Brain structure connection map construction method and device

Abstract

The invention provides a construction method and device for a brain structure connection atlas, and the method comprises the steps: obtaining a three-dimensional fluorescence microscopic image of brain cells; calculating a response function according to the gradient distribution of the unidirectional fiber groups; according to the gradient weighted sampling data and the response function of each voxel v in the three-dimensional fluorescence microscopic image, constructing a brain structure connection map; the gradient weighted sampling data at each voxel v represents discrete sampling in each direction of the gradient spherical continuous distribution function within a predetermined range near each voxel v. According to the method, the three-dimensional orientation distribution of the white matter nerve fibers of the brain can be obtained through analysis, then the three-dimensional brain structure connection diagram is constructed according to the fiber orientation distribution, and the method has the advantages of being high in speed, high in precision and capable of achieving three-dimensional analysis.

Description

Technical Field

[0001] The present disclosure relates to the field of biotechnology, and in particular to a method and device for constructing a brain structure connection map. Background Art

[0002] The brain is characterized by complex structure and function, countless times more complex than the largest supercomputer currently available. Past research has focused primarily on the brain's information encoding and storage, while relatively little is known about the brain's complex network structure. Through research, we have a general understanding of the relationship between brain regions and functions. For example, there is a visual function area at the back of the brain, and motor, sensory, and olfactory functions at the front and top. However, the brain's mechanisms are extremely complex, and all brain function circuits within the nervous system exist within interconnected neural networks. A single function cannot be simply attributed to a single brain region. Instead, functional differences between brain regions may be due to the different brain regions to which they are connected.

[0003] Axons transmit signals from one neuron to the next and can be several centimeters long. Some axons are wrapped in myelin, and these myelinated axons are nerve fibers. Many fibers gather together to form white matter fiber bundles. In order to achieve the coordinated effects between brain regions, the various brain regions are connected through white matter, thus forming a brain network. To understand the mechanisms of the brain, it is necessary to explore the complete structure of the various components and synaptic connection networks in the brain, to figure out the output and input fibers of each type of neuron in each brain region, and where the information comes from and where it goes, that is, to draw a brain connection map.

[0004] Brain connectivity mapping research can be broadly divided into three levels, depending on the scale of observation: macroscopic connectivity mapping, primarily using diffusion magnetic resonance imaging (MRI), with a resolution above millimeters, to study the connections between different gray matter regions; mesoscopic connectivity mapping, primarily based on optical microscopy, with a resolution of micrometers to submicrometers, to study the connections between different types of neurons; and microscopic connectivity mapping, using electron microscopy with a resolution of nanometers, to study the connections between neurons at the synaptic level. Existing Nissl staining is primarily used to label glial cells and is not used to visualize nerve fibers.

[0005] Existing diffusion magnetic resonance imaging technology has low analysis accuracy, while electron microscopy is too slow. Existing optical microscopy technology and Nissl staining-based analysis technology are limited to two-dimensional image data and cannot obtain the three-dimensional directional structure of nerve fibers. Summary of the Invention

[0006] The present disclosure is used to solve the problems in the prior art that the brain structure connection map is mainly a two-dimensional directional structure of nerve fibers and cannot reflect the three-dimensional directional structure of nerve fibers, and the process of determining the brain structure connection map has low accuracy and slow speed.

[0007] In order to solve the above technical problems, the present disclosure provides, on one hand, a method for constructing a brain structure connection map, comprising:

[0008] Obtain three-dimensional fluorescence microscopic images of brain cells;

[0009] The response function is calculated based on the gradient distribution of the unidirectional fiber group;

[0010] constructing a three-dimensional brain structural connectivity map based on the gradient-weighted sampling data and response function at each voxel v in the three-dimensional fluorescence microscopy image;

[0011] The gradient weighted sampling data at each voxel v represents discrete sampling of the gradient spherical continuous distribution function in each direction within a predetermined range near each voxel v.

[0012] As a further embodiment of the present disclosure, the process of determining the gradient spherical continuous distribution function includes:

[0013] For each voxel v in the three-dimensional fluorescence microscopy image, constructing a gradient term of each voxel within a predetermined range near the voxel v;

[0014] The convolution term is obtained by convolving the gradient terms of each voxel within a predetermined range near the voxel v using a point spread function defined by spherical coordinates;

[0015] The sum of the convolution terms of all voxel gradients within a predetermined range near the voxel v is calculated to obtain a gradient spherical continuous distribution function within the predetermined range near the voxel v.

[0016] As a further embodiment of the present disclosure, the gradient spherical continuous distribution function is expressed by the following formula:

[0017]

[0018] Where GWF is the weighted gradient at voxel v; I represents the voxel space; is defined on the sphere S 2 The Dirac trigonometric function on The direction of , represents the gradient vector at voxel v′; dist(v, v′) is the Euclidean distance between voxel v and v′; r is the predetermined range; ||·||2 is the 2-norm; represents the gradient magnitude at voxel v′; N = |{v′∈I|dist(v, v′)<r} represents the number of voxels v′ within a predetermined range near voxel v; PSF is the point spread function, defined as where <·,·> represents the inner product, is the direction along the Cartesian coordinate The unit vector pointing to, where n is a preset value.

[0019] As a further embodiment of the present disclosure, the gradient weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy image is calculated using the following formula:

[0020]

[0021]

[0022] Where GWF represents the gradient weighted sampling data at voxel v; I represents the voxel space; represents a set of predefined unit vectors, uniformly distributed on the sphere; <·, ·> represents the inner product; Represents the gradient at voxel v'; N represents the number of voxels v' within a predetermined range near voxel v; n represents the control value of the width of the control point spread function; m represents the number of predefined spherical sampling points.

[0023] As a further embodiment of the present disclosure, constructing a three-dimensional brain structure connectivity map based on the gradient-weighted sampling data and response function at each voxel v in the three-dimensional fluorescence microscopy image includes:

[0024] Performing spherical deconvolution on the gradient-weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy image using a response function to obtain the spherical direction distribution of the fibers at each location;

[0025] Random walk seed points;

[0026] Starting from various sub-points, fiber tracking is performed on the fiber spherical surface direction distribution at each location to obtain the fiber orientation distribution;

[0027] Establish a three-dimensional brain structure connection map based on fiber orientation distribution.

[0028] As a further embodiment of the present disclosure, the three-dimensional fluorescence microscopic image of brain cells includes: three-dimensional fluorescence microscopic images taken from multiple viewing angles;

[0029] Performing spherical deconvolution on the gradient-weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy image using the response function to obtain the spherical direction distribution of the fibers at each location further includes:

[0030] Determine the gradient weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy image taken at each viewing angle;

[0031] The response function is used to perform spherical deconvolution on the gradient-weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy images taken at different viewing angles, and the spherical direction distribution of the fibers at each location is obtained.

[0032] A second aspect of the present disclosure provides a device for constructing a brain structure connection map, comprising:

[0033] an acquisition unit, used for acquiring three-dimensional fluorescence microscopic images of brain cells;

[0034] a first calculation unit, configured to calculate a response function according to a gradient distribution in the corpus callosum;

[0035] a second computing unit, configured to construct a three-dimensional brain structure connectivity map based on the gradient-weighted sampling data and the response function at each voxel v in the three-dimensional fluorescence microscopy image;

[0036] The gradient weighted sampling data at each voxel v represents discrete sampling of the gradient spherical continuous distribution function in each direction within a predetermined range near each voxel v.

[0037] A third aspect of the present disclosure provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any one of the aforementioned embodiments when executing the computer program.

[0038] A fourth aspect of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor of a computer device, implements the method described in any of the aforementioned embodiments.

[0039] A fifth aspect of the present disclosure provides a computer program product, which includes a computer program. When the computer program is executed by a processor of a computer device, it implements the method described in any of the aforementioned embodiments.

[0040] The method and device for constructing a brain structure connection map provided by the present disclosure replace the diffusion-weighted nuclear magnetic resonance signal in the nuclear magnetic resonance by constructing gradient-weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy image. Combined with the response function obtained based on the diffusion-weighted nuclear magnetic resonance analysis, it is possible to analyze the three-dimensional orientation distribution of the brain's white matter nerve fibers, and then construct a three-dimensional brain structure connection map based on the fiber orientation distribution. The method and device have the characteristics of fast speed, high accuracy and three-dimensional analysis.

[0041] In order to make the above and other purposes, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0043] Figure 1 A flowchart showing a method for constructing a brain structure connection map according to an embodiment of the present disclosure is shown;

[0044] Figure 2 A flow chart showing a process for determining a gradient spherical continuous distribution function according to an embodiment of the present disclosure is shown;

[0045] Figure 3 A flowchart showing the process of constructing a three-dimensional brain structure connection map according to an embodiment of the present disclosure is shown;

[0046] Figure 4 A flowchart of a device for constructing a brain structure connection map according to an embodiment of the present disclosure is shown;

[0047] Figure 5 A structural diagram of a computer device according to an embodiment of the present disclosure is shown.

[0048] Description of the accompanying symbols:

[0049] 401. Get unit;

[0050] 402. First computing unit;

[0051] 403. Second computing unit;

[0052] 502. Computer equipment;

[0053] 504, processor;

[0054] 506. Memory;

[0055] 508, driving mechanism;

[0056] 510, input / output module;

[0057] 512. Input devices;

[0058] 514. Output device;

[0059] 516. Presentation equipment;

[0060] 518. Graphical User Interface;

[0061] 520, network interface;

[0062] 522, communication link;

[0063] 524. Communication bus. DETAILED DESCRIPTION

[0064] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0065] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, apparatus, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0066] This specification provides method operation steps as described in the embodiments or flowcharts, but more or fewer operation steps may be included based on routine or non-creative work. The order of steps listed in the embodiments is only one way of executing the steps among many orderings and does not represent the only execution order. When a system or device product is actually executed, the method can be executed in the order shown in the embodiments or the drawings or in parallel.

[0067] In the fiber crossing regions, cells or cell rows with multiple orientations are densely packed together. Compared to the short rows of cells in areas with a single fiber orientation, such as the corpus callosum, the cell groups in these fiber crossing regions form an abstract and complex structure, manifesting as the coexistence of multiple orientations in a confined space. The main methods for analyzing the orientation of nerve fibers in these regions are as follows:

[0068] (1) The classic diffusion tensor analysis method shows isotropy in the fiber crossing area, but has the problem of not being able to analyze the various directions of the fibers.

[0069] (2) Diffusion magnetic resonance imaging is used to analyze brain structural connectivity maps. This method has the problem of low analysis accuracy.

[0070] (3) The method of analyzing brain structure connection maps using electron microscopy technology has the problem of being too slow.

[0071] (4) Optical microscopy analysis method: This method analyzes two-dimensional image data and can only determine a two-dimensional brain structure connection map.

[0072] In summary, existing optical microscopy techniques can only analyze the two-dimensional structure of nerve fibers, but cannot reflect their three-dimensional structure. Diffusion tensor analysis, nuclear magnetic resonance imaging, and electron microscopy analysis of brain structural connectivity maps suffer from low accuracy and slow speed. No high-resolution, efficient solution for obtaining three-dimensional brain structural connectivity maps has been found in the prior art.

[0073] Spherical deconvolution is an algorithm for estimating fiber orientation distribution from diffusion-weighted MRI data, which outperforms the classical diffusion tensor model in dealing with fiber crossing regions. Specifically, the spherical deconvolution algorithm assumes that the diffusion-weighted MRI signal From the response function and fiber orientation density function After the response function is measured by tensor fitting from a unidirectional fiber group (such as the corpus callosum), the fiber orientation density function can be obtained by spherical deconvolution, that is:

[0074]

[0075] in, is the convolution symbol, θ and are the elevation and azimuth angles in spherical coordinates, respectively, and the response function Reflects the distribution of diffuse nuclear magnetic signals of unidirectional fibers.

[0076] To address the aforementioned technical issues, the inventors of this disclosure, through extensive research, discovered that the spherical distribution of the diffusion-weighted NMR signal is perpendicular to the fiber orientation. Because the fiber and cell structural orientations are highly similar, the gradient of a cell image is typically perpendicular to the linear orientation of the cell. Therefore, the gradient at each location in a 3D fluorescence microscopy image shares the same mathematical properties as the diffusion-weighted NMR signal: both are perpendicular to the fiber orientation. Based on this, a gradient-weighted function representing each location in a 3D fluorescence microscopy image can be constructed to replace the diffusion-weighted NMR signal S. This allows the spherical deconvolution algorithm to be used to determine the 3D orientation of nerve fibers, thereby establishing a 3D brain structural connectivity map.

[0077] Specifically, the present disclosure provides a method for constructing a brain structure connection map, such as Figure 1 As shown, including:

[0078] Step 101: Acquire a three-dimensional fluorescence microscopic image of brain cells.

[0079] Specifically, the three-dimensional fluorescence microscopic image is, for example, a cell image labeled by Nissl staining. In specific implementations, it can also be a three-dimensional microscopic image obtained by other staining methods. The three-dimensional microscopic image can be obtained using existing three-dimensional optical microscopy equipment.

[0080] Step 102: Calculate the response function based on the gradient distribution of the unidirectional fiber group.

[0081] When this step is implemented, the diffusion magnetic resonance signals at unidirectional fibers (such as the corpus callosum) in the brain structure are first determined, and then the diffusion magnetic resonance signals at the unidirectional fibers are subjected to tensor fitting to obtain a response function.

[0082] Step 103: Construct a three-dimensional brain structural connectivity map based on the gradient-weighted sampling data and response function at each voxel v in the three-dimensional fluorescence microscopy image. The gradient-weighted sampling data at each voxel v represents discrete sampling of a gradient spherical continuous distribution function in each direction within a predetermined range around each voxel v.

[0083] During specific implementation, the predetermined range can be set according to actual needs, and this disclosure does not limit its specific value.

[0084] This embodiment replaces the diffusion-weighted nuclear magnetic resonance signal in nuclear magnetic resonance by constructing gradient-weighted sampling data at each voxel v in a three-dimensional fluorescence microscopy image. Combined with the response function obtained based on diffusion nuclear magnetic resonance analysis, it is possible to analyze the three-dimensional orientation distribution of white matter nerve fibers in the brain, and then construct a three-dimensional brain structure connection map based on the fiber orientation distribution. This method has the characteristics of fast speed, high accuracy and three-dimensional analysis.

[0085] In some embodiments, Figure 2 As shown, the process of determining the gradient spherical continuous distribution function includes:

[0086] Step 201 : For each voxel v in the three-dimensional fluorescence microscopy image, construct a gradient term of each voxel within a predetermined range near the voxel v.

[0087] Specifically, the gradient term can be expressed using the following formula:

[0088]

[0089] in, is defined on the sphere S 2 The Dirac trigonometric function on The direction of , represents the gradient direction at voxel v′; dist(v, v′) is the Euclidean distance between voxel v and v′; r is the predetermined range; ||·||2 is the 2-norm; Represents the gradient magnitude at voxel v′.

[0090] Step 202 : Convolve the gradient terms of each voxel within a predetermined range near the voxel v using a point spread function defined by spherical coordinates to obtain a convolution term.

[0091] In detail, the point spread function PSF (point spread function) is defined as:

[0092]

[0093] Among them, <·,·> represents the inner product, is the direction along the Cartesian coordinate The unit vector pointing to; n is a preset value, for example, 100.

[0094] Step 203 : Calculate the sum of the convolution terms of all voxel gradients within a predetermined range near the voxel v to obtain a gradient spherical continuous distribution function within the predetermined range near the voxel v.

[0095] In detail, the gradient spherical continuous distribution function is expressed using the following formula:

[0096]

[0097] Where GWF is the weighted gradient at voxel v; I represents the voxel space; is defined on the sphere S 2 The Dirac trigonometric function on The direction of , represents the gradient direction at voxel v′; dist(v, v′) is the Euclidean distance between voxel v and v′; r is the predetermined range; ||·||2 is the 2-norm; represents the gradient size at voxel v′ N = |{v′∈I|dist(v, v′)<r} represents the number of voxels v′ within a predetermined range near voxel v; PSF is the point spread function, defined as where <·,·> represents the inner product, is the direction along the Cartesian coordinate The unit vector pointing to, where n is a preset value.

[0098] In other implementations, the gradient vectors of all voxels within a predetermined range at the voxel v may be obtained by least square fitting.

[0099] In order to improve the efficiency of convolution solution, the gradient spherical continuous distribution function GWF expressed in the previous formula is sampled to obtain the sampling data of each voxel v, where the sampling data is expressed as S 2 The amplitude of the uniformly distributed position on the image is the same as the voxel image of the diffusion magnetic resonance imaging weighted image, and the spherical deconvolution algorithm used in the diffusion magnetic resonance imaging weighted image can be used for processing.

[0100] Specifically, since convolution on a sphere is equivalent to rotation, according to the definition of PSF, the gradient spherical continuous distribution function expressed by the previous formula can be expressed as:

[0101]

[0102] make Taking j as the independent variable, the gradient weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy image can be calculated using the following formula:

[0103]

[0104]

[0105] Where GWF represents the gradient weighted sampling data at voxel v; I represents the voxel space; represents a set of predefined unit vectors, uniformly distributed on the sphere; <·, ·> represents the inner product; Represents the gradient at voxel v'; N represents the number of voxels v' within a predetermined range near voxel v; n represents the control value of the width of the control point spread function; m represents the number of predefined spherical sampling points.

[0106] Specifically, the larger n is, the narrower the corresponding point spread function is, the better it can describe high-frequency angular information, and the larger r is required. m is generally not less than 45.

[0107] In one embodiment of the present disclosure, Figure 3 As shown, the above step 103 constructs a three-dimensional brain structure connection map based on the gradient-weighted sampling data and response function at each voxel v in the three-dimensional fluorescence microscopy image, including:

[0108] Step 301 : Perform spherical deconvolution on the gradient-weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy image using a response function to obtain the spherical direction distribution of the fibers at each location.

[0109] Step 302: Randomly walk the seed points.

[0110] Step 303 : Starting from various sub-points, fiber tracking is performed on the fiber spherical surface direction distribution at each location to obtain the fiber orientation distribution.

[0111] Step 304: Create a three-dimensional brain structure connection map based on the fiber orientation distribution.

[0112] In one embodiment of the present disclosure, considering that three-dimensional fluorescence microscopy imaging may have anisotropy problems that may cause deviations in the above-mentioned cell structure direction analysis, spherical deconvolution will produce unreliable and noisy direction estimates. Based on this, three-dimensional fluorescence microscopy images can be captured at multiple perspectives.

[0113] Performing spherical deconvolution on the gradient-weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy image using the response function to obtain the spherical direction distribution of the fibers at each location further includes:

[0114] Determine the gradient weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy image taken at each viewing angle;

[0115] The response function is used to perform spherical deconvolution on the gradient-weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy images taken at different viewing angles, and the spherical direction distribution of the fibers at each location is obtained.

[0116] This embodiment extends the single-value constraint of spherical deconvolution to multiple-value constraints, so as to obtain a common optimal solution under multi-view constraints, thereby reducing anisotropic deviation.

[0117] Based on the same inventive concept, the present disclosure also provides a device for constructing a brain structure connectivity map, as described in the following embodiments. Because the principles for solving the problem solved by the device for constructing a brain structure connectivity map are similar to those of the method for constructing a brain structure connectivity map, the implementation of the device for constructing a brain structure connectivity map can be referenced to the method for constructing a brain structure connectivity map, and any repetitions will not be repeated.

[0118] Specifically, such as Figure 4 As shown, the device for constructing the brain structure connection map includes:

[0119] The acquisition unit 401 is used to acquire a three-dimensional fluorescence microscopic image of brain cells.

[0120] The first calculation unit 402 is configured to calculate a response function according to the gradient distribution of the corpus callosum.

[0121] The second calculation unit 403 is used to construct a three-dimensional brain structure connection map based on the gradient weighted sampling data and response function at each voxel v in the three-dimensional fluorescence microscopy image.

[0122] The gradient weighted sampling data at each voxel v represents discrete sampling of the gradient spherical continuous distribution function in each direction within a predetermined range near each voxel v.

[0123] Since the analysis in this embodiment is performed under three-dimensional fluorescence microscopy, results with a resolution of tens of microns can be obtained, which is greatly improved compared to the millimeter-level resolution accuracy of diffusion nuclear magnetic resonance. Compared with other optical microscopy analysis methods, it has the advantages of three-dimensional analysis and the ability to distinguish crossing fibers. It can also perform whole-brain scale analysis, which is more efficient than electron microscopy.

[0124] In addition, experimental results have shown that the method and device for constructing a brain structure connection map provided by this application can display the directional distribution of fibers in each region.

[0125] In one embodiment of the present disclosure, a computer device is also provided. Figure 5As shown, computer device 502 may include one or more processors 504, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. Computer device 502 may also include any memory 506 for storing any type of information, such as code, settings, data, and the like. For example, and without limitation, memory 506 may include any one or more combinations of the following: any type of RAM, any type of ROM, a flash memory device, a hard disk, an optical disk, and the like. More generally, any memory may use any technology to store information. Furthermore, any memory may provide volatile or non-volatile retention of information. Furthermore, any memory may represent a fixed or removable component of computer device 502. In one embodiment, when processor 504 executes associated instructions stored in any memory or combination of memories, computer device 502 may perform any operation of the associated instructions. Computer device 502 also includes one or more drive mechanisms 508, such as a hard disk drive mechanism, an optical disk drive mechanism, and the like, for interacting with any memory.

[0126] The computer device 502 may also include an input / output module 510 (I / O) for receiving various inputs (via input devices 512) and for providing various outputs (via output devices 514). A specific output mechanism may include a presentation device 516 and an associated graphical user interface 518 (GUI). In other embodiments, the input / output module 510 (I / O), input devices 512, and output devices 514 may not be included, and the computer device 502 may simply be a computer device in a network. The computer device 502 may also include one or more network interfaces 520 for exchanging data with other devices via one or more communication links 522. One or more communication buses 524 couple the components described above together.

[0127] The communication link 522 may be implemented in any manner, for example, via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 522 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0128] An embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are executed.

[0129] An embodiment of the present disclosure further provides a computer-readable instruction, wherein when a processor executes the instruction, the program therein causes the processor to execute the method of any of the aforementioned embodiments.

[0130] The present disclosure also provides a computer program product, which includes a computer program. When the computer program is executed by a processor of a computer device, the method described in any of the above embodiments is implemented.

[0131] It should be understood that in the various embodiments of the present disclosure, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.

[0132] It should also be understood that in the embodiments of this disclosure, the term "and / or" merely describes the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this disclosure generally indicates that the associated objects are in an "or" relationship.

[0133] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this disclosure can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0134] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0135] In the several embodiments provided in the present disclosure, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or can be an electrical, mechanical or other form of connection.

[0136] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the embodiments of the present disclosure.

[0137] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0138] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or partly contributed to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0139] Specific embodiments are used in the present disclosure to illustrate the principles and implementation methods of the present disclosure. The description of the above embodiments is only used to help understand the method and core ideas of the present disclosure. At the same time, for those skilled in the art, according to the ideas of the present disclosure, there may be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present disclosure.

Claims

1. A method for constructing a brain structure connection map, characterized in that: include: Obtain three-dimensional fluorescence microscopic images of brain cells; The response function is calculated based on the gradient distribution of the unidirectional fiber group; constructing a three-dimensional brain structural connectivity map based on the gradient-weighted sampling data and response function at each voxel v in the three-dimensional fluorescence microscopy image; The gradient weighted sampling data at each voxel v represents discrete sampling of the gradient spherical continuous distribution function in each direction within a predetermined range near each voxel v.

2. The method according to claim 1, wherein The gradient spherical continuous distribution function determination process includes: For each voxel v in the three-dimensional fluorescence microscopy image, constructing a gradient term of each voxel within a predetermined range near the voxel v; The convolution term is obtained by convolving the gradient terms of each voxel within a predetermined range near the voxel v using a point spread function defined by spherical coordinates; The sum of the convolution terms of all voxel gradients within a predetermined range near the voxel v is calculated to obtain a gradient spherical continuous distribution function within the predetermined range near the voxel v.

3. The method according to claim 2, wherein The gradient spherical continuous distribution function is expressed using the following formula: Among them, GWF is the gradient weighting at voxel v; I represents the voxel space; is the Dirac delta function defined on the sphere S 2 in the direction of, representing the gradient direction at voxel v'; dist(v, v') is the Euclidean distance between voxel v and voxel v'; r is a predetermined range; ‖·‖2 is the 2-norm; The magnitude of the gradient at voxel v' is represented by N = |{v' ∈ I| dist(v, v') < r}, which represents the number of voxels v' within a predetermined range near voxel v; PSF is the point spread function, defined as where <·, ·> represents the inner product, is the unit vector in the Cartesian coordinate system pointing in the direction of and n is a preset value.

4. The method according to claim 1, wherein The gradient weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy image is calculated using the following formula: Where GWF represents the gradient weighted sampling data at voxel v; I represents the voxel space; represents a set of predefined unit vectors, uniformly distributed on the sphere; <·,·> represents the inner product; Represents the gradient at voxel v'; N represents the number of voxels v' within a predetermined range near voxel v; n represents the control value of the width of the control point spread function; m represents the number of predefined spherical sampling points.

5. The method according to claim 1, wherein Constructing a three-dimensional brain structure connectivity map based on the gradient-weighted sampling data and response function at each voxel v in the three-dimensional fluorescence microscopy image includes: Performing spherical deconvolution on the gradient-weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy image using a response function to obtain the spherical direction distribution of the fibers at each location; Random walk seed points; Starting from various sub-points, fiber tracking is performed on the fiber spherical surface direction distribution at each location to obtain the fiber orientation distribution; Establish a three-dimensional brain structure connection map based on fiber orientation distribution.

6. The method according to claim 5, wherein The three-dimensional fluorescence microscopic image of brain cells includes: three-dimensional fluorescence microscopic images taken from multiple viewing angles; Performing spherical deconvolution on the gradient-weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy image using the response function to obtain the spherical direction distribution of the fibers at each location further includes: Determine the gradient weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy image taken at each viewing angle; The response function is used to perform spherical deconvolution on the gradient-weighted sampling data at each voxel v in the three-dimensional fluorescence microscopy images taken at different viewing angles, and the spherical direction distribution of the fibers at each location is obtained.

7. A device for constructing a brain structure connection map, characterized in that: include: an acquisition unit, used for acquiring three-dimensional fluorescence microscopic images of brain cells; a first calculation unit, configured to calculate a response function according to a gradient distribution in the corpus callosum; a second computing unit, configured to construct a three-dimensional brain structure connectivity map based on the gradient-weighted sampling data and the response function at each voxel v in the three-dimensional fluorescence microscopy image; The gradient weighted sampling data at each voxel v represents discrete sampling of the gradient spherical continuous distribution function in each direction within a predetermined range near each voxel v.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor of a computer device, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor of a computer device, the method according to any one of claims 1 to 6 is implemented.

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