CN106373116B - Cynapse detection method based on two-photon image - Google Patents
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Abstract
The invention discloses a kind of cynapse detection methods based on two-photon image, are related to pattern-recognition and neurology technical field.Wherein, this method includes obtaining the two-photon image；The two-photon image is divided into aixs cylinder image and dendron image；The 2D detection of aixs cylinder brief summary is carried out in the aixs cylinder image；The 2D detection of dendritic spines is carried out in the dendron image；2D cynapse detection is carried out with the aixs cylinder brief summary detected and dendritic spines；Based on 2D cynapse testing result, 3D cynapse detection is carried out.Through the invention, the efficiency and precision of detection cynapse are improved, also there is universality, and insensitive to image type.
Description
Technical field
The present invention relates to pattern-recognitions and neurology technical field, more particularly, to a kind of cynapse based on two-photon image
Detection method.
Background technique
A large number of studies show that the synaptic plasticity of neuron includes function plasticity and structure plasticity, with study and note
Recall closely related.Synaptic plasticity dysregulation is often thought of as the main reason for cognition, learning and memory dysfunction.Perhaps
More spiritual and cognitive disorder pathogenesis and the exception that synaptic plasticity is adjusted are closely related, the research adjusted to synaptic plasticity
Be conducive to disclose the pathomechanism of intellectual development sluggishness, this kind of neuropsychiatric disease of cognition dysfunction.
In recent years, using just setting Two Photon Fluorescence system and fluorescence probe labelling technique method always is the heat of research
Point.Existing detection cynapse method is exactly the searching cynapse of neurosurgeon one by one by naked eyes.This method expends a large amount of artificial
Time, and precision is not high, the case where easily omission.
In view of this, the present invention is specifically proposed.
The following are the prior arts related to the present invention:
[1]N.Dey,L.Blanc-Feraud,C.Zimmer,“Richardson–Lucy algorithm with
total variation regularization for 3D confocal microscope deconvolution,”
Microscopy research and technique,vol.69,no.4,pp.260-266,2006.
[2]J.E.Ledoux,“Emotion circuits in the brain,”Annual Review of
Neuroscience,vol.23,no.2,pp.155-184,2000.
[3]V.D.Paola,A.Holtmaat,G.Knott,et al.,“Cell Type-Specific Structural
Plasticity of Axonal Branches and Boutons in the Adult Neocortex,”Neuron,
vol.49,no.6,pp.861-875,2006.
[4]N.Becker,C.J.Wierenga,R.Fonseca,et al.,“LTD induction causes
morphological changes of presynaptic boutons and reduces their contacts with
spines,”Neuron,vol.60,no.4,pp.590-597,2008.
[5]F.Jing,X.Zhou,J.G.Dy,et al.,“An Automated Pipeline for Dendrite
Spine Detection and Tracking of 3D Optical Microscopy Neuron Images of In
Vivo Mouse Models,”Neuroinformatics,vol.7,no.2,pp.113-130,2009.
[6]H.Park and M.M.Poo,“Neurotrophin regulation of neural circuit
development and function,”Nature Reviews Neuroscience,vol.14,no.1,pp.7-23,
2013.
[7]F.W.Grillo,S.Song,L.M.Teles-Grilo,et al.,“Increased axonal bouton
dynamics in the aging mouse cortex,”Proceedings of the National Academy of
Sciences of the United States of America, vol.110, no.16, pp.1514-1523,2013.
[8]Y.Yang,D.Q.Liu,W.Huang,et al.,“Selective synaptic remodeling of
amygdalocortical connections associated with fear memory,”Neuroscience,to be
published.
Summary of the invention
The embodiment of the present invention provides a kind of cynapse detection method based on two-photon image, is dashed forward with solving how to improve detection
The problem of efficiency and precision of touching.
To achieve the goals above, the following technical schemes are provided:
A kind of cynapse detection method based on two-photon image, which comprises
Obtain the two-photon image；
The two-photon image is divided into aixs cylinder image and dendron image；
The 2D detection of aixs cylinder brief summary is carried out in the aixs cylinder image；
The 2D detection of dendritic spines is carried out in the dendron image；
2D cynapse detection is carried out with the aixs cylinder brief summary detected and dendritic spines；
Based on 2D cynapse testing result, 3D cynapse detection is carried out.
Further, the 2D detection that aixs cylinder brief summary is carried out in the aixs cylinder image, specifically includes:
To the 3D deconvolution processing that the aixs cylinder image is iterated, deconvolution image is obtained；
Determine the Local modulus maxima in the deconvolution image；
Based on the aixs cylinder image, enhancing image is determined；
Binary segmentation processing is carried out to the enhancing image；
Based on the Local modulus maxima and binary segmentation processing result, remove in the binary segmentation processing result not
Region including the Local modulus maxima.
Further, the Local modulus maxima in the determination deconvolution image specifically includes:
Set first threshold；
Find out the point that pixel value in the deconvolution image is greater than the doubtful local maximum of first threshold；
It is maximum for judging whether the point of each doubtful local maximum meets the pixel value in a predetermined neighborhood
, if it is, determining that the point of the doubtful local maximum is the Local modulus maxima.
Further, described to be based on the aixs cylinder image, determine that enhancing image specifically includes:
On the basis of two-dimentional xy coordinate system, it is based on the aixs cylinder image, determines the enhancing image according to the following formula:
Wherein, the g indicates the enhancing image；The fxIndicate the local derviation of the aixs cylinder image in the x direction；It is described
fyIndicate the local derviation of the aixs cylinder image in y-direction；The fxxIndicate the fxLocal derviation on the direction x；The fxy
Indicate the fxLocal derviation in said y direction；The fyyIndicate the fyLocal derviation in said y direction.
Further, which comprises the 2D detection that dendritic spines are carried out in the dendron image specifically includes:
The dendron image is normalized and structure enhancing is handled；
Skeletonizing structure enhances the dendron in processing result, obtains dendron skeleton binary map；
Based on the dendron skeleton binary map, the bifurcation on dendron skeleton is determined；
Based on the dendron skeleton binary map, and according to the bifurcation, the dendritic spines are positioned.
Further, it is described the dendron image is normalized and structure enhancing processing specifically include:
By the dendron image normalization between 0 and 1；
According to the following formula, dendritic spines INTERFACE MODEL is set:
Wherein, the θ indicates the angle of the dendritic spines；The σ indicates the width of the dendritic spines；F (x, the y) table
Show the dendritic spines INTERFACE MODEL；The x, the y respectively indicate cross, the ordinate of pixel in the image after normalization；
According to the cable architecture of image after drag enhancing normalization:
Further, the dendron in the skeletonizing structure enhancing processing result, obtains dendron skeleton binary map and specifically wraps
It includes:
Threshold segmentation is carried out to structure enhancing processing result, obtains dendron bianry image；
The dendron bianry image is traversed, deletes pixel according to the following conditions, until the dendron bianry image is no longer
Until variation, the first bianry image is obtained:
XH(p)=1；2≤min{n1(p),n2(p)}≤3；
Wherein, the p indicates pixel；X1, the x2 ..., x8 is the institute since the adjoint point of right side by inverse time needle sort
State 8 consecutive points pixels of p；
First bianry image is traversed, deletes pixel according to the following conditions, until the dendron bianry image is no longer
Until variation, the dendron skeleton binary map is obtained:
XH(q)=1；2≤min{n1(q),n2(q)}≤3；
Wherein, the q indicates pixel；The x1,x2,...,x8For the institute since the adjoint point of right side by inverse time needle sort
State 8 consecutive points pixels of q.
Further, described to be based on the dendron skeleton binary map, determine that the bifurcation on dendron skeleton specifically includes:
Construct filter；
The filter and the dendron skeleton binary map are subjected to process of convolution, and second threshold will be greater than in trellis diagram
Point be determined as bifurcation.
Further, described to be based on the dendron skeleton binary map, and according to the bifurcation, the dendritic spines are positioned,
It specifically includes:
The structural element of predetermined radii is constructed, and morphological erosion is carried out to the dendron skeleton binary map；
Skeleton drawing after subtracting corrosion with the dendron skeleton binary map, obtains dendritic spines alternate location；
Based on the dendron skeleton binary map, centered on the bifurcation, the region of predetermined side length is extracted；
The skeleton part of the bifurcation region is chosen, and judges whether there is the dendritic spines alternate location and is somebody's turn to do
Skeleton part is connected；If there is, it is determined that the dendritic spines alternate location is dendritic spines.
Further, it is described with the aixs cylinder brief summary detected and dendritic spines carry out 2D cynapse detection specifically include:
The aixs cylinder brief summary is represented with the central point of each aixs cylinder brief summary；
The dendritic spines are represented with the central point of the dendritic spines alternate location；
Calculate and record the minimum value of distance between each aixs cylinder brief summary central point and all dendritic spines central points；
If the minimum value of the distance is less than third threshold value, the aixs cylinder brief summary and the dendritic spines are determined as 2D and dashed forward
Touching.
Further, described to be based on 2D cynapse testing result, it carries out 3D cynapse detection and specifically includes:
It is represented with the midpoint of aixs cylinder brief summary central point and dendritic spines alternate location central point in the 2D cynapse described prominent
The position of touching；
The distance of the aixs cylinder brief summary central point described in the 2D cynapse and the dendritic spines alternate location central point is come generation
The authenticity of cynapse described in table；
The two-photon image sequence be sliced it is adjacent before and after in layer, coordinate in the neighborhood of the 2D synoptic sites
It is interior to search the cynapse；
Judge the authenticity of each cynapse the two-photon image sequence be sliced it is adjacent before and after whether be most in layer
High, if so, determining that the cynapse is 3D cynapse.
The embodiment of the present invention provides a kind of cynapse detection method based on two-photon image.By the way that two-photon image is divided into
Aixs cylinder image and dendron image detect aixs cylinder brief summary and dendritic spines respectively to find doubtful cynapse for neurosurgeon's screening, use
The aixs cylinder brief summary detected and dendritic spines carry out 2D cynapse detection；It is then based on 2D cynapse testing result, carries out 3D cynapse detection,
The efficiency and precision of detection cynapse are greatly increased in this way, also there is universality, and insensitive to image type.
Detailed description of the invention
Fig. 1 is the cynapse detection method flow diagram based on two-photon image according to the embodiment of the present invention；
Fig. 2 is to delete the schematic diagram of pixel based on dendron bianry image according to the embodiment of the present invention；
Fig. 3 is the filter schematic according to the embodiment of the present invention；
Fig. 4 is to be sliced interlayer relation schematic diagram according to the two-photon image sequence of the embodiment of the present invention.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with the drawings and specific embodiments,
The present invention is described in more detail.
The embodiment of the present invention provides a kind of cynapse detection method based on two-photon image.As shown in Figure 1, this method can be with
Include:
S100: two-photon image is obtained.
Such as: it is glimmering that the image can be the dendron progress for projecting come aixs cylinder and auditory cortex at a distance to mouse brain amygdaloid nucleus
The image of signal.
S110: two-photon image is divided into aixs cylinder image and dendron image.
Wherein, aixs cylinder and dendron are shown with different colors in two-photon image.By extracting corresponding color
Part can therefrom extract aixs cylinder or dendron image.
S120: the 2D detection of aixs cylinder brief summary is carried out in aixs cylinder image.
This step can further include:
S121: the 3D deconvolution that aixs cylinder image is iterated is handled, deconvolution image is obtained.
In practical applications, this step can pass through the plug-in unit Diffraction PSF 3D and plug-in unit in ImageJ tool
Iterative Deconvolution 3D.class is realized.Wherein, plug-in unit Diffraction PSF 3D is run, phase is inputted
Parameter is closed, a convolution kernel is generated.Preferably, parameter and selection range can be inputted according to following configuration: medium refraction index:
Usual range (1.3-1.5)；Objective aperture: usual range (1.0-1.35)；Wavelength: usual range (460-500nm), most preferably
Ground is 488；Flat image resolution ratio: about 200nm；Z-axis resolution ratio: about 700nm；The wide, high of image, the number of plies are followed successively by 512,
512,140；Longitudinal spherical aberration maximum diameter of hole: usual range (120-870) is most preferably 500-700nm.Simultaneous selection is former
Figure and the convolution kernel generated, and input deconvolution number (preferably 10 times).Then plug-in unit Iterative is run
The image of deconvolution is calculated in Deconvolution 3D.class.
S122: the Local modulus maxima in deconvolution image is determined.
Specifically, this step may include:
S1221: preset a first threshold (such as: 1000).
S1222: the point that pixel value in deconvolution image is greater than the doubtful local maximum of first threshold is found out.
S1223: the pixel value for judging whether the point of each doubtful local maximum meets in a predetermined neighborhood is the largest,
If so, thening follow the steps S1224；It is no to then follow the steps S1225.
S1224: the point for determining the doubtful local maximum is Local modulus maxima.
S1225: the point for determining the doubtful local maximum is not Local modulus maxima.
S123: being based on aixs cylinder image, determines enhancing image.
Wherein, this step may include:
On the basis of two-dimentional xy coordinate system, it is based on aixs cylinder image, determines enhancing image according to the following formula:
Wherein, g indicates enhancing image；fxIndicate the local derviation of aixs cylinder image in the x direction；fyIndicate aixs cylinder image in the direction y
On local derviation；fxxIndicate fxLocal derviation in the x direction；fxyIndicate fxLocal derviation in y-direction；fyyIndicate fyIn y-direction
Local derviation.
S124: binary segmentation processing is carried out to enhancing image.
S125: being based on Local modulus maxima and binary segmentation processing result, removes and does not wrap in binary segmentation processing result
Include the region of Local modulus maxima.
S130: the 2D detection of dendritic spines is carried out in dendron image.
This step may include:
S131: being normalized dendron image and structure enhancing processing.
Specifically, this step may include:
S1311: by dendron image normalization to [0,1].
S1312: according to the following formula, dendritic spines INTERFACE MODEL is set:
Wherein, θ indicates the angle of dendritic spines；The width of σ expression dendritic spines；F (x, y) indicates dendritic spines INTERFACE MODEL；x,y
Cross, the ordinate of pixel in image after respectively indicating normalization.
S1313: according to the cable architecture of image after drag enhancing normalization:
Wherein, the meaning for the parameter being related in this step is referring to the related explanation of step S1312, and details are not described herein.
S132: skeletonizing structure enhances the dendron in processing result, obtains dendron skeleton binary map.
This step may include:
S1321: Threshold segmentation is carried out to structure enhancing processing result, obtains dendron bianry image.
S1322: traversal dendron bianry image deletes pixel according to the following conditions, until dendron bianry image no longer becomes
It turns to only, obtains the first bianry image:
XH(p)=1；2≤min{n1(p),n2(p)}≤3；
Wherein,
Wherein, p indicates pixel；x1,x2,...,x8It is adjacent by 8 of the p of inverse time needle sort since the adjoint point of right side
Point pixel.
In this step, XHIt (p) is the condition for discriminating whether to delete pixel p.biDetermine XH(p) value.Such as:
If meeting x1=0 and (x2=1 or x3=1), then b1=1.And then obtain XH(p) value.
x1,x2,...,x8For 8 consecutive points pixels since the adjoint point of right side by the p of inverse time needle sort, such as Fig. 2 institute
Show.
S1323: the first bianry image of traversal deletes pixel according to the following conditions, until dendron bianry image no longer becomes
It turns to only, obtains dendron skeleton binary map:
XH(q)=1；2≤min{n1(q),n2(q)}≤3；
Wherein,
Wherein, q indicates pixel；x1,x2,...,x8It is adjacent by 8 of the q of inverse time needle sort since the adjoint point of right side
Point pixel.
The explanation of parameter in relation to being related in this step may refer to the explanation of step S1322, and details are not described herein.
S133: it is based on dendron skeleton binary map, determines the bifurcation on dendron skeleton.
This step can specifically include:
S1331: construction filter.
Such as: the filter that construction size is 3 × 3, as shown in Figure 3.Wherein, 4 vertex values are 0, remaining point value
It is 1.
S1332: filter and dendron skeleton binary map are subjected to process of convolution, and second threshold will be greater than in trellis diagram
Point is determined as bifurcation.
For example, the point that trellis diagram intermediate value is more than or equal to 4 can be determined as bifurcation.
S134: being based on dendron skeleton binary map, and according to bifurcation, positions dendritic spines.
Specifically, this step may include:
S1341: the structural element of predetermined radii is constructed, and morphological erosion is carried out to dendron skeleton binary map.
In this step, predetermined radii can (it accounts for several pixels, such as 3) determines according to the length of dendritic spines.
S1342: the skeleton drawing after subtracting corrosion with dendron skeleton binary map obtains dendritic spines alternate location.
S1343: the region of predetermined side length is extracted centered on bifurcation based on dendron skeleton binary map.
Wherein, predetermined side length can be determined according to the length (it accounts for several pixels, example, 31) of dendritic spines, it is preferable that its
2 times of dendritic spines length can be greater than.
S1344: the skeleton part of bifurcation region is chosen, and judges whether there is dendritic spines alternate location and the bone
Frame part is connected；If so, thening follow the steps S1345；Otherwise, step S1346 is executed.
S1345: determine that dendritic spines alternate location is dendritic spines.
S1346: determine that dendritic spines alternate location is not dendritic spines.
S140: 2D cynapse detection is carried out with the aixs cylinder brief summary detected and dendritic spines.
This step can further include:
S141: the aixs cylinder brief summary is represented with the central point of each aixs cylinder brief summary.
S142: dendritic spines are represented with the central point of dendritic spines alternate location.
S143: calculating and records the minimum value of distance between each aixs cylinder brief summary central point and all dendritic spines central points
d。
S144: if this is less than third threshold value apart from minimum value d, aixs cylinder brief summary and dendritic spines are determined as 2D cynapse.
If this is more than or equal to third threshold value apart from minimum value d, aixs cylinder brief summary and dendritic spines 2D cynapse is not determined as.
S150: being based on 2D cynapse testing result, carries out 3D cynapse detection.
This step may include:
S151: cynapse is represented with the midpoint of aixs cylinder brief summary central point in 2D cynapse and dendritic spines alternate location central point
Position.
S152: prominent to represent with the distance of aixs cylinder brief summary central point and dendritic spines alternate location central point in above-mentioned 2D cynapse
The authenticity of touching.
The minimum value of distance is smaller between aixs cylinder brief summary central point and all dendritic spines central points, and authenticity is higher.
S153: two-photon image sequence be sliced it is adjacent before and after layer in, in the coordinate in the neighborhood of 2D synoptic sites
Search cynapse.
S154: judge the authenticity of each cynapse two-photon image sequence be sliced it is adjacent before and after whether be most in layer
High, if so, thening follow the steps S155；Otherwise, step S156 is executed.
S155: determine that the cynapse is 3D cynapse.
S156: determining the cynapse not is 3D cynapse.
Fig. 4 schematically illustrates two-photon image sequence slice interlayer relation.Wherein, r indicates two-photon image sequence
Slice；R-1, r+1 respectively indicate two-photon image sequence and are sliced adjacent front and back layer.
2D segmentation is carried out on two-photon image sequence slice, 2D segmentation result is stringed together on 3D then, finds 3D
Cynapse.
Embodiment in above-described embodiment can be further combined or replace, and embodiment is only to of the invention
Preferred embodiment is described, and it is not intended to limit the concept and scope of the present invention, is not departing from design philosophy of the present invention
Under the premise of, the various changes and modifications that professional and technical personnel in the art make technical solution of the present invention belong to this hair
Bright protection scope.
Claims (11)
1. a kind of cynapse detection method based on two-photon image, which is characterized in that the described method includes:
Obtain the two-photon image；
The two-photon image is divided into aixs cylinder image and dendron image；
The 2D detection of aixs cylinder brief summary is carried out in the aixs cylinder image；
The 2D detection of dendritic spines is carried out in the dendron image；
2D cynapse detection is carried out with the aixs cylinder brief summary detected and dendritic spines；
Based on 2D cynapse testing result, 3D cynapse detection is carried out.
2. the method according to claim 1, wherein the 2D for carrying out aixs cylinder brief summary in the aixs cylinder image
Detection, specifically includes:
To the 3D deconvolution processing that the aixs cylinder image is iterated, deconvolution image is obtained；
Determine the Local modulus maxima in the deconvolution image；
Based on the aixs cylinder image, enhancing image is determined；
Binary segmentation processing is carried out to the enhancing image；
Based on the Local modulus maxima and binary segmentation processing result, removes in the binary segmentation processing result and do not include
The region of the Local modulus maxima.
3. according to the method described in claim 2, it is characterized in that, local maximum in the determination deconvolution image
Point specifically includes:
Set first threshold；
Find out the point that pixel value in the deconvolution image is greater than the doubtful local maximum of first threshold；
The pixel value for judging whether the point of each doubtful local maximum meets in a predetermined neighborhood is the largest, such as
Fruit is, it is determined that the point of the doubtful local maximum is the Local modulus maxima.
4. according to the method described in claim 2, determining enhances image tool it is characterized in that, described be based on the aixs cylinder image
Body includes:
On the basis of two-dimentional xy coordinate system, it is based on the aixs cylinder image, determines the enhancing image according to the following formula:
Wherein, the g indicates the enhancing image；The fxIndicate the local derviation of the aixs cylinder image in the x direction；The fyTable
Show the local derviation of the aixs cylinder image in y-direction；The fxxIndicate the fxLocal derviation on the direction x；The fxyIt indicates
The fxLocal derviation in said y direction；The fyyIndicate the fyLocal derviation in said y direction.
5. the method according to claim 1, wherein the described method includes: it is described in the dendron image into
The 2D detection of row dendritic spines specifically includes:
The dendron image is normalized and structure enhancing is handled；
Skeletonizing structure enhances the dendron in processing result, obtains dendron skeleton binary map；
Based on the dendron skeleton binary map, the bifurcation on dendron skeleton is determined；
Based on the dendron skeleton binary map, and according to the bifurcation, the dendritic spines are positioned.
6. according to the method described in claim 5, it is characterized in that, it is described the dendron image is normalized and structure increase
Strength reason specifically includes:
By the dendron image normalization between 0 and 1；
According to the following formula, dendritic spines INTERFACE MODEL is set:
Wherein, the θ indicates the angle of the dendritic spines；The σ indicates the width of the dendritic spines；The F (x, y) indicates institute
State dendritic spines INTERFACE MODEL；The x, the y respectively indicate cross, the ordinate of pixel in the image after normalization；
According to the cable architecture of image after drag enhancing normalization:
7. according to the method described in claim 5, it is characterized in that, the skeletonizing structure enhancing processing result in dendron,
Dendron skeleton binary map is obtained to specifically include:
Threshold segmentation is carried out to structure enhancing processing result, obtains dendron bianry image；
The dendron bianry image is traversed, deletes pixel according to the following conditions, until the dendron bianry image no longer changes
Until, obtain the first bianry image:
XH(p)=1；2≤min{n1(p),n2(p)}≤3；
Wherein, the p indicates pixel；The x1,x2,...,x8For since the adjoint point of right side by the p's of inverse time needle sort
8 consecutive points pixels；
First bianry image is traversed, deletes pixel according to the following conditions, until the dendron bianry image no longer changes
Until, obtain the dendron skeleton binary map:
XH(q)=1；2≤min{n1(q),n2(q)}≤3；
Wherein, the q indicates pixel；The x1,x2,...,x8For since the adjoint point of right side by the q's of inverse time needle sort
8 consecutive points pixels.
8. according to the method described in claim 5, it is characterized in that, it is described be based on the dendron skeleton binary map, determine dendron
Bifurcation on skeleton specifically includes:
Construct filter；
The filter and the dendron skeleton binary map are subjected to process of convolution, and the point that will be greater than second threshold in trellis diagram
It is determined as bifurcation.
9. according to the method described in claim 5, it is characterized in that, described be based on the dendron skeleton binary map, and according to institute
Bifurcation is stated, the dendritic spines is positioned, specifically includes:
The structural element of predetermined radii is constructed, and morphological erosion is carried out to the dendron skeleton binary map；
Skeleton drawing after subtracting corrosion with the dendron skeleton binary map, obtains dendritic spines alternate location；
Based on the dendron skeleton binary map, centered on the bifurcation, the region of predetermined side length is extracted；
The skeleton part of the bifurcation region is chosen, and judges whether there is the dendritic spines alternate location and the skeleton
Part is connected；If there is, it is determined that the dendritic spines alternate location is dendritic spines.
10. according to the method described in claim 9, it is characterized in that, described carried out with the aixs cylinder brief summary detected and dendritic spines
2D cynapse detection specifically includes:
The aixs cylinder brief summary is represented with the central point of each aixs cylinder brief summary；
The dendritic spines are represented with the central point of the dendritic spines alternate location；
Calculate and record the minimum value of distance between each aixs cylinder brief summary central point and all dendritic spines central points；
If the minimum value of the distance is less than third threshold value, the aixs cylinder brief summary and the dendritic spines are determined as 2D cynapse.
11. according to the method described in claim 9, it is characterized in that, described be based on 2D cynapse testing result, progress 3D cynapse inspection
Measuring tool body includes:
The cynapse is represented with the midpoint of aixs cylinder brief summary central point and dendritic spines alternate location central point in the 2D cynapse
Position；
The distance of the aixs cylinder brief summary central point described in the 2D cynapse and the dendritic spines alternate location central point is to represent
State the authenticity of cynapse；
The two-photon image sequence be sliced it is adjacent before and after layer in, looked into the coordinate in the neighborhood of the 2D synoptic sites
Look for the cynapse；
Judge the authenticity of each cynapse the two-photon image sequence be sliced it is adjacent before and after in layer whether be it is highest,
If so, determining that the cynapse is 3D cynapse.
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