and analysis method extract leucocyte by the method realization of artificial intelligence from blood automatically, are subsequent statistical
The quantity of various types of cells in blood provides corresponding cell morphology image.Existing artificial mesh is replaced by artificial intelligence
It surveys, can significantly improve working efficiency, also, artificial intelligence is under the support of a large amount of sample data, standard with higher
True property and consistency improve accurately and effectively data foundation for diagnosis disease.
After describing the basic principle of the application, carry out the various non-limits for specifically introducing the application below with reference to the accompanying drawings
Property embodiment processed.
Fig. 1 show the embodiment of the present invention analysis blood sample image schematic diagram, wherein 1 for present viewing field region,
2 it is individual cells image, 3 be nuclear area, 4 is cell compartment image.It should be appreciated that Fig. 1 is in order to more explicit
The implementation for illustrating the embodiment of the present application and the illustrative blood sample image provided, not blood sample in practical application
This true picture.
Fig. 2 show a kind of flow chart of cellular morphology intelligent extract method of one embodiment of the invention offer.Such as Fig. 2 institute
Show, which includes the following steps:
Step 110: determining the nuclear area in blood sample image.It is determined in selected present viewing field region 1 thin
Karyon region 3, wherein present viewing field region 1 can be the whole region in blood sample image, can also be by blood sample figure
As being divided into multiple area of visual field, successively choosing one of area of visual field is present viewing field region.The cell of cell-free core
Canceration will not be generated, therefore, can only need to pay close attention to karyocyte in the diseases such as analysis cancer.
In the embodiment of the present application, as shown in figure 3, step 110 may include sub-step:
Step 1101: selection meets the continuum of pre-set color condition for the nuclear area in present viewing field region 1
3.In order to distinguish the various pieces in blood sample, it will usually be dyed to blood sample, using various pieces in coloring agent
Under the action of the different colours that are shown can distinguish each portions such as nucleus, cytoplasm, cell, impurity in blood sample
Point.Pre-set color condition is arranged according to the color that stained cells core is shown in the embodiment of the present application, and selection meets the default face
The continuum of vitta part is nuclear area.Staining method can be include the existing colouring method such as Wright's staining, this Shen
Please embodiment for staining method without limitation.
Optionally, pre-set color condition may is that red (R), green (G), blue (B) three colors of the continuum of selection are logical
Road component value is respectively in preset threshold range.Rgb color mode is a kind of color standard of industry, is by red
(R), green (G), the variation of blue (B) three Color Channels and their mutual superpositions obtain miscellaneous color
, i.e., each color can be uniquely determined by it in the component value of red (R), green (G), blue (B) three Color Channels, because
This, can choose color area required for our by the way that the threshold range of red (R), green (G), blue (B) three component values is arranged
Domain, i.e. nuclear area.
Optionally, as shown in figure 4, step 110 can also include following sub-step:
Step 1102: selecting the continuum of same color.
Step 1103: judging whether continuum meets pre-set color condition, if so, going to step 1104, otherwise, abandon
Subsequent step to the continuum simultaneously goes to step 1102.
Step 1104: the area of continuum and the size relation of the first area threshold, second area threshold value are judged, when even
When the area in continuous region is greater than the first area threshold and is less than second area threshold value, 1105 are gone to step；When the area of continuum
When more than or equal to second area threshold value, 1106 are gone to step；Otherwise, 1107 are gone to step.
Step 1105: choosing continuum is the nuclear area in present viewing field region.When the face of a continuum
When chroman foot pre-set color condition, it can determine that the continuum is nuclear area, but due to selected present viewing field
Area image is flat image, and multiple cells are likely to result in multiple nucleus and are selected or the leaflet core of cell when being laminated
It may be chosen for different multiple nuclear areas.In order to avoid above-mentioned erroneous judgement, the embodiment of the present application is pre- to meeting
Judged if the continuum of color condition is further, by judging whether the area of continuum meets individual cells core
Whether size, the i.e. area of continuum are greater than the first area threshold and are less than second area threshold value, if meeting the preset condition,
Then determine that the continuum is single nuclear area.
Step 1106: continuum is divided into multiple nuclear areas.When the area of continuum is greater than or equal to the
When two area thresholds, i.e. the area of continuum has been more than the area of nucleus, then it is assumed that have in the continuum it is multiple (at least
Two) therefore the continuum is divided into multiple nuclear areas, and to multiple nuclear areas point by the nucleus of stacking
It is not analyzed.
The method that continuum is divided into multiple nuclear areas be may is that into the figure by scaled down continuum
Continuum is divided into multiple nuclear areas by picture.It, can will be more in image by the image of scaled down continuum
The width of the join domain of a nucleus gradually reduces, until join domain disconnect, that is, realize continuum is divided into it is more
A nuclear area.
It should be appreciated that the embodiment of the present application can choose other modes for continuum point according to practical application scene
Multiple nuclear areas are cut into, continuum for being divided into the concrete mode of multiple nuclear areas not by the embodiment of the present application
It limits.
Step 1107: judgement is default comprising meeting within the scope of the default third area threshold of continuum with the presence or absence of other
Color condition and area are respectively less than or otherwise, turn step if so, going to step 1108 equal to the continuum of the first area threshold
Rapid 120.
Step 1108: meet pre-set color condition and the area within the scope of default third area threshold are respectively less than or are equal to
All continuums of first area threshold are merged into a nuclear area.When a certain range (default third area threshold
Be worth range) in multiple continuums area be respectively less than or be equal to the first area threshold when, then it is assumed that multiple continuum
For a leaflet nuclear area (cell B as shown in Figure 1), a nuclear area is merged into multiple continuum.
Wherein, the area value that third area threshold can be leaflet nucleus is preset.
Optionally, as shown in figure 5, step 110 can also include sub-step:
Step 1109: the image in present viewing field region 1 being inputted into third nerve network model, passes through third nerve network mould
Type determines the nuclear area 3 in present viewing field region 1.By establishing third nerve network model, by present viewing field region
Image inputs third nerve network model, determines nuclear area using third nerve network model.It is instructed by a large amount of sample
Practice the accuracy that can effectively improve determining nuclear area, avoids causing to determine nuclear area because dyeing effect is bad
The problem of certainty reduces.
Optionally, third nerve network model can be convolutional neural networks model.It should be appreciated that third nerve network mould
Type can also be other neural network models, the embodiment of the present application to the type of third nerve network model without limitation.
Step 120: determining the region to be split comprising the nuclear area.By determining that cell boundaries are available complete
Whole individual cells image, so as to be partitioned into the cell compartment image including individual cells image.
Step 130: the image in region to be split is inputted into first nerves network model, it is true by first nerves network model
It surely include the cell boundaries of nuclear area.Wherein, the training sample of first nerves network model is to include nuclear area
Second sample image of first sample image and corresponding determining cell boundaries will include cell by the training of great amount of samples
The image in the region to be split of core region inputs first nerves network model, can determine the cell side comprising the nuclear area
Boundary.Wherein, region to be split can be above-mentioned cell compartment, may not be, as long as region to be split includes complete single
A cell, the embodiment of the present application for region to be split concrete shape without limitation.
Optionally, region to be split can be centered on the center of gravity of nuclear area 3.Small as far as possible includes cell in order to obtain
The center in the region to be split of core region, region to be split can be set as the center of gravity of nuclear area, i.e., region to be split is
Equal proportion expands on the basis of nuclear area.Certainly, region to be split may be the other shapes such as rectangular.
Optionally, the area in region to be split can be 10%-15% bigger than the area of nuclear area.According to usual cell
Area and its nucleus area corresponding relationship, the area in region to be split can be set to the face than nuclear area
The big 10%-15% of product, to ensure that region to be split includes nuclear area.
It optionally, is respectively that reference axis establishes coordinate with the direction on orthogonal two boundaries in present viewing field region 1
It is (as shown in Figure 1)；When at least there is a point in certain nucleus region 3 in reference axis, nucleus can be removed
Region 3 (cell C as shown in Figure 1).I.e. when the contour connection in the nuclear area of cell C and present viewing field region, then table
The bright cell C comprising the nucleus some in area of visual field 1 (do not include in this prior complete in the present viewing field region
Cell C), therefore, abandon the subsequent processing to the nuclear area, again choose a new nuclear area, to avoid
Lead to the problem of identification inaccuracy because cell is incomplete.
The intelligent extraction leucocyte from blood automatically is realized by the method for artificial intelligence, is white thin in subsequent statistical blood
The type and quantity of born of the same parents provides corresponding cell morphology image.Existing artificial range estimation, Neng Gou great are replaced using artificial intelligence
The raising working efficiency of width, also, artificial intelligence is under the support of a large amount of sample data, accuracy with higher with it is consistent
Property, accurately and effectively data foundation is improved for diagnosis disease.
Fig. 6 show a kind of flow chart of cellular morphology intelligent analysis method of one embodiment of the invention offer.Such as Fig. 6 institute
Show, which includes the following steps:
Step 210: choosing the present viewing field region 1 in blood sample image.Choose the blood sample being placed on glass slide
The type and quantity of cell in present viewing field region 1 are analyzed in present viewing field region 1 in this image.
Present viewing field region 1 can be the blood sample on glass slide and scatter the middle section part at direction both ends；Preferably, in
The width of section part is that blood sample scatters the 1/3 of width.Blood sample can cause to dissipate when scattering on glass slide because of extruding
Evolution to initial segment cell quantity is less and the accumulation of the cell quantity of concluding paragraph is more, and the cell quantity of middle section part is suitable
In and scatter more uniformly, therefore, middle portion can be chosen and be allocated as preferably reflecting blood for present viewing field region 1
Cell distribution situation in sample.
It should be appreciated that the embodiment of the present application can according to the demand of practical application scene choose blood sample whole or
Partial region is as present viewing field region, as long as selected present viewing field region can accurately reflect in blood sample carefully
The difference state of born of the same parents, the embodiment of the present application for present viewing field region specific location without limitation.
Present viewing field region 1 in blood sample image can also be by successively choosing one in multiple area of visual field work
For present viewing field region 1.It, can directly will be under microscope since the image that blood sample is showed under the microscope is bigger
Present viewing field region (such as above-mentioned middle section part) is chosen after blood sample image scanning or first chooses present viewing field region sweeps again
The image in selected present viewing field region is retouched, and present viewing field area image is analyzed；It can also will choose present viewing field
Blood sample be divided into multiple area of visual field, successively choose a region as present viewing field in multiple area of visual field,
And the area image of present viewing field is analyzed, the result of multiple area of visual field is aggregated to form to final analysis result.It is logical
It crosses and blood sample image is divided into multiple area of visual field, it is possible to reduce the data volume analyzed every time reduces the side of the application
Requirement of the method to realization device.It should be appreciated that can be used under the premise of not considering the configuration and the speed of service of realization device
The method that any one of the above chooses present viewing field region, specific method of the embodiment of the present application for selection present viewing field region
Without limitation.
In order to preferably realize the analysis of multiple area of visual field, coordinate label can be carried out to multiple field-of-view images, according to
Coordinate marks one successively chosen in multiple area of visual field to be used as present viewing field region 1.It is marked by setting coordinate, Ke Yijing
The continuous analysis of the true multiple area of visual field of realization, avoids the omission in partial visual field region and part cell.
Optionally, length of the overlapping region of two adjacent area of visual field on the moving direction of the visual field is greater than preset length
Threshold value；Preferably, pre-set length threshold is 4-5 microns.The diameter of usual leucocyte is 4-5 microns, therefore, is in order to prevent
The omission of cell between two adjacent area of visual field, the overlapping region of two adjacent area of visual field at least can include one
A cell, i.e., length of the overlapping region of two adjacent area of visual field on the moving direction of the visual field are greater than the straight of a cell
Diameter.
Step 220: extracting the cell compartment image 4 including individual cells image 2, wherein individual cells image includes thin
Karyon region and cytosolic domain (cell A as shown in Figure 1).The method that cell compartment image is extracted in step 220 can be
Any one of cellular morphology intelligent extract method in above-described embodiment, wherein the individual cells image in cell compartment image 4
2 cell boundaries are it has been determined that i.e. cell compartment image 4 includes the individual cells image 2 for determining cell boundaries.Wherein, cellular regions
Area image 4 may include individual cells image 2 and be greater than individual cells image 2, and cell compartment image 4 can also be exactly single thin
Born of the same parents' image 2, as long as cell compartment image 4 includes complete individual cells image 2, the embodiment of the present application is for cell compartment
The concrete shape of image is without limitation.
Step 230: cell compartment image 4 being inputted into nervus opticus network model, is identified by nervus opticus network model
The type of individual cells.After extracting the cell compartment image 4 including individual cells image 2, which is inputted
Nervus opticus network model identifies the type of the individual cells using nervus opticus network model.
Optionally, as shown in fig. 7, step 230 may include sub-step:
Step 2301: deleting other images in the cell compartment image 4 being partitioned into addition to individual cells image 2.
Step 2302: the cell compartment image 4 for deleting other images is inputted into nervus opticus network model.
Other images (including impurity and other cells etc.) in cell compartment image 4 in addition to individual cells image are deleted
Except being in order to exclude interference of other images to the type of identification individual cells, to improve the accuracy and identification of identification
Efficiency.
It is realized by the method for artificial intelligence and extracts leucocyte from blood automatically and identify its type, to count bleeding
The quantity of all kinds of leucocytes in liquid.Existing artificial range estimation is replaced using artificial intelligence, can significantly improve working efficiency,
Also, artificial intelligence is under the support of a large amount of sample data, accuracy and consistency with higher, is diagnosis disease
Improve accurately and effectively data foundation.
Fig. 8 show another embodiment of the present invention provides a kind of cellular morphology intelligent analysis method flow chart.Such as Fig. 8
Shown, the present embodiment may also include the steps of: on the basis of the above embodiments
Step 225: multiple cell compartment images of coincidence are merged into a cell compartment image.When the cell being partitioned into
When there are multiple cell compartment picture registrations in region, then illustrate that multiple cells that multiple cell compartment image is included are one
Cell, wherein the nucleus of the cell is leaflet core, then multiple cell compartment image is merged into a cell compartment figure
Picture prevents from repeating to count the cell and cause final statistical result.
Fig. 9 show a kind of flow chart of Establishment of Neural Model method of one embodiment of the invention offer.Such as Fig. 9 institute
Show, the establishment step of nervus opticus network model can include:
Step 310: the first sample image comprising nuclear area being inputted into first nerves network model, passes through the first mind
The cell boundaries comprising nuclear area 3 are determined through network model, to obtain determining the second sample image of cell boundaries.
Step 320: nervus opticus network model is added using the second sample image and corresponding cell type as sample
In training set.Preamble model of the first nerves network model as nervus opticus network model, first nerves network is obtained
Input of second sample image as nervus opticus network model, corresponding cell type are defeated as nervus opticus network model
Out, the sample in using the input-output as the training set of nervus opticus network model, is realized by nervus opticus network model
The identification of individual cells.
By the way that the segmentation of individual cells and identification is complete by first nerves network model and nervus opticus network model respectively
At the complexity and calculation amount of each neural network model being reduced, to can also improve the efficiency of analysis.
Figure 10 show another embodiment of the present invention provides a kind of Establishment of Neural Model method flow chart.Such as figure
Shown in 10, above-mentioned steps 320 can also include:
Step 321: deleting other images in the second sample image in addition to individual cells image 2.
Step 322: being added second using the second sample image for deleting other images and corresponding cell type as sample
In the training set of neural network model.
By other image-erasings in the second sample image in addition to individual cells image, and will delete except other images
Second sample image and corresponding cell type are added in the training set of nervus opticus network model as sample, eliminate other
Interference of the image to the type of identification individual cells, to improve the accuracy of identification and the efficiency of identification.
Figure 11 show another embodiment of the present invention provides a kind of Establishment of Neural Model method flow chart.Such as figure
Shown in 11, above-mentioned steps 320 can also include:
Step 323: marking the individual cells area image in the second sample image with different colors.
Step 324: nervus opticus is added using the second sample image for marking color and corresponding cell type as sample
In the training set of network model.
The individual cells area image in the second sample image is marked using different colors, can clearly be distinguished different
Boundary between individual cells region (the individual cells region especially closed on), avoids interfering with each other, to improve identification
Accuracy.
Optionally, nervus opticus network model can be convolutional neural networks model.It should be appreciated that nervus opticus network mould
Type can also be other neural network models, the embodiment of the present application to the type of nervus opticus network model without limitation.
Figure 12 show another embodiment of the present invention provides a kind of Establishment of Neural Model method flow chart.Such as figure
Shown in 12, the establishment step of first nerves network model can include:
Step 410: by the first sample image comprising nuclear area 3 and the second sample of corresponding determining cell boundaries
Image is added in the training set of first nerves network model as sample.
Step 420: the second sample image and corresponding cell type that first nerves network model is obtained are as sample
It is added in the training set of nervus opticus network model.
By establishing first nerves network model and nervus opticus network model, the segmentation of individual cells and identification are distinguished
It is completed by first nerves network model and nervus opticus network model, reduces the complexity and meter of each neural network model
Calculation amount, to can also improve the efficiency of analysis.
Optionally, first nerves network model can be convolutional neural networks model.It should be appreciated that first nerves network mould
Type can also be other neural network models, the embodiment of the present application to the type of first nerves network model without limitation.
Optionally, first sample image and/or the second sample image can be obtained by way of manually demarcating.Due to
The acquisition modes of one sample image and the second sample image are simple compared to the sample acquisition mode in nervus opticus network model,
Therefore, the mode manually obtained can also be chosen in the embodiment of the present invention.
Figure 13 show another embodiment of the present invention provides a kind of cellular morphology intelligent analysis method flow chart.Such as figure
Shown in 13, the present embodiment may also include that on the basis of Fig. 6 corresponding embodiment
Step 240: calculating separately the value of the confidence that individual cells are various cell types, and determining single thin according to the value of the confidence
The type of born of the same parents.To each individual cells, the value of the confidence that it is various cell types is calculated separately, and determine according to each the value of the confidence
The type of individual cells.It should be appreciated that existing probability can be used by calculating the value of the confidence that individual cells are certain cell type
Any one method in statistics, the embodiment of the present application to calculate the value of the confidence specific method without limitation.Usual the value of the confidence is one
A percent value, to describe the determination degree that individual cells are this kind of cell type.
Optionally, when maximum the value of the confidence is greater than default confidence threshold, it may be determined that individual cells are maximum the value of the confidence
Corresponding type.Confidence threshold (such as can be set to 90%-95%) is preset, after calculating each the value of the confidence,
If maximum the value of the confidence is greater than the confidence threshold, then it is assumed that determine that the individual cells are that maximum the value of the confidence is corresponding thin substantially
Born of the same parents' type.
Optionally, when maximum the value of the confidence is less than or equal to default confidence threshold, it is maximum extremely that the value of the confidence can be marked
Few two types and corresponding the value of the confidence.When maximum the value of the confidence is less than or equal to default confidence threshold, then it is assumed that this is single
The type of cell is unable to judge accurately, and maximum at least two type of the value of the confidence and corresponding the value of the confidence are marked, by
It is artificial to determine, it avoids judging by accident and influencing final statistical result.It should be appreciated that the embodiment of the present application can mark preset quantity
Type can also preset a threshold value (such as 40%), and the value of the confidence is only marked to be greater than the type of the threshold value, the embodiment of the present application pair
The quantity and mode of label are without limitation.
Preferably, maximum at least two type of the value of the confidence and corresponding the value of the confidence can also be marked with different colours.
Cell type and the value of the confidence are marked by different colours, more convenient can manually be checked.
Figure 14 show a kind of structural schematic diagram of cellular morphology intelligent extraction device of one embodiment of the invention offer.Such as
Shown in Figure 14, which includes:
Nucleus determining module 71 is configured to determine the nuclear area in blood sample image；Region to be split determines
Module 72 is configured to determine the region to be split comprising nuclear area；Cell boundaries determining module 73, being configured to will be to be split
The image in region inputs first nerves network model, determines the cell side comprising nuclear area by first nerves network model
Boundary.
It is realized by the method for artificial intelligence and extracts leucocyte from blood automatically and identify its type, to count bleeding
The quantity of all kinds of leucocytes in liquid.Existing artificial range estimation is replaced using artificial intelligence, can significantly improve working efficiency,
Also, artificial intelligence is under the support of a large amount of sample data, accuracy and consistency with higher, is diagnosis disease
Improve accurately and effectively data foundation.
In one embodiment, nucleus determining module 71 can be further configured to:
Selection meets the continuum of pre-set color condition for the nuclear area 3 in present viewing field region 1.The application is real
It applies example and pre-set color condition is arranged according to the color that stained cells core is shown, selection meets the continuous of the pre-set color condition
Region is nuclear area.
Optionally, pre-set color condition may is that red (R), green (G), blue (B) three colors of the continuum of selection are logical
Road component value is respectively in preset threshold range.
Optionally, nucleus determining module 71 may further include: selection unit, for selecting the continuous of same color
Region；First judging unit, for judging whether continuum meets pre-set color condition；Second judgment unit, for when company
When continuous region meets pre-set color condition, judge whether the area of continuum is greater than the first area threshold and is less than second area
Threshold value；And nucleus selection unit, it is greater than the first area threshold for the area when continuum and is less than second area threshold
When value, choosing continuum is the nuclear area in present viewing field region.In order to avoid above-mentioned erroneous judgement, the application is implemented
Example is further to the continuum for meeting pre-set color condition to be judged.
Optionally, second judgment unit can be further configured to: when the area of continuum is greater than or equal to the second face
When product threshold value, continuum is divided into multiple nuclear areas.When the area of continuum is greater than or equal to second area threshold
When value, i.e. the area of continuum has been more than the area of nucleus, then it is assumed that has multiple (at least two) layers in the continuum
Therefore the continuum is divided into multiple nuclear areas, and divide respectively multiple nuclear areas by folded nucleus
Analysis.
The method that continuum is divided into multiple nuclear areas be may is that into the figure by scaled down continuum
Continuum is divided into multiple nuclear areas by picture.
Optionally, nucleus determining module 71 may further include: third judging unit, for working as the face of continuum
When product is less than or equal to first area threshold, whether judgement within the scope of the default third area threshold of continuum comprising depositing
Other meet pre-set color condition and area be respectively less than or equal to the first area threshold continuum；Other meet if it exists
The pre-set color condition and area be respectively less than or equal to first area threshold continuum, by default third area threshold
Meet pre-set color condition and area within the scope of value are respectively less than or close equal to all continuums of first area threshold
And at a nuclear area.
In one embodiment, nucleus determining module 71 can be further configured to:
Direction with orthogonal two boundaries in present viewing field region 1 is respectively that reference axis establishes coordinate system (such as Fig. 1
It is shown)；When at least there is a point in certain nucleus region 3 in reference axis, can abandon to the nuclear area
Subsequent processing (cell C as shown in Figure 1), again choose a nuclear area, avoid because cell not exclusively due to cause to know
Not inaccurate problem.
In one embodiment, nucleus determining module 71 can also be further configured to: directly by present viewing field region 1
Image inputs third nerve network model, determines the nuclear area in present viewing field region 1 by third nerve network model
3.The accuracy that can effectively improve determining nuclear area by a large amount of sample training avoids making because dyeing effect is bad
The problem of at the certainty reduction for determining nuclear area.
Optionally, third nerve network model can be convolutional neural networks model.
Optionally, region to be split can be centered on the center of gravity of nuclear area 3.Small as far as possible includes cell in order to obtain
The center in the region to be split of core region, region to be split can be set as the center of gravity of nuclear area, i.e., region to be split is
Equal proportion expands on the basis of nuclear area.
Optionally, the area in region to be split can be 10%-15% bigger than the area of nuclear area.According to usual cell
Area and its nucleus area corresponding relationship, the area in region to be split can be set to the face than nuclear area
The big 10%-15% of product, to ensure that region to be split includes nuclear area.
Figure 15 show a kind of structural schematic diagram of cellular morphology intellectual analysis device of one embodiment of the invention offer.Such as
Shown in Figure 15, which includes:
Module 81 is chosen, is configured to choose the present viewing field region in blood sample image；Cell compartment extraction module 82,
Be configured to extract include individual cells image cell compartment image, wherein individual cells image includes nuclear area and thin
Cytoplasmic region；And cell type identification module 83, it is configured to cell compartment image inputting nervus opticus network model, pass through
The type of nervus opticus network model identification individual cells, wherein cell compartment extraction module 82 includes that cell boundaries determine mould
Block 73.
It is realized by the method for artificial intelligence and extracts leucocyte from blood automatically and identify its type, to count bleeding
The quantity of all kinds of leucocytes in liquid.Existing artificial range estimation is replaced using artificial intelligence, can significantly improve working efficiency,
Also, artificial intelligence is under the support of a large amount of sample data, accuracy and consistency with higher, is diagnosis disease
Improve accurately and effectively data foundation.
In one embodiment, choosing module 81 can be further configured to:
Present viewing field region 1 can be the blood sample on glass slide and scatter the middle section part at direction both ends；Preferably, in
The width of section part is that blood sample scatters the 1/3 of width.Blood sample can cause to dissipate when scattering on glass slide because of extruding
Evolution to initial segment cell quantity is less and the accumulation of the cell quantity of concluding paragraph is more, and the cell quantity of middle section part is suitable
In and scatter more uniformly, therefore, middle portion can be chosen and be allocated as preferably reflecting blood for present viewing field region 1
Cell distribution situation in sample.
Present viewing field region 1 in blood sample image can also be by successively choosing one in multiple area of visual field work
For present viewing field region 1.Directly present viewing field region will can be chosen after the blood sample image scanning under microscope or first select
It takes present viewing field region to scan the image in selected present viewing field region again, and present viewing field area image is analyzed；?
It can will choose blood sample and be divided into multiple area of visual field, successively choose one in multiple area of visual field as present viewing field
Region, and the area image of present viewing field is analyzed, the result of multiple area of visual field is aggregated to form to final analysis knot
Fruit.By the way that blood sample image is divided into multiple area of visual field, it is possible to reduce the data volume analyzed every time reduces the application
Requirement of the method to realization device.
Preferably, in order to preferably realize the analyses of multiple area of visual field, coordinate mark can be carried out to multiple field-of-view images
Note, one successively chosen in multiple area of visual field according to coordinate label are used as present viewing field region 1.
Optionally, length of the overlapping region of two adjacent area of visual field on the moving direction of the visual field is greater than preset length
Threshold value；Preferably, pre-set length threshold is 4-5 microns.The diameter of usual leucocyte is 4-5 microns, therefore, is in order to prevent
The omission of cell between two adjacent area of visual field, the overlapping region of two adjacent area of visual field at least can include one
A cell, i.e., length of the overlapping region of two adjacent area of visual field on the moving direction of the visual field are greater than the straight of a cell
Diameter.
In one embodiment, cell type identification module 83 can be further configured to:
Other images in the cell compartment image 4 being partitioned into addition to individual cells image 2 are deleted, other figures will be deleted
The cell compartment image 4 of picture inputs nervus opticus network model.By its in cell compartment image 4 in addition to individual cells image
His image-erasing is the interference in order to exclude other images to the type of identification individual cells, to improve the accuracy of identification
And the efficiency of identification.
In one embodiment, cell type identification module 83 may include: the value of the confidence computing unit, for calculating separately list
A cell is the value of the confidence of various cell types, and the type of individual cells is determined according to the value of the confidence.To each individual cells, divide
The value of the confidence that it is various cell types is not calculated, and the type of individual cells is determined according to each the value of the confidence.
Optionally, cell type identification module 83 may further include: type determining units, for working as maximum confidence
When value is greater than default confidence threshold, determine that individual cells are type corresponding to maximum the value of the confidence.Preset confidence threshold
(such as can be set to 90%-95%), after calculating each the value of the confidence, if maximum the value of the confidence is greater than the confidence threshold
When, then it is assumed that determine that the individual cells are the corresponding cell type of maximum the value of the confidence substantially.
Optionally, cell type identification module 83 further comprises: the value of the confidence marking unit, for working as maximum the value of the confidence
When less than or equal to default confidence threshold, maximum at least two type of the value of the confidence and corresponding the value of the confidence can be marked.When most
When big the value of the confidence is less than or equal to default confidence threshold, then it is assumed that the type of the individual cells is unable to judge accurately, and will be set
Maximum at least two type of letter value and corresponding the value of the confidence are marked, and by manually determining, avoid judging by accident and influencing finally
Statistical result.
Preferably, the value of the confidence marking unit is also configured as: marking the value of the confidence maximum at least two with different colours
Seed type and corresponding the value of the confidence.Cell type and the value of the confidence are marked by different colours, more convenient can manually be checked.
Figure 16 show another embodiment of the present invention provides a kind of cellular morphology intellectual analysis device structural schematic diagram.
As shown in figure 16, which can also include:
Duplicate checking module 84 is configured to the multiple cell compartment images being overlapped merging into a cell compartment image.When point
When there are multiple cell compartment picture registrations in the cell compartment cut out, then illustrate that multiple cell compartment image is included multiple
Cell is a cell, wherein the nucleus of the cell is leaflet core, then merges into multiple cell compartment image one thin
Born of the same parents' area image prevents from repeating to count the cell and cause final statistical result.
Figure 17 show another embodiment of the present invention provides a kind of cellular morphology intellectual analysis device structural schematic diagram.
As shown in figure 17, which can also include:
Nervus opticus network model establishes module 85, is configured to will to protect the first sample image of nuclear area to input the
One neural network model determines the cell boundaries comprising nuclear area by first nerves network model, is determined carefully with extracting
Second sample image on born of the same parents boundary；And nervus opticus net is added using the second sample image and corresponding cell type as sample
In the training set of network model.
In one embodiment, nervus opticus network model is established module 85 and can also be further configured to:
Delete other images in the second sample image in addition to individual cells image；And the of other images will be deleted
Two sample images and corresponding cell type are added in the training set of nervus opticus network model as sample.
By other image-erasings in the second sample image in addition to individual cells image, and will delete except other images
Second sample image and corresponding cell type are added in the training set of nervus opticus network model as sample, eliminate other
Interference of the image to the type of identification individual cells, to improve the accuracy of identification and the efficiency of identification.
In one embodiment, nervus opticus network model is established module 85 and can also be further configured to:
The individual cells area image in the second sample image is marked with different colors；And color will be marked
Second sample image and corresponding cell type are added in the training set of nervus opticus network model as sample.
The individual cells area image in the second sample image is marked using different colors, can clearly be distinguished different
Boundary between individual cells region (the individual cells region especially closed on), avoids interfering with each other, to improve identification
Accuracy.
Optionally, nervus opticus network model can be convolutional neural networks model.
Figure 18 show another embodiment of the present invention provides a kind of cellular morphology intellectual analysis device structural schematic diagram.
As shown in figure 18, which can also include:
First nerves network model establishes module 86, and being configured to will be comprising the first sample image and correspondence of nuclear area
Determination cell boundaries the second sample image as sample be added first nerves network model training set in；And by first
Nervus opticus network model is added as sample in the second sample image and corresponding cell type that neural network model extracts
In training set.
By establishing first nerves network model and nervus opticus network model, the segmentation of individual cells and identification are distinguished
It is completed by first nerves network model and nervus opticus network model, reduces the complexity and meter of each neural network model
Calculation amount, to can also improve the efficiency of analysis.
Optionally, first nerves network model can be convolutional neural networks model.
Figure 19 show the structural schematic diagram of the electronic equipment of one embodiment of the invention offer.As shown in figure 19, the electronics
Equipment can be the online electronic equipment of such as medicine detector device equipped with cell type analytical equipment etc thereon, can also
Capable of being communicated with online electronic equipment to transmit the offline electronic equipment of trained machine learning model to it.
Figure 19 illustrates the block diagram of the electronic equipment according to the embodiment of the present application.
As shown in figure 19, electronic equipment 9 includes one or more processors 91 and memory 92.
Processor 91 can be central processing unit (CPU) or have data-handling capacity and/or instruction execution capability
Other forms processing unit, and can control the other assemblies in electronic equipment 9 to execute desired function.
Memory 92 may include one or more computer program products, and the computer program product may include each
The computer readable storage medium of kind form, such as volatile memory and/or nonvolatile memory.The volatile storage
Device for example may include random access memory (RAM) and/or cache memory (cache) etc..It is described non-volatile to deposit
Reservoir for example may include read-only memory (ROM), hard disk, flash memory etc..It can be deposited on the computer readable storage medium
One or more computer program instructions are stored up, processor 91 can run described program instruction, to realize this Shen described above
The area marking method of each embodiment please and/or other desired functions.In the computer readable storage medium
In can also store the various contents such as blood sample image information, location information, target area, training sample.
In one example, electronic equipment 9 can also include: input unit 93 and output device 94, these components pass through
The interconnection of bindiny mechanism's (not shown) of bus system and/or other forms.
For example, the input unit 93 can be image device, for acquiring blood sample image information, acquired image
Information can be stored in memory 92 for other assemblies use.It is of course also possible to using other it is integrated or it is discrete at
The blood sample image information is acquired as device, and is sent to electronic equipment 9.In addition, the input equipment 93 may be used also
To include such as keyboard, mouse and communication network and its remote input equipment connected etc..
The output device 94 can export various information, including determination to external (for example, user or machine learning model)
Cell type, cell quantity, training sample out etc..The output equipment 94 may include such as display, loudspeaker, printing
Machine and communication network and its remote output devices connected etc..
Certainly, to put it more simply, illustrated only in Figure 19 it is some in component related with the application in the electronic equipment 9,
The component of such as bus, input/output interface etc. is omitted.In addition to this, according to concrete application situation, electronic equipment 9 is also
It may include any other component appropriate.
Other than the above method and equipment, embodiments herein can also be computer program product comprising meter
Calculation machine program instruction, the computer program instructions execute the processor in this specification to retouch
The step in the area marking method according to the various embodiments of the application stated.
The computer program product can be write with any combination of one or more programming languages for holding
The program code of row the embodiment of the present application operation, described program design language includes object oriented program language, such as
Java, C++ etc. further include conventional procedural programming language, such as " C " language or similar programming language.Journey
Sequence code can be executed fully on the user computing device, partly execute on a user device, be independent soft as one
Part packet executes, part executes on a remote computing or completely in remote computing device on the user computing device for part
Or it is executed on server.
In addition, embodiments herein can also be computer readable storage medium, it is stored thereon with computer program and refers to
It enables, it is described in this specification according to this that the computer program instructions execute the processor
Apply for the step in the area marking method of various embodiments.
The computer readable storage medium can be using any combination of one or more readable mediums.Readable medium can
To be readable signal medium or readable storage medium storing program for executing.Readable storage medium storing program for executing for example can include but is not limited to electricity, magnetic, light, electricity
Magnetic, the system of infrared ray or semiconductor, device or device, or any above combination.Readable storage medium storing program for executing it is more specific
Example (non exhaustive list) includes: the electrical connection with one or more conducting wires, portable disc, hard disk, random access memory
Device (RAM), read-only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc
Read-only memory (CD-ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.
The basic principle of the application is described in conjunction with specific embodiments above, however, it is desirable to, it is noted that in this application
The advantages of referring to, advantage, effect etc. are only exemplary rather than limitation, must not believe that these advantages, advantage, effect etc. are the application
Each embodiment is prerequisite.In addition, detail disclosed above is merely to exemplary effect and the work being easy to understand
With, rather than limit, it is that must be realized using above-mentioned concrete details that above-mentioned details, which is not intended to limit the application,.
Device involved in the application, device, equipment block diagram only as illustrative example and be not intended to require
Or hint must be attached in such a way that box illustrates, arrange, configure.As the skilled person will recognize,
It can be connected by any way, arrange, configure these devices, device, equipment, system.Such as "include", "comprise", " having "
Etc. word be open vocabulary, refer to " including but not limited to ", and can be used interchangeably with it.Vocabulary "or" used herein above
Refer to vocabulary "and/or" with "and", and can be used interchangeably with it, unless it is not such that context, which is explicitly indicated,.It is used herein above
Vocabulary " such as " refers to phrase " such as, but not limited to ", and can be used interchangeably with it.
It may also be noted that each component or each step are can to decompose in the device of the application, device and method
And/or reconfigure.These decompose and/or reconfigure the equivalent scheme that should be regarded as the application.
The above description of disclosed aspect is provided so that any person skilled in the art can make or use this
Application.Various modifications in terms of these are readily apparent to those skilled in the art, and are defined herein
General Principle can be applied to other aspect without departing from scope of the present application.Therefore, the application is not intended to be limited to
Aspect shown in this, but according to principle disclosed herein and the consistent widest range of novel feature.
In order to which purpose of illustration and description has been presented for above description.In addition, this description is not intended to the reality of the application
It applies example and is restricted to form disclosed herein.Although multiple embodiment aspect and embodiment already discussed above, this field
Its certain modifications, modification, change, addition and sub-portfolio will be recognized in technical staff.
Claims (10)
1. a kind of cellular morphology intelligent extract method characterized by comprising
Determine the nuclear area in blood sample image；
Determine the region to be split comprising the nuclear area；And
The image in the region to be split is inputted into first nerves network model, is determined and is wrapped by the first nerves network model
Cell boundaries containing the nuclear area.
2. the method according to claim 1, wherein the establishment step of the first nerves network model includes:
Using the first sample image comprising the nuclear area and the second sample image of corresponding determining cell boundaries as
Sample is added in the training set of the first nerves network model.
3. the method according to claim 1, wherein the nuclear area packet in the determining blood sample image
It includes:
The continuum that selection meets pre-set color condition is the nuclear area.
4. the method according to claim 1, wherein the nuclear area packet in the determining blood sample image
It includes:
Select the continuum of same color；
Judge whether the continuum meets pre-set color condition；
When the continuum meets the pre-set color condition, judge whether the area of the continuum is greater than the first face
It accumulates threshold value and is less than second area threshold value；And
When the area of the continuum is greater than the first area threshold and is less than second area threshold value, the continuum is chosen
For the nuclear area.
5. according to the method described in claim 4, it is characterized in that, the nuclear area in the determining blood sample image also
Include:
When the area of the continuum is greater than or equal to the second area threshold value, the continuum is divided into multiple
The nuclear area.
6. according to the method described in claim 5, it is characterized in that, described be divided into multiple cells for the continuum
Core region includes:
By the image of continuum described in scaled down, the continuum is divided into multiple nuclear areas.
7. according to the method described in claim 4, it is characterized in that, the nuclear area in the determining blood sample image also
Include:
When the area of the continuum is less than or equal to first area threshold, judgement is pre- comprising the continuum
If meeting the pre-set color condition and area with the presence or absence of other within the scope of third area threshold is respectively less than or is equal to described the
The continuum of one area threshold；
If it exists other meet the pre-set color condition and area be respectively less than or equal to first area threshold continuum
Meet the pre-set color condition and area within the scope of the default third area threshold is respectively less than or is equal to described the by domain
All continuums of one area threshold are merged into the nuclear area.
8. the method according to claim 1, wherein the nuclear area in the determining blood sample image is also
Include:
Direction with orthogonal two boundaries in present viewing field region is respectively that reference axis establishes coordinate system；
When at least there is a point in the determining nuclear area in the reference axis, abandon the nucleus
The subsequent processing in region.
9. a kind of cellular morphology intelligent analysis method characterized by comprising
Choose the present viewing field region in blood sample image；
The cell compartment image including individual cells image is extracted, wherein the individual cells image includes the nuclear area
The cytosolic domain and；And
The cell compartment image is inputted into nervus opticus network model, it is single thin by nervus opticus network model identification
The type of born of the same parents；
Wherein, it extracts the cell compartment image including individual cells image and uses such as side described in any item of the claim 1 to 8
Method.
10. a kind of cellular morphology intelligent extraction device characterized by comprising
Nucleus determining module is configured to determine the nuclear area in blood sample image；
Area determination module to be split is configured to determine the region to be split comprising the nuclear area；And
Cell boundaries determining module is configured to the image in the region to be split inputting first nerves network model, passes through institute
It states first nerves network model and determines the cell boundaries comprising the nuclear area.
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Application publication date: 20190322 |