KR102098794B1 - Web based system and method for analyzing cell profile using 3d brain neural circuit image - Google Patents

Abstract

A web-based cell profile analysis system is disclosed. According to the present invention, the system comprises: a pre-processing module for generating three-dimensional (3D) brain image data for each of a wild sample and a mutant sample when the wild sample and the mutant sample corresponding to a specific region of a brain are photographed based on a predetermined photographing type, and are inputted as one or more two-dimensional (2D) brain images; a cell profile extraction unit for extracting cell profile information from the 3D brain image data; a quantitative analysis unit for performing quantitative analysis based on the extracted cell-related profiling information; and a control module for controlling the pre-processing module, the cell profile extraction unit, and the quantitative analysis unit. Accordingly, brain disease research may be proceeded easily through the cell profile.

Description

Web-based cell profile analysis system and method using 3D brain neural circuit images {WEB BASED SYSTEM AND METHOD FOR ANALYZING CELL PROFILE USING 3D BRAIN NEURAL CIRCUIT IMAGE}

The present invention relates to a system and method for analyzing web-based cell profiles using three-dimensional (3D) brain neural circuit images.

With the development of next-generation sequencing technology in the field of biological research, various image and image data, active data, and molecular (epigenetic, transcript, proteome, etc.) data are generated, and these data are large-capacity due to characteristics such as high resolution. It has the characteristic of data.

In the existing system device, it was difficult to obtain and extract segmentation data for each area such as activity and molecules of limited specific image data, and certain systems were designed to receive and process only specific types of image data, so it is limited to receive various types of image data. There was.

In addition, shifting and stitching processes are required to 3D data 2D image data for volume rendering. These processing functions have a system limitation depending on a specific program, and in the case of large-scale data, there is a limitation that a supercomputer must be used in rendering processing.

Accordingly, researchers in the field, which cannot easily use equipment and programs for 3D data 2D image data, are experiencing a problem that 3D images necessary for research cannot be easily generated and utilized, which is a brain study. It is becoming a factor to inhibit the development of.

In addition, in extracting cell profile information from a 3D-based brain image, there are many constraints due to various images and difficulty in efficiently extracting information.

Meanwhile, the above information is only presented as background information to help understanding of the present invention. As to whether or not any of the above is applicable as the prior art related to the present invention, no decision has been made, and no claim is made.

Korean Registered Patent Publication No. 10-1630257

The present invention was created in view of the above-described circumstances, and an embodiment of the present invention proposes a method of generating 2D brain images captured using devices of different types as 3D brain image data.

In addition, an embodiment of the present invention proposes a method of extracting a cell profile from 3D brain image data.

The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems that are not mentioned will be clearly understood by those skilled in the art from the following description. Will be able to.

The web-based cell profile analysis system according to an embodiment of the present invention is photographed based on a predetermined shooting type, but each of a normal sample and an abnormal sample corresponding to a specific region of the brain is one or more 2D brain images. A pre-processing module that, when input, generates 3D brain image data for each of the normal sample and the abnormal sample; A cell profile extraction unit for extracting cell profile information from the 3D brain image data; A quantitative analysis unit for performing quantitative analysis based on the extracted cell profile information; And it may include a control module for controlling the pre-processing module, cell profile extraction unit and quantitative analysis unit.

Here, when the photographing type is the first type, the control module receives the overlap region information between the 2D brain images and, based on the received overlap region information, the pre-processing so that the 2D brain images are stitched. The module may be controlled, and the pre-processing module may be controlled to align the stitched image with respect to a specific axis.

More specifically, when the photographing type is the second type, the control module generates a stitching work folder and an alignment work folder, re-edits the file name based on a predetermined criterion, and the grid is A * B * C (A, B, C is a natural number of 1 or more and each axis indicating 3D), the pre-processing module is controlled to stitch a plurality of 2D brain images located on the AB plane based on the overlap region information based on the C axis, and the The pre-processing module may be controlled to perform alignment to match stitched images based on the C-axis.

More specifically, when the 2D brain image is stitched, the control module calculates and shifts the degree to which the 2D brain image files to be stitched are shifted through Equation 1) below, The pre-processing module may be controlled to stitch shifted images.

Equation 1)

(Here, here, FI ₁ (u, v), FI2 (u, v) is the result of Fourier transform of two images I ₁ (x, y), I ₂ (x, y) shifted from each other)

More specifically, after the operation of shifting brain images, the control module calculates a correlation coefficient r through Equation 2) below for the overlapped regions of the images, and stitching is impossible when the r value is less than 0.8. You can generate a message.

Equation 2)

In addition, the cell profile information may include at least one of cell type information, cell number information, and cell volume information.

The web-based cell profile analysis method according to an embodiment of the present invention may include one or more 2D brain images of each of a normal sample and an abnormal sample corresponding to a specific region of the brain, which are photographed based on a predetermined shooting type. Inputted into; A pre-processing step of generating 3D brain image data for each of the normal sample and the abnormal sample; Extracting cell profile information from the 3D brain image data; And performing a quantitative analysis based on the extracted cell profile information.

More specifically, in the pre-processing step, when the photographing type is the first type, receiving overlap region information between the 2D brain images and stitching the 2D brain image based on the received overlap region information; And performing alignment of the stitched image with respect to a specific axis.

More specifically, the pre-processing step includes: when the photographing type is the second type, generating a stitching work folder and an alignment work folder, and re-editing the file name based on a predetermined criterion; When the grid is set to A * B * C (A, B, C are natural numbers of 1 or more and each axis representing 3D), a plurality of 2D brain images located on the AB plane based on the C axis are based on the overlap area information. Stitching; And aligning stitched images based on the C-axis.

More specifically, the cell-related profiling information may include at least one of cell type information, cell number information, and cell volume information.

The solving means of the technical problems to be achieved in the present invention is not limited to the above-mentioned solving means, and other solving means not mentioned are apparent to those skilled in the art from the following description. It can be understood.

Accordingly, according to various embodiments of the present invention, the following effects may be derived.

First, a 2D brain image can be easily converted into 3D brain image data.

Second, since the brain state of the normal state and the brain state of the abnormal state can be compared by a simple method, the diagnosis of brain disease can be performed easily, and it may be helpful in discovering a target to identify the cause of the brain disease. .

1 is a view for explaining a schematic operation of the web-based cell profile analysis system according to an embodiment of the present invention.
2 is a block diagram showing the configuration of a web-based cell profile analysis system according to an embodiment of the present invention.
3 and 4 are sequence diagrams for explaining a specific operation of the web-based cell profile analysis system according to an embodiment of the present invention.
Further scope of applicability of the present invention will become apparent from the following detailed description. However, various changes and modifications within the spirit and scope of the present invention may be clearly understood by those skilled in the art, and thus, it should be understood that specific embodiments such as detailed description and preferred embodiments of the present invention are given as examples only.

Hereinafter, various embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, in the description of the present invention, when it is determined that a detailed description of a related known function or configuration may unnecessarily obscure the subject matter of the present invention, a detailed description thereof will be omitted.

1 is a view for schematically explaining the operation of a web-based cell profile analysis system (reference numeral 100 in FIG. 2) according to an embodiment of the present invention.

The web-based cell profile analysis system 100 may process an image on the web, extract profile information of a cell included in the image, and analyze the extracted cell profile information.

In the present specification, it is assumed that the first test subject (U1) is a normal (Wild) state without a disease, and the second test subject (U2) is a abnormal (Mutant) state with a disease, and the first test subject (U1) of the brain The predetermined region R1 may correspond to a predetermined region U2 of the second subject U2 brain. Accordingly, the web-based cell profile analysis system 100 may collect normal samples W and abnormal samples M photographed for a predetermined region of the brain.

The web-based cell profile analysis system 100 may generate a plurality of 2D brain images as 3D brain image data by classifying the shooting type. Here, the photographing type varies depending on the photographed microscope, the first type is a type photographed by a confocal microscope, and the second type represents a type photographed by a LaVision microscope, but other microscopes may be applied according to embodiments. It might be.

The web-based cell profile analysis system 100 classifies the shooting type and performs pre-processing (11). The pre-processing is to generate 3D image data based on a plurality of 2D captured images. Specifically, the web-based cell profile analysis system 100 sets a predetermined grid shape and size, determines the overlap size between 2D captured images, and displays 2D captured images while the 2D captured images overlap. Stitching can be performed.

The web-based cell profile analysis system 100 may extract cell profile information from the generated 3D image data (13). The web-based cell profile analysis system 100 may extract neighboring cell profile information using the nucleus as a marker.

The web-based cell profile analysis system 100 may perform quantitative analysis between normal and abnormal samples based on the extracted cell profile information. Thus, by measuring the amount of cell change, the cause of brain disease can be identified and help in target discovery.

Hereinafter, a configuration of the web-based cell profile analysis system 100 will be described with reference to FIG. 2. The web-based cell profile analysis system 100 includes a pre-processing module 110, a cell profile extraction unit 120, a quantitative analysis unit 130, a storage unit 140, and a control module 150. The web-based cell profile analysis system 100 may be connected to a terminal 200 that can be used by a researcher through a local network 500 or the Internet 700.

The researchers assume as described above that they have brain images obtained with a Confocal microscope or a LaVision microscope, which is a kind of polarization microscope, and these images are a plurality of 2D images and require 3D data rendering through rendering. Confocal microscopes or LaVision microscopes, etc., produce high-capacity image files with high-resolution files, and there are constraints that it is difficult to capture the entire area of the brain to be observed with a single sheet. Thus, each image obtained from the microscope is an image representing a portion of the entire area.

Through the web-based cell profile analysis system 100 according to an embodiment of the present invention, the researcher transfers the acquired 2D brain images to the brain 3D rendering server through the website using the terminal 200 and requires 3D brain from the server The image can be provided.

The pre-processing module 110 of the web-based cell profile analysis system 100 includes a wild sample and an abnormal (Mutant) sample corresponding to a specific region of the brain, which are photographed based on a predetermined type of photographing (confocal or LaVision). ) When each sample is input as one or more 2D brain images, 3D brain image data may be generated for each of the normal sample and the abnormal sample.

The pre-processing module 110 is an image type determination module for determining the type of the image input by the researcher, an image downsizing module for reducing the image capacity so that the input image can be processed using a minimum resource, and downsized An image shifting module for allowing images to be shifted for stitching, an image stitching module for stitching shifted images, a stitching result review module for reviewing the quality of the stitched image results, etc. may be additionally included, but embodiments are limited to this. It does not work.

The pre-processing module 110 may determine, for example, a grid size in order to combine 2D brain images captured by dividing into 4 equal parts based on the Z axis, and the grid size may be set according to the matrix value used. In addition, the pre-processing module 110 may calculate a phase correlation matrix Q value through a phase correlation method.

The pre-processing module 110 may perform a stitching operation based on the received overlap area information, wherein a shifting range of an image to be stitched may be determined using a phase correlation method, and stitching using a correlation coefficient You can also calculate the accuracy of

Specifically, for example, it will be described below.

The pre-processing module 110 may determine a grid size in order to combine mouse brain images photographed in quarters, and the grid size may be set according to a corresponding matrix value. For example, the grid size may be set to a local maxima (2 * 2 * n neighborhood), and the degree of shifting may be calculated by calculating a phase correlation matrix Q value of two images through a phase correlation method.

The pre-processing module 110 may be stitched by shifting respective images representing a part of the brain based on the received overlap area information. For example, first two image files can be shifted and combined with each other to the correct position, and the two stitched image files are expressed in the frequency domain through Fourier transform, and based on this, the following phase correlation method (Phase Correlation) Mehthod) can be used to shift the images by calculating the degree of shifting the images in the x and y directions.

[Equation 1]

Here, I1 (x, y) and I2 (x, y) are two images that are stitched to each other, and FI1 (u, v) and FI2 (u, v) are the result of Fourier transform of two images.

When the two images to be stitched to each other are shifted to the correct position through shifting based on the above equation, the pre-processing module 110 calculates the correlation coefficient for the area where the two images overlap using Equation 2 below and overlaps them. You can measure how similar the images are. The correlation coefficient value may be calculated through commonly known methods in addition to the following equation.

[Equation 2]

In the above formula, the r value is between -1 and 1, and as the measured r value approaches 1, the correlation increases, so by calculating the r value after shifting and / or stitching, the accuracy of shifting and / or stitching is measured. can do.

If the images are three-dimensional and the stitched image is evaluated in three dimensions to obtain a correlation coefficient, Equations 3 and 4 below may be used.

[Equation 3]

[Equation 4]

In Equation 4, A and B mean each image.

When the r value is less than a certain value (preferably 0.8) to ensure higher accuracy in image combining, a message indicating that stitching is impossible may be generated and transmitted to a researcher or user who inputs the image.

The researcher or the user who received the impossible message may correct the overlap area, resolution, etc. of the images, photograph the image again, or correct the meta information of the image and re-enter the same image.

When the r value is greater than or equal to a certain value, a message informing that the shifting / stitching operation has been correctly performed may be generated and transmitted to a researcher or a user.

The calculation of the r value (review of the accuracy of shifting and / or stitching) and the impossibility / delivery of the message 330 are shown in FIG. 4 as being performed after shifting and stitching the image to the correct location. After shifting, it may be made before the stitching operation, or after the shifting and stitching operation is completed.

After shifting to the correct position to form the entire cross-section, the stitched cross-sections are arranged to be 3D rendered, aligned with a depth axis, for example, the z-axis. That is, the sliced 2D images align the cross section around the x and y axes and align the images with rigid transformation on the z axis. Here, rigid transformation means matching the images with the features of the cross-section image 1 and the cross-section image 2 by maintaining the shape of the square and rotating only the content of the features inside. For example, cross-section 1 and cross-section 2 are aligned so that the characteristic feature of cross-section 1 and the characteristic feature of cross-section 2 are continuous. Accordingly, a step of determining whether the features of the section 1 and the features of the section 2 are continuous between stitched sections, and arranging the sections 1 and 2 according to the determination may be added. The aligned sections are 3D imaged through 3D rendering.

Next, the cell profile extractor 120 searches for nuclei in the 3D brain image data, converts the searched nuclei into markers, serves as a reference, and converts membrane channels into cytoplasmic signals. You can.

The cell profile information extracted by the cell profile extraction unit 120 may include cell type (intensity) information, cell number information, and cell volume (area) information.

The quantitative analysis unit 130 compares the cell profile information of the normal sample with the cell profile information of the abnormal sample based on the extracted cell profile information, quantitatively calculates a cell change amount, and accurately calculates the brain state based on the calculated information. Can be measured.

The quantitative analysis unit 130 may analyze the increase or decrease in intensity, population, etc. by comparing the cell profile information in the normal state with the cell profile information in the abnormal state.

The storage unit 140 may store normal cell profile information and abnormal cell profile information for each area of the brain, and the storage unit 140 may store various information under the control of the control module 150. . For example, the storage unit 140 may store the cell profile information in the normal state and the cell profile information in the abnormal state of the frontal lobe.

The control module 150 corresponds to a configuration that controls all of the above-described components. Hereinafter, the operation of the control module 150 will be described.

When the photographing type is Confocal type, the control module 150 receives overlap region information between the 2D brain images, stitches the 2D brain image based on the received overlap region information, and specifies the stitched 2D image on a specific axis. The pre-processing module 110 may be controlled to perform alignment matching with (eg, a depth axis).

The control module 150 may generate a stitching work folder and an alignment work folder when the photographing type is a LaVision type. The stitching task folder stores a 2D image file when performing a stitching task, and the stitched data may be stored in a completed stitching folder.

First, the control module 150 may edit and sort 2D image file names. The control module 150 may remove the space included in the 2D image file name and edit the file name as the input file. For example, when the grid is 2 * 2 * 3, images included on the XY plane based on the Z axis may be stored in each of 4 subordinate folders in the stitching work folder, 4 pieces each. For example, four 2D image files based on the Z axis may be stored in the first to third dependent folders in the stitching work folder. Also, the control module 150 may separately include a folder for storing the stitched file. In the folder, images stitched on the XY plane with respect to the Z axis may be stored, and in the case of 2 * 2 * 3, three stitched files may be stored.

When the stitching is completed, the control module 150 may match the stitched image with respect to the Z axis, and the matched image may be displayed as a 3D image through 3D rendering. When using the above method, 3D image data generation can be easily performed even if the photographing type is a LaVision type. In addition, since the phase correlation method described above can be used as it is for the stitching method, there is an effect superior to that of the conventional method for downsizing an image.

3 is a sequence diagram showing that the web-based cell profile analysis system 100 according to an embodiment of the present invention performs pre-processing differently according to a photographing type.

Referring to FIG. 3, the web-based cell profile analysis system 100 receives a plurality of 2D brain image files for a predetermined area of the brain (S310).

When the captured image is captured by the Confocal microscope, the 2D brain may be changed to a specific file format (S315). For example, the nd2 file may be changed to an tiff file in which alignment is performed, but the file format may be variously set.

The web-based cell profile analysis system 100 sets a grid and performs stitching based on overlap area information (S320). Next, the web-based cell profile analysis system 100 performs alignment by matching the stitched image with respect to a depth axis (S323). The stitching and alignment method follows the method described above.

If the captured image is taken with a LaVision microscope, the web-based cell profile analysis system 100 creates a stitch work folder and an alignment work folder (S325), edits the file name, and sorts it (S330).

Then, the web-based cell profile analysis system 100 shifts and stitches the slicing 2D brain image located on the XY plane based on the Z-axis according to the set grid based on the overlap region information (S335).

Specifically, when the grid is composed of (3X3X10), the first subordinate folder to the ten subordinate folders are generated in the stitching work folder, and 10 slicing 2D images along the Z axis can be disposed in each subordinate folder. The web-based cell profile analysis system 100 may stitch images arranged in each subfolder for each folder.

Then, the web-based cell profile analysis system 100 generates 3D image data by performing alignment to match stitched images based on the Z axis (S340).

4 shows a method of retrieving a cell profile by the web-based cell profile analysis system 100 according to an embodiment of the present invention.

First, the web-based cell profile analysis system 100 searches for a nucleus in a predetermined region of the brain (S410).

In step S410, the web-based cell profile analysis system 100 may perform image preparation and segmentation. The image preparation operation may include RescaleIntensity, Resize, and MedianFilter operations. That is, the image preparation operation may fix the shape of the cells and include filtering operations on the fixed cells. Segmentation operations can include Threshold, RemoveHoles, Watershed, and ResizeObjects operations.

Then, the web-based cell profile analysis system 100 converts the nucleus into a marker (S420).

The web-based cell profile analysis system 100 can extract cells based on the nucleus. At this time, the web-based cell profile analysis system 100 may perform ConvertObjectsToImage and Erosion operations.

Thereafter, the web-based cell profile analysis system 100 converts a membrane channel into a cytoplasmic signal (S430).

Specifically, the web-based cell profile analysis system 100 may perform Threshod, ImageMath, RemoveHoles, ImageMath, Closing, ApplyThreshold, ImageMath, and Watershed operations.

On the other hand, the functional operations and subject implementations described in this specification are implemented as digital electronic circuits, or the computer software, firmware, or hardware including the structures and structural equivalents disclosed herein, or one or more of them. It can be implemented in combination. Implementations of the subject matter described herein are one or more modules for computer program instructions encoded on a tangible program storage medium to control or thereby execute operations of one or more computer program products, ie processing systems. Can be implemented.

The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of materials affecting a machine-readable propagated signal, or a combination of one or more of these.

In this specification, the term "system" or "device" includes all apparatus, devices, and machines for processing data, including, for example, a programmable processor unit, a computer or multiprocessor unit, or a computer. The processing system may include, in addition to hardware, code that forms an execution environment for a computer program upon request, such as code constituting the processor unit firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. have.

A computer program (also known as a program, software, software application, script, or code) can be written in any form of a compiled or interpreted language or a programming language, including a priori or procedural languages. It can be deployed in any form, including components, subroutines or other units suitable for use in a computer environment. Computer programs do not necessarily correspond to files in the file system. The program is in a single file provided to the requested program, or in multiple interactive files (e.g., one or more modules, files storing subprograms or parts of code), or part of a file holding other programs or data (Eg, one or more scripts stored in a markup language document). Computer programs may be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

On the other hand, computer-readable media suitable for storing computer program instructions and data include, for example, semiconductor memory devices such as EPROM, EEPROM and flash memory devices, such as magnetic disks such as internal hard disks or external disks, magneto-optical disks and CDs. It can include any form of non-volatile memory, media and memory devices, including -ROM and DVD-ROM disks. The processor portion and the memory may be supplemented by, or incorporated into, special purpose logic circuits.

Implementations of the subject matter described herein include, for example, a back-end component such as a data server, for example, a middleware component such as an application server, or, for example, a web browser or graphic user that a buyer can interact with the implementations of the subject matter described herein. It may be implemented in a front-end component, such as a client computer with an interface, or in a computing system that includes all one or more combinations of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, such as a communication network.

This specification includes details of many specific implementations, but these should not be understood as limiting on the scope of any invention or claim, but rather as a description of features that may be specific to a particular embodiment of a particular invention. It should be understood. Likewise, certain features described herein in the context of individual embodiments may be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable subcombination. Further, although features may operate in a particular combination and may be initially depicted as so claimed, one or more features from the claimed combination may in some cases be excluded from the combination, and the claimed combination subcombined. Or sub-combinations.

Also, although the descriptions describe the operations in the drawings in a specific order, it should be understood that such operations must be performed in the specific order or sequential order shown in order to obtain a desired result, or that all illustrated actions should be performed. Can not be done. In certain cases, multitasking and parallel processing may be advantageous. Further, the separation of various system components of the above-described embodiments should not be understood as requiring such separation in all embodiments, and the described program components and systems are generally integrated together into a single software product or packaged in multiple software products. Understand that you can

As such, this specification is not intended to limit the invention to the specific terms presented. Therefore, although the present invention has been described in detail with reference to the above-described examples, those skilled in the art can make modifications, alterations, and modifications to these examples without departing from the scope of the present invention. The scope of the present invention is indicated by the following claims rather than the above detailed description, and it should be construed that all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts are included in the scope of the present invention. do.

Claims (10)

When each of the normal sample and the abnormal sample corresponding to a specific region of the brain, which is photographed based on a predetermined photographing type, is input as one or more 2D brain images, 3D for each of the normal sample and the abnormal sample A pre-processing module for generating brain image data;
A cell profile extraction unit for extracting cell profile information from the 3D brain image data;
A quantitative analysis unit for performing quantitative analysis based on the extracted cell profile information; And
It includes a control module for controlling the pre-processing module, cell profile extraction unit and quantitative analysis unit,
The control module,
When the photographing type is the second type, a stitching work folder and an alignment work folder are created, and the file name is re-edited based on a predetermined criterion,
When the grid is set to A * B * C (A, B, C is a natural number of 1 or more and each axis representing 3D), a plurality of 2D brain images located on the AB plane based on the C axis are based on the overlap area information. The pre-processing module is controlled to stitch,
And a web-based cell profile analysis system configured to control the pre-processing module to perform alignment to match stitched images based on the C-axis. According to claim 1,
The control module,
When the photographing type is the first type, the overlap region information between the 2D brain images is received, and the preprocessing module is controlled to stitch the 2D brain image based on the received overlap region information, and the stitching is performed. A web-based cell profile analysis system for controlling the pre-processing module to align an image with respect to a specific axis. delete delete delete According to claim 1,
The cell profile information,
A web-based cell profile analysis system including at least one of cell type information, cell number information, and cell volume information. As a web-based cell profile analysis method of the web-based cell profile analysis system,
When each of the normal sample and the abnormal sample corresponding to a specific region of the brain, which is photographed based on a predetermined photographing type, is input as one or more 2D brain images, by the pre-processing module, the normal sample and the A pre-processing step of generating 3D brain image data for each abnormal sample;
Extracting cell profile information from the 3D brain image data by a cell profile extraction unit; And
And performing a quantitative analysis based on the extracted cell profile information by the quantitative analysis unit,
The control module for controlling the pre-processing module,
When the photographing type is the second type, a stitching work folder and an alignment work folder are created, and the file name is re-edited based on a predetermined criterion,
When the grid is set to A * B * C (A, B, C is a natural number of 1 or more and each axis representing 3D), a plurality of 2D brain images located on the AB plane based on the C axis are based on the overlap area information. The pre-processing module is controlled to stitch,
Web-based cell profile analysis method configured to control the pre-processing module to perform alignment to match stitched images based on the C-axis. The method of claim 7,
The pre-treatment step,
If the photographing type is the first type, receiving overlap region information between the 2D brain images, and stitching the 2D brain image based on the received overlap region information; And
And aligning the stitched image with respect to a specific axis. delete The method of claim 7,
The cell profile information,
A web-based cell profile analysis method comprising at least one of cell type information, cell number information, and cell volume information.

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