CN110136775A - A kind of cell division and anti-interference detection system and method - Google Patents
A kind of cell division and anti-interference detection system and method Download PDFInfo
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- 230000032823 cell division Effects 0.000 title claims abstract description 82
- 238000001514 detection method Methods 0.000 title claims abstract description 49
- 238000000034 method Methods 0.000 title claims abstract description 32
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 35
- 239000000284 extract Substances 0.000 claims abstract description 13
- 230000002068 genetic effect Effects 0.000 claims abstract description 5
- 238000012360 testing method Methods 0.000 claims abstract description 5
- 210000004027 cell Anatomy 0.000 claims description 35
- 238000001914 filtration Methods 0.000 claims description 25
- 238000000926 separation method Methods 0.000 claims description 25
- 238000012545 processing Methods 0.000 claims description 14
- 230000003044 adaptive effect Effects 0.000 claims description 11
- 210000002569 neuron Anatomy 0.000 claims description 9
- 238000000605 extraction Methods 0.000 claims description 8
- 238000004113 cell culture Methods 0.000 claims description 6
- 238000005260 corrosion Methods 0.000 claims description 6
- 230000007797 corrosion Effects 0.000 claims description 6
- 230000005284 excitation Effects 0.000 claims description 6
- 230000004913 activation Effects 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 5
- 230000004992 fission Effects 0.000 claims description 4
- 102000014150 Interferons Human genes 0.000 claims description 3
- 108010050904 Interferons Proteins 0.000 claims description 3
- 241000907316 Zika virus Species 0.000 claims description 3
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 210000000170 cell membrane Anatomy 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 3
- 230000010339 dilation Effects 0.000 claims description 3
- 238000006073 displacement reaction Methods 0.000 claims description 3
- 230000003628 erosive effect Effects 0.000 claims description 3
- 229940079322 interferon Drugs 0.000 claims description 3
- 230000000877 morphologic effect Effects 0.000 claims description 3
- 238000003062 neural network model Methods 0.000 claims description 3
- 230000008775 paternal effect Effects 0.000 claims description 3
- 230000011664 signaling Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 2
- 238000007689 inspection Methods 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 5
- 238000009395 breeding Methods 0.000 description 2
- 230000001488 breeding effect Effects 0.000 description 2
- 210000000130 stem cell Anatomy 0.000 description 2
- 230000004663 cell proliferation Effects 0.000 description 1
- 210000000805 cytoplasm Anatomy 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
Abstract
The invention belongs to technical field of biological, disclose a kind of cell division and anti-interference detection system and method, by taking ontology, carry out genetic test, determine optimal cell division jamming program；It extracts division and replicates corresponding un-mixing bases because carrying out gene compatibility and biological rejection detection by division duplication un-mixing bases because biological microcomputer is added；Detect it is errorless after, again be implanted into ontology body in is normally cultivated, control frequency dividing cell；Cell is counted after being interfered, with the difference of period inner cell division number or the difference of cell total amount；Calculate the anti-interference ability of cell.Our religious name, which preferably resolves currently available technology, can influence that fissional factor is many and extremely complex, not reach the horizontal problem to its full appreciation also at present.
Description
Technical field
The invention belongs to technical field of biological more particularly to a kind of cell division and anti-interference detection system and sides
Method.
Background technique
Currently, the immediate prior art:
Cell division (cell division) is mistake of its number of Living cells proliferation by a cell division for two cells
Journey.Cell before division claims mother cell, and the neoblast formed after division claims daughter cell.Generally comprise nuclear division and cytoplasm
Divide two steps.Inhereditary material is transmitted to daughter cell by mother cell during nuclear fission.Cell division is exactly in unicellular organism
The breeding of individual, cell division is individual growth, development and the basis of breeding in multicellular organism.
It can influence that fissional factor is many and extremely complex, not reach the water to its full appreciation also at present
It is flat.
In conclusion problem of the existing technology is: it is many that currently available technology can influence fissional factor,
And it is extremely complex, do not reach the level to its full appreciation also at present.
Existing biology microcomputer carries out obtaining image and other data in gene compatibility and biological rejection detection
Information accuracy is poor.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of cell division and anti-interference detection system and sides
Method.
The invention is realized in this way a kind of cell division and anti interference detection method, the anti-interference inspection of cell division
Survey method includes:
It extracts division and replicates corresponding un-mixing bases because carrying out base by division duplication un-mixing bases because biological microcomputer is added
Because of compatibility and biological rejection detection；
Division duplication separation gene data image information is passed through adaptive by the lesser pulse biology rejection sex differernce of density
Weighted filtering is answered to handle；Division duplication separation gene data image information is adopted by the biggish pulse biology rejection sex differernce of density
Secondary filtering is carried out with the introducing binode constitutive element mathematical morphology of holding edge detail information；
It is suitble to processing division duplication separation gene data iconic model:
Fij[n]=Sij；
Uij[n]=Fij[n](1+βij[n]Lij[n])；
θij[n]=θ0e-αθ(n-1)；
Wherein, βij[n] is adaptive link strength factor；
Sij、Fij[n]、Lij[n]、Uij[n]、θij[n] is respectively received image signal, feed back input, link input, inside
Active entry and dynamic threshold, NwFor the sum of all pixels in selected window W to be processed, Δ is adjustment factor, chooses 1~3；
In division duplication separation gene data image information adaptive weighting filter biology rejection filtering, when pulse is defeated
Y outij=1 and NY=1~8, NYIt is to work as in 3*3 template B for 1 number, filter window M is chosen, to biological rejection disparity map
As fijAdaptive-filtering, filtering equations are as follows:
In formula, xrsIt is the coefficient of respective pixel in filter window, SrsFor the gray value of respective pixel in filter window, fij
To correspond to the output valve of window center position after filtering:
D in formulaijFor pixel grey scale intermediate value in box filter window M, ΩijEach pixel of filter window and center gray scale difference are exhausted
To mean value, max is maximizing symbol.
Further, the cell division anti interference detection method specifically includes:
Step 1 takes ontology, carries out genetic test, determines optimal cell division jamming program；
Step 2, extract division replicate corresponding un-mixing bases because, will division duplication un-mixing bases because biological microcomputer is added,
Carry out gene compatibility and biological rejection detection；
Step 3 after detection is errorless, is implanted into ontology body again and is normally cultivated, controls frequency dividing cell；
Step 4 counts cell after being interfered, with the difference or cell total amount of period inner cell division number
Difference；
Step 5 calculates the anti-interference ability of cell.
Further, when Pulse-coupled Neural Network Model detects division duplication separation gene data image information,
Make gray scale S using network characteristicij maxPixel light a fire activation, then carry out second of Pulse Coupled Neural Network iterative processing,
Between [Sij max/1+βijLij,Sij max] between pixel capture activation, make the corresponding Y of the pixel activated twiceijOutput is
1；Then processing highlighted to protozoa rejection differential image, then to treated image SijProcessing is iterated by aforementioned, and
Make corresponding output Yij=1, gray scale difference big characteristic small using image biological rejection pixel and surrounding pixel correlation, when
As soon as the excitation of neuron does not cause the excitation of most of neurons near region, illustrate that the neuron corresponds to picture
Element may be biological rejection point；
Tentatively screen out Yij=0 corresponding pixel is the signaling point of division duplication separation gene data image information, is given
With protection；To YijOutput counts for 1 pixel within the scope of 3*3 template B to export Yij=1 be center neighborhood element value is 1
Number NYDifferentiate and sort out: 1≤NY≤ 8, for biological rejection point, work as NY=9, it is determined as image slices vegetarian refreshments.
Further, the specific method of binode constitutive element mathematical morphology second level filtering:
The division duplication separation gene data image information of residual impulse biology rejection is f, and E is structural element SE, then
Expansion has following relational expression:
In formulaFor dilation operation symbol, F and G are the domain of f and E respectively, and x-z is displacement parameter；
Above formula extension relationship is all to be merged into all background dots contacted with object in object, expands boundary to outside
Process, fill up the hole in object；
Above formulaFor erosion operation symbol, corrosion is to eliminate boundary point, and boundary is internally shunk, while in the base of corrosion expansion
On plinth, in conjunction with morphologic opening and closing operation:
Further, the cell division jamming program has interferon, zika virus.
Further, the cell division time when needing untreated to the cell before carrying out the anti-interference detection of cell division has one
Fixed assurance needs to carry out cell division detection to selected cell.
Further, the cell division detection method includes that the side of feature modeling is carried out based on fissional image-region
Method, the method for cell division recognition sequence based on feature extraction.
Further, the method for carrying out feature modeling based on fissional image-region specifically includes:
A kind of cell is divided into multiple contrast groups, image is constantly obtained in cell cultivation process by step 1；
Step 2, according to the continuous image sequence of image construction that certain time interval obtains, to institute in image sequence
Some fission process carry out judgement comparison；
Step 3 marks the position that can obviously observe paternal cell in cell division and be split into two sub- cell membranes
Note, constitutes the data set of mark；
Step 4, associative function dispose trained model, the cell for similar type cell culture data
The automatic detection of division.
Further, the method for the cell division recognition sequence based on feature extraction specifically includes:
Step 1 obtains the first cell division based on the method for cell division region notable feature and space time correlation characteristic and waits
Favored area, and extract cell division candidate sequence；
Step 2 is described first cell division candidate region by histograms of oriented gradients, passes through feature
It extracts and converts characteristic vector sequence for the cell division candidate sequence；
Step 3 realizes cell division sequence by the study and deduction of hidden condition random model according to characteristic vector sequence
Column identification.
It is anti-that another object of the present invention is to provide a kind of cell division for implementing the cell division anti interference detection method
Interference Detection system.
In conclusion advantages of the present invention and good effect are as follows: the present invention carries out genetic test, determine by taking ontology
Optimal cell division jamming program；It extracts division and replicates corresponding un-mixing bases because division duplication un-mixing bases are miniature because biology is added
Computer carries out gene compatibility and biological rejection detection；Detect it is errorless after, again be implanted into ontology body in is normally trained
It supports, controls frequency dividing cell；Cell is counted after being interfered, with the difference or cell of period inner cell division number
The difference of total amount；Calculate the anti-interference ability of cell.Preferably resolve currently available technology can influence it is fissional because
It is plain many and extremely complex, do not reach the horizontal problem to its full appreciation also at present.
The present invention extract division replicate corresponding un-mixing bases because, will division duplication un-mixing bases because biological microcomputer is added,
Carry out gene compatibility and biological rejection detection；
Division duplication separation gene data image information is passed through adaptive by the lesser pulse biology rejection sex differernce of density
Weighted filtering is answered to handle；Division duplication separation gene data image information is adopted by the biggish pulse biology rejection sex differernce of density
Secondary filtering is carried out with the introducing binode constitutive element mathematical morphology of holding edge detail information；It is suitble to processing division duplication separation
Gene data iconic model:
Fij[n]=Sij；
Uij[n]=Fij[n](1+βij[n]Lij[n])；
θij[n]=θ0e-αθ(n-1)；
Wherein, βij[n] is adaptive link strength factor；
Sij、Fij[n]、Lij[n]、Uij[n]、θij[n] is respectively received image signal, feed back input, link input, inside
Active entry and dynamic threshold, NwFor the sum of all pixels in selected window W to be processed, Δ is adjustment factor, chooses 1~3；
In division duplication separation gene data image information adaptive weighting filter biology rejection filtering, when pulse is defeated
Y outij=1 and NY=1~8, NYIt is to work as in 3*3 template B for 1 number, filter window M is chosen, to biological rejection disparity map
As fijAdaptive-filtering, filtering equations are as follows:
In formula, xrsIt is the coefficient of respective pixel in filter window, SrsFor the gray value of respective pixel in filter window, fij
To correspond to the output valve of window center position after filtering:
D in formulaijFor pixel grey scale intermediate value in box filter window M, ΩijEach pixel of filter window and center gray scale difference are exhausted
To mean value, max is maximizing symbol.It can be achieved to obtain image and other numbers in gene compatibility and biological rejection detection
According to information accuracy height, accuracy improves nearly 6 percentage points compared with the prior art, up to 98% or so.
Detailed description of the invention
Fig. 1 is cell division anti interference detection method flow chart provided in an embodiment of the present invention.
Fig. 2 is the method flow provided in an embodiment of the present invention that feature modeling is carried out based on fissional image-region
Figure.
Fig. 3 is the method flow diagram of the cell division recognition sequence provided in an embodiment of the present invention based on feature extraction.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to
Limit the present invention.
Application principle of the invention is described in detail with reference to the accompanying drawing.
As shown in Figure 1, cell division anti interference detection method provided in an embodiment of the present invention includes:
S101: taking ontology, carries out genetic test, determines optimal cell division jamming program；
S102: extract division replicate corresponding un-mixing bases because, will division duplication un-mixing bases because biological microcomputer is added, into
Row gene compatibility and biological rejection detection；
S103: it after detection is errorless, is implanted into ontology body and is normally cultivated again, control frequency dividing cell；
S104: statistics cell is after being interfered, with the difference or cell total amount of period inner cell division number
Difference；
S105: the anti-interference ability of cell is calculated.
As the preferred embodiment of the present invention.The cell division jamming program has interferon, zika virus etc..
As the preferred embodiment of the present invention.It is thin when needing untreated to the cell before carrying out the anti-interference detection of cell division
Born of the same parents' splitting time has certain assurance, needs to carry out cell division detection to selected cell.
As the preferred embodiment of the present invention.The cell division detection method include based on fissional image-region into
The method of row feature modeling, method of cell division recognition sequence based on feature extraction etc..
As the preferred embodiment of the present invention.The method for carrying out feature modeling based on fissional image-region is specific
Include:
S201: a kind of cell is divided into multiple contrast groups, image is constantly obtained in cell cultivation process；
S202: the continuous image sequence of image construction obtained according to certain time interval owns in image sequence
Fission process carry out judgement comparison；
S203: being labeled the position that can obviously observe paternal cell in cell division and be split into two sub- cell membranes,
Constitute the data set of mark；
S204: associative function disposes trained model, the cell point for similar type cell culture data
Split automatic detection.
Further, the method for the cell division recognition sequence based on feature extraction specifically includes:
S301: it is candidate that the first cell division is obtained based on the method for cell division region notable feature and space time correlation characteristic
Region, and extract cell division candidate sequence；
S302: first cell division candidate region is described by histograms of oriented gradients, is mentioned by feature
It takes and converts characteristic vector sequence for the cell division candidate sequence；
S303: according to characteristic vector sequence, cell division sequence is realized by the study and deduction of hidden condition random model
Identification.
In embodiments of the present invention, it extracts division and replicates corresponding un-mixing bases because by division duplication un-mixing bases because biology is added
Microcomputer carries out gene compatibility and biological rejection detection；
Division duplication separation gene data image information is passed through adaptive by the lesser pulse biology rejection sex differernce of density
Weighted filtering is answered to handle；Division duplication separation gene data image information is adopted by the biggish pulse biology rejection sex differernce of density
Secondary filtering is carried out with the introducing binode constitutive element mathematical morphology of holding edge detail information；
It is suitble to processing division duplication separation gene data iconic model:
Fij[n]=Sij；
Uij[n]=Fij[n](1+βij[n]Lij[n])；
θij[n]=θ0e-αθ(n-1)；
Wherein, βij[n] is adaptive link strength factor；
Sij、Fij[n]、Lij[n]、Uij[n]、θij[n] is respectively received image signal, feed back input, link input, inside
Active entry and dynamic threshold, NwFor the sum of all pixels in selected window W to be processed, Δ is adjustment factor, chooses 1~3；
In division duplication separation gene data image information adaptive weighting filter biology rejection filtering, when pulse is defeated
Y outij=1 and NY=1~8, NYIt is to work as in 3*3 template B for 1 number, filter window M is chosen, to biological rejection disparity map
As fijAdaptive-filtering, filtering equations are as follows:
In formula, xrsIt is the coefficient of respective pixel in filter window, SrsFor the gray value of respective pixel in filter window, fij
To correspond to the output valve of window center position after filtering:
D in formulaijFor pixel grey scale intermediate value in box filter window M, ΩijEach pixel of filter window and center gray scale difference are exhausted
To mean value, max is maximizing symbol.
Further, when Pulse-coupled Neural Network Model detects division duplication separation gene data image information,
Make gray scale S using network characteristicij maxPixel light a fire activation, then carry out second of Pulse Coupled Neural Network iterative processing,
Between [Sij max/1+βijLij,Sij max] between pixel capture activation, make the corresponding Y of the pixel activated twiceijOutput is
1；Then processing highlighted to protozoa rejection differential image, then to treated image SijProcessing is iterated by aforementioned, and
Make corresponding output Yij=1, gray scale difference big characteristic small using image biological rejection pixel and surrounding pixel correlation, when
As soon as the excitation of neuron does not cause the excitation of most of neurons near region, illustrate that the neuron corresponds to picture
Element may be biological rejection point；
Tentatively screen out Yij=0 corresponding pixel is the signaling point of division duplication separation gene data image information, is given
With protection；To YijOutput counts for 1 pixel within the scope of 3*3 template B to export Yij=1 be center neighborhood element value is 1
Number NYDifferentiate and sort out: 1≤NY≤ 8, for biological rejection point, work as NY=9, it is determined as image slices vegetarian refreshments.
The specific method of binode constitutive element mathematical morphology second level filtering:
The division duplication separation gene data image information of residual impulse biology rejection is f, and E is structural element SE, then
Expansion has following relational expression:
In formulaFor dilation operation symbol, F and G are the domain of f and E respectively, and x-z is displacement parameter；
Above formula extension relationship is all to be merged into all background dots contacted with object in object, expands boundary to outside
Process, fill up the hole in object；
Above formulaFor erosion operation symbol, corrosion is to eliminate boundary point, and boundary is internally shunk, while in the base of corrosion expansion
On plinth, in conjunction with morphologic opening and closing operation:
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.
Claims (10)
1. a kind of cell division and anti interference detection method, which is characterized in that the cell division anti interference detection method includes:
It extracts division and replicates corresponding un-mixing bases because it is simultaneous to carry out gene by division duplication un-mixing bases because biological microcomputer is added
Capacitive and biological rejection detection；
Duplication separation gene data image information is divided by the lesser pulse biology rejection sex differernce of density by adaptively adding
Power filtering processing；Division duplication separation gene data image information is used by the biggish pulse biology rejection sex differernce of density and is protected
The introducing binode constitutive element mathematical morphology for holding edge detail information carries out secondary filtering；
It is suitble to processing division duplication separation gene data iconic model:
Fij[n]=Sij；
Uij[n]=Fij[n](1+βij[n]Lij[n])；
θij[n]=θ0e-αθ(n-1)；
Wherein, βij[n] is adaptive link strength factor；
Sij、Fij[n]、Lij[n]、Uij[n]、θij[n] is respectively received image signal, feed back input, link input, internal activity
Item and dynamic threshold, NwFor the sum of all pixels in selected window W to be processed, Δ is adjustment factor, chooses 1~3；
In division duplication separation gene data image information adaptive weighting filter biology rejection filtering, when pulse exports Yij
=1 and NY=1~8, NYIt is to work as in 3*3 template B for 1 number, filter window M is chosen, to biological rejection differential image fij
Adaptive-filtering, filtering equations are as follows:
In formula, xrsIt is the coefficient of respective pixel in filter window, SrsFor the gray value of respective pixel in filter window, fijFor filter
The output valve of window center position is corresponded to after wave:
D in formulaijFor pixel grey scale intermediate value in box filter window M, ΩijEach pixel of filter window and center gray scale difference are absolutely equal
Value, max are maximizing symbol.
2. cell division anti interference detection method as described in claim 1, which is characterized in that the anti-interference detection of cell division
Method specifically includes:
Step 1 takes ontology, carries out genetic test, determines optimal cell division jamming program；
Step 2 extracts division and replicates corresponding un-mixing bases because carrying out by division duplication un-mixing bases because biological microcomputer is added
Gene compatibility and biological rejection detection；
Step 3 after detection is errorless, is implanted into ontology body again and is normally cultivated, controls frequency dividing cell；
Step 4 counts cell after being interfered, with the difference of period inner cell division number or the difference of cell total amount
Not；
Step 5 calculates the anti-interference ability of cell.
3. cell division anti interference detection method as described in claim 1, which is characterized in that Pulse-coupled Neural Network Model pair
When division duplication separation gene data image information is detected, make gray scale S using network characteristicijmaxPixel igniting swash
It is living, then second of Pulse Coupled Neural Network iterative processing is carried out, between [Sij max/1+βijLij,Sij max] between pixel catch
Activation is obtained, the corresponding Y of the pixel activated twice is madeijOutput is 1；Then processing is highlighted to protozoa rejection differential image,
Again to treated image SijIt is iterated processing by aforementioned, and makes corresponding output Yij=1, utilize image biological rejection
Pixel and surrounding pixel correlation are small, the big characteristic of gray scale difference, when the excitation of a neuron does not cause near region
When the excitation of most of neurons, just illustrate that the neuron respective pixel may be biological rejection point；
Tentatively screen out Yij=0 corresponding pixel is the signaling point of division duplication separation gene data image information, is protected
Shield；To YijOutput counts for 1 pixel within the scope of 3*3 template B to export Yij=1 is that center neighborhood element value is 1
Number NYDifferentiate and sort out: 1≤NY≤ 8, for biological rejection point, work as NY=9, it is determined as image slices vegetarian refreshments.
4. cell division anti interference detection method as described in claim 1, which is characterized in that binode constitutive element mathematical morphology
The specific method of secondary filter:
The division duplication separation gene data image information of residual impulse biology rejection is f, and E is structural element SE, then expands
There is following relational expression:
In formulaFor dilation operation symbol, F and G are the domain of f and E respectively, and x-z is displacement parameter；
Above formula extension relationship is all to be merged into all background dots contacted with object in object, makes boundary to the mistake of outside expansion
Journey fills up the hole in object；
Above formula Θ is erosion operation symbol, and corrosion is to eliminate boundary point, and boundary is internally shunk, while on the basis of corrosion expansion
On, in conjunction with morphologic opening and closing operation:
5. cell division anti interference detection method as claimed in claim 2, which is characterized in that the cell division jamming program has
Interferon, zika virus.
6. cell division anti interference detection method as claimed in claim 2, which is characterized in that carrying out the anti-interference inspection of cell division
Cell division time when needing untreated to the cell before survey has certain assurance, needs to carry out cell division to selected cell
Detection.
7. cell division anti interference detection method as claimed in claim 2, which is characterized in that the cell division detection method packet
Include the side of the method based on fissional image-region progress feature modeling, the cell division recognition sequence based on feature extraction
Method.
8. cell division anti interference detection method as claimed in claim 7, which is characterized in that described to be based on fissional image
The method that region carries out feature modeling specifically includes:
A kind of cell is divided into multiple contrast groups, image is constantly obtained in cell cultivation process by step 1；
Step 2, according to the continuous image sequence of image construction that certain time interval obtains, to all in image sequence
Fission process carries out judgement comparison；
Step 3 is labeled the position that can obviously observe paternal cell in cell division and be split into two sub- cell membranes, structure
At the data set of mark；
Step 4, associative function dispose trained model, the cell division for similar type cell culture data
Automatic detection.
9. cell division anti interference detection method as claimed in claim 7, which is characterized in that the cell based on feature extraction
The method of division recognition sequence specifically includes:
Step 1 obtains the first cell division candidate regions based on the method for cell division region notable feature and space time correlation characteristic
Domain, and extract cell division candidate sequence；
Step 2 is described first cell division candidate region by histograms of oriented gradients, passes through feature extraction
Characteristic vector sequence is converted by the cell division candidate sequence；
Step 3 realizes that cell division sequence is known by the study and deduction of hidden condition random model according to characteristic vector sequence
Not.
10. a kind of anti-interference detection system of cell division for implementing cell division anti interference detection method described in claim 1.
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| CN111666895A (en) * | 2020-06-08 | 2020-09-15 | 上海市同济医院 | Neural stem cell differentiation direction prediction system and method based on deep learning |
| CN112862742A (en) * | 2019-11-27 | 2021-05-28 | 静宜大学 | Artificial intelligent cell detection method and system using photodynamic technology |
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