Geometric modeling method and device for soft tissue structure, equipment and medium

Abstract

The invention provides a geometric modeling method of a soft tissue structure, which can be applied to the technical field of virtual reality, and comprises the following steps: acquiring a volume data value of a volume data point of a soft tissue; preprocessing the volume data value to obtain a multi-dimensional parameter tensor of the volume data point; based on the multi-dimensional parameter tensor and the color index table, constructing an object image feature transfer function to obtain color information of the volume data points; based on the color information, an improved ray casting algorithm is used for endowing each sampling point with geometric attributes, a geometric model of the soft tissue structure is constructed, and the color index table comprises organ types of the soft tissue and a corresponding multi-dimensional parameter matrix. A tissue boundary is extracted by constructing a thresholding method of multi-dimensional parameters, an object image feature transfer function of the multi-dimensional parameters is designed based on a color index table, color information is given to volume data points according to physiological features, and the inter-structure resolution is improved. The invention further provides a geometric modeling device and equipment for the soft tissue structure and a medium.

Description

Technical Field

[0001] This invention relates to the field of virtual reality technology, and in particular to a geometric modeling method, apparatus, device, and medium for soft tissue structures. Background Technology

[0002] To improve computational efficiency and achieve good real-time performance, current virtual soft tissue 3D modeling methods extract only the organ surface and establish a homogeneous physical model. A typical approach is the face rendering algorithm, represented by the Marching Cubes algorithm. This algorithm approximates the isosurface of the tissue surface using a threshold value (such as density) of the soft tissue. Inside this isosurface, the gray values ​​of all volume data points are greater than this threshold; outside the isosurface, the gray values ​​of all volume data points are less than this threshold. Therefore, by extracting the gray-level isosurface, the object surface can be reconstructed, and the complete shape of the soft tissue can be obtained. Face-rendered 3D soft tissue models have the advantages of relatively simple computation and fast rendering speed, and are widely used in traditional virtual surgery. They can achieve good visual effects and meet real-time requirements for deformation operations such as stretching and compression of soft tissues. However, face-rendered soft tissue modeling only extracts surface information. When the topological structure changes, it lacks visual representation of internal organs. The reconstructed incision texture is difficult to display the real tissue structure inside the incision, which is not conducive to obtaining good visual feedback and locating lesions for surgeons, and it is difficult to achieve the expected training effect.

[0003] Volume rendering-based 3D reconstruction methods for soft tissue can completely describe the volume data that makes up soft tissue. This not only more clearly expresses surface details but also reveals realistic internal structures after surface-damaging operations such as cutting and suction, making it more suitable for improving virtual surgical systems. When soft tissue is cut by surgical instruments, the structures beneath the epidermis can be seen from the incision, increasing the surgeon's immersion and operational experience. Ray casting algorithms, as a typical volume rendering method, have advantages including simple principles and ease of implementation, no limitation on body data types, suitability for GPU parallel computing, good real-time performance, and high image quality. However, traditional ray casting algorithms produce models with poor resolution when processing tissues with different color values ​​but similar grayscale values. During volume rendering modeling, the volume data values ​​of different soft tissues may be the same or similar. Assigning the same optical properties and material coefficients during preprocessing makes it difficult to achieve the expected visual and tactile differentiation standards. For example, the liver and heart are adhered to each other, making it difficult to extract their respective boundaries. Because the heart has a higher density, its grayscale value is similar to that of bone. During volume data preprocessing, it is easy to assign the same color information and physical coefficients to volume data points, which reduces the resolution of the reconstructed volume model. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address at least one of the aforementioned technical problems in 3D soft tissue modeling in existing technologies, embodiments of the present invention provide a geometric modeling method, apparatus, device, and medium for soft tissue structures. This method extracts tissue boundaries by constructing a thresholding method for multi-dimensional parameters, designs an image feature transfer function based on a color index table, and assigns color information to volume data points according to physiological characteristics, thereby improving the resolution between structures.

[0006] (II) Technical Solution

[0007] To address the aforementioned technical problems, embodiments of the present invention provide a geometric modeling method, apparatus, device, and medium for soft tissue structures.

[0008] According to a first aspect of the present invention, a geometric modeling method for soft tissue structures is provided, comprising: acquiring volume data values ​​of volume data points of the soft tissue; preprocessing the volume data values ​​to obtain a multidimensional parameter tensor of the volume data points; constructing an image feature transfer function based on the multidimensional parameter tensor and a color index table to obtain color information of the volume data points; and assigning geometric attributes to each sampling point using an improved ray casting algorithm based on the color information to construct a geometric model of the soft tissue structure, wherein the color information includes color values ​​and transparency values; and the color index table includes the organ type of the soft tissue and the corresponding multidimensional parameter matrix.

[0009] In some exemplary embodiments, the multidimensional parameter tensor includes grayscale values, density values, gradient values, and second derivative values, wherein the volume data values ​​include grayscale values; the density values ​​are the average of the volume data values ​​of the 27 volume data points adjacent to the coordinate position of the volume data point to be obtained; the gradient values ​​are obtained by performing central difference calculation on the volume data values; and the second derivative values ​​are obtained by performing central difference calculation on the gradient values.

[0010] In some exemplary embodiments, constructing an image feature transfer function based on a multidimensional parameter tensor and a color index table to obtain the color information of volume data points includes: determining the organ type corresponding to the soft tissue using a color index table based on the multidimensional parameter tensor; determining the corresponding multidimensional parameter matrix using a color index table based on the organ type; and calculating the color information of the volume data points based on the multidimensional parameter matrix and the multidimensional parameter tensor.

[0011] In some exemplary embodiments, based on color information, a geometric model of the soft tissue structure is constructed by assigning geometric properties to each sampling point using an improved ray projection algorithm. This includes: obtaining sampling points by ray projection sampling using parallel projection; accumulating the color and transparency values ​​of the sampling points using alpha mixing technology; calculating the color composite value when the accumulated opacity is 1 or when light passes through the soft tissue to obtain the final color and final transparency values; and constructing a three-dimensional soft tissue model containing internal fine structure information based on the final color and final transparency values.

[0012] In some exemplary embodiments, when the sampling point is not located on a volume data point with a value, a planar interpolation algorithm is used to calculate the volume data value of the sampling point.

[0013] In some exemplary embodiments, the formula for alpha hybrid technology includes:

[0014]

[0015]

[0016] in, For the color value of the i-th sampling point, The transparency value of the i-th sampling point The cumulative color values ​​of all sampling points before the i-th sampling point and This is the cumulative transparency value of all sampling points before the i-th sampling point.

[0017] In some exemplary embodiments, the formula for calculating the color information of volume data points based on the multidimensional parameter matrix and the multidimensional parameter tensor is as follows:

[0018] (R,G,B,A)=(p,g,d,s)∙Tab.A

[0019] Where R is the value corresponding to red in the color value, G is the value corresponding to green in the color value, B is the value corresponding to blue in the color value, A is the transparency value, p is the gray value, g is the gradient value, d is the density value, s is the second derivative value, and Tab.A is the four-dimensional parameter matrix corresponding to the index table.

[0020] According to a second aspect of the present invention, a geometric modeling apparatus for soft tissue structures is provided, comprising: an acquisition module for acquiring volume data values ​​of volume data points of the soft tissue; a first calculation module for preprocessing the volume data values ​​to obtain a multidimensional parameter tensor of the volume data points; a second calculation module for constructing an image feature transfer function based on the multidimensional parameter tensor and a color index table to obtain color information of the volume data points; and a construction module for assigning geometric attributes to each sampling point based on the color information using an improved ray casting algorithm to construct a geometric model of the soft tissue structure, wherein the color information includes color values ​​and transparency values; and the color index table includes organ types of the soft tissue and corresponding multidimensional parameter matrices.

[0021] According to a third aspect of the present invention, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method described above.

[0022] According to a fourth aspect of the present invention, a storage medium is provided, characterized in that: the storage medium stores computer-readable instructions, which, when executed by one or more processors, cause one or more processors to perform the method described above.

[0023] (III) Beneficial Effects

[0024] As can be seen from the above technical solutions, the geometric modeling method, apparatus, device, and medium for soft tissue structures provided by the embodiments of the present invention have at least the following beneficial effects:

[0025] (1) By preprocessing the volume data, a multidimensional parameter tensor is obtained. Based on the multidimensional parameter tensor and the color index table, an object feature transfer function is established. Combined with the real color features of soft tissue, the volume data value is mapped to the color information of soft tissue, making the distinction between structures more accurate.

[0026] (2) By improving the ray projection algorithm, realistic geometric properties are given to each volume data point, and a geometric model of the fine structure of liver tissue that conforms to the principle of visual observation is established. Attached Figure Description

[0027] The above-described features, other objects, and advantages of the present invention will become clearer from the following description of embodiments of the invention with reference to the accompanying drawings, in which:

[0028] Figure 1 The schematic diagram illustrates a flow chart of a geometric modeling method for a soft tissue structure according to an embodiment of the present invention;

[0029] Figure 2This illustration schematically shows a process diagram of constructing an object feature transfer function based on a multidimensional parameter tensor and a color index table according to an embodiment of the present invention to obtain the color information of volume data points;

[0030] Figure 3 This illustration schematically shows a design flow diagram of the object feature transfer function according to an embodiment of the present invention;

[0031] Figure 4 The illustration shows a flowchart of constructing a geometric model of a soft tissue structure by assigning geometric properties to each sampling point using an improved ray casting algorithm according to an embodiment of the present invention.

[0032] Figure 5 The diagram illustrates a comparison between a soft tissue model constructed using an improved ray casting algorithm according to an embodiment of the present invention and a soft tissue model constructed with the same optical properties.

[0033] Figure 6 This illustration schematically shows the principle of the improved ray casting algorithm according to an embodiment of the present invention;

[0034] Figure 7 A schematic diagram of a brain model constructed using a surface-based 3D reconstruction method is shown.

[0035] Figure 8 A schematic diagram of soft tissue constructed using a one-dimensional transformation function is shown.

[0036] Figure 9 This illustration schematically shows a soft tissue structure constructed based on an object feature transfer function using a multidimensional parametric tensor according to an embodiment of the present invention.

[0037] Figure 10 A schematic diagram illustrates a structural block diagram of a geometric modeling apparatus for soft tissue structures according to an embodiment of the present invention; and

[0038] Figure 11 A block diagram of an electronic device illustrating a geometric modeling method for soft tissue structures according to an embodiment of the present invention is shown schematically. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0040] Figure 1The illustration shows a flowchart of a geometric modeling method for soft tissue structures according to an embodiment of the present invention.

[0041] like Figure 1 As shown, a geometric modeling method for soft tissue structures according to an embodiment of the present invention includes steps S110-S140.

[0042] In step S110, the volume data values ​​of the volume data points of the soft tissue are obtained.

[0043] In this embodiment of the invention, the data is obtained by scanning organ tomographic images with CT and MRI instruments. The complete set of CT and MRI grayscale images can be converted into volume data values, and the concepts related to volume data are voxels and volume textures.

[0044] In step S120, the volume data values ​​are preprocessed to obtain the multidimensional parameter tensor of the volume data points.

[0045] In some exemplary embodiments, the multidimensional parameter tensor includes grayscale values, density values, gradient values, and second derivative values. The volume data values ​​include grayscale values; the density value is the mean of the volume data values ​​of the 27 nearest volume data points to the desired volume data point; the gradient value is calculated by centrally differencing the volume data values; and the second derivative value is calculated by centrally differencing the gradient values. By leveraging the influence of parameters such as gradient values, density values, and second derivatives on the fine structure of soft tissue, a multidimensional parameter tensor that accurately maps the optical properties and physiological characteristics of soft tissue is established.

[0046] For example, the gradient value is calculated using the central difference method, and the calculation formula is as follows:

[0047] (1)

[0048] (2)

[0049] (3)

[0050] (4)

[0051] in, Coordinates are The volume data values ​​of the volume data points, Coordinates are The gradient value of the volume data points along the x-direction. Coordinates are The gradient value of the volume data points along the y-direction. Coordinates are The gradient value of the volume data points along the z-direction. Coordinates are The gradient values ​​of the volume data points.

[0052] The formula for calculating density is as follows:

[0053] (5)

[0054] in, Coordinates are The density values ​​of the volume data points.

[0055] The second derivative is obtained by further calculating the gradient value using the central difference method, as shown in the following formula:

[0056] (6)

[0057] (7)

[0058] (8)

[0059] in, Coordinates are The second derivative value of the volume data points along the x-direction. Coordinates are The second derivative value of the volume data points along the y-direction. Coordinates are The second derivative value of the volume data point along the z-direction.

[0060] In step S130, based on the multidimensional parameter tensor and the color index table, an object feature transfer function is constructed to obtain the color information of the volume data points. The color information includes color values ​​and transparency values. The color index table includes the organ types of soft tissues and the corresponding multidimensional parameter matrices.

[0061] In some exemplary embodiments, step S130 includes steps S131-S133, see [link to relevant documentation]. Figure 2 .

[0062] In step S131, the organ type corresponding to the soft tissue is determined using a color index table based on the multidimensional parameter tensor.

[0063] By introducing a multidimensional parameter tensor, an object feature transfer function is established. Combined with the true color features of soft tissue, volume data values ​​are mapped to the color information of the soft tissue. (See [link to relevant documentation]). Figure 3 ,Depend on Figure 3 As can be seen, the object feature transfer function consists of a multidimensional parameter tensor and a color index table, where the color information index table is shown in Table 1.

[0064] Table 1 Color Index Table

[0065]

[0066] In step S132, the corresponding multidimensional parameter matrix is ​​determined using a color index table based on the organ type.

[0067] In step S133, the color information of the volume data points is calculated based on the multidimensional parameter matrix and the multidimensional parameter tensor.

[0068] For example, organ types can be determined by multidimensional parameter tensors and index tables, and then the corresponding parameter matrix can be determined. The optical properties of volume data points can be calculated using the following formula.

[0069] (R,G,B,A)=(p,g,d,s)∙Tab.A (9)

[0070] Where R is the value corresponding to red in the color value, G is the value corresponding to green in the color value, B is the value corresponding to blue in the color value, A is the transparency value, p is the gray value, g is the gradient value, d is the density value, s is the second derivative value, and Tab.A is the four-dimensional parameter matrix corresponding to the index table.

[0071] In step S140, based on color information, geometric properties are assigned to each sampling point using an improved ray projection algorithm to construct a geometric model of the soft tissue structure.

[0072] In this embodiment of the invention, the color information assigned to the soft tissue model consists of red, green, blue, and transparency (RGBA). Traditional ray casting algorithms directly convert one-dimensional volume data into multi-dimensional color values ​​and opacity through an empirical transfer function during the classification stage, leading to low resolution between different structures and blurred tissue boundaries. This invention preprocesses and calculates the original volume data to obtain parameters of four dimensions or higher to map color information, making the distinction between structures more accurate.

[0073] In some exemplary embodiments, step S140 includes steps S141-S143, see [link to relevant documentation]. Figure 4 .

[0074] In step S141, sampling points are obtained by using parallel projection to sample light projection.

[0075] When the sampling point is not located on a volume data point with a value, a planar interpolation algorithm is used to calculate the volume data value of the sampling point.

[0076] For example, to improve computational efficiency, the volume data value V0 of the ray sampling point P0(x, y) that is not on a volume data point with a value is obtained by planar interpolation. The calculation formula is as follows:

[0077]

[0078] (10)

[0079] Where V 01 V 11 V 00 V 10 The volume data values ​​are divided into four volume data points surrounding point P0(x,y).

[0080] In step S142, the color value and transparency value of the sampling points are accumulated using alpha mixing technology. When the accumulated opacity is 1 or light passes through the soft tissue, the color synthesis value is calculated to obtain the final color value and the final transparency value.

[0081] Based on the principle of ray casting algorithms, light rays emerge from the viewport and pass through the entire volume data field. Data resampling and computation follow the alpha mixing technique, and the calculation formula is as follows:

[0082] (11)

[0083] (12)

[0084] in, For the color value of the i-th sampling point, The transparency value of the i-th sampling point The cumulative color values ​​of all sampling points before the i-th sampling point and This is the cumulative transparency value of all sampling points before the i-th sampling point.

[0085] In step S143, a three-dimensional soft tissue model containing internal fine structure information is constructed based on the final color value and the final transparency value.

[0086] In geometric modeling of soft tissue fine structures based on ray casting algorithms, volume data is derived from patients' medical images. Volume data values ​​for different soft tissues may be the same or similar. Assigning the same optical properties and material coefficients during preprocessing makes it difficult to achieve the expected visual and tactile differentiation criteria. (See [link to relevant documentation]). Figure 5 (b) of the present invention describes how an improved ray casting algorithm assigns realistic geometric properties to each volume data point, establishing a geometric model of the fine structure of liver tissue that conforms to the principles of visual observation. (See also: [link to related document]). Figure 5 (c) in the middle.

[0087] Figure 6 The schematic diagram illustrates the principle of the improved ray projection algorithm according to an embodiment of the present invention.

[0088] Depend on Figure 6 As can be seen, the improved ray projection algorithm according to embodiments of the present invention includes the following steps:

[0089] Data preprocessing: The abdominal CT data is preprocessed, including noise reduction and removal of outliers.

[0090] Data value classification: The data values ​​are initially classified according to region and boundary.

[0091] Soft tissue extraction and classification: Calculate multidimensional parameter tensors, classify the volume data values ​​of soft tissue according to the color information index table, and assign corresponding color information and transparency.

[0092] Data resampling: Resampling is performed in the three-dimensional data field using the parallel projection method, and the volume data values ​​of the sampled points are calculated using the planar interpolation method.

[0093] Color information synthesis: Color values ​​and transparency are synthesized using alpha blending technology. When the accumulated opacity is 1 or light passes through the soft tissue, the color synthesis value is calculated to reconstruct a three-dimensional soft tissue model containing information about its internal fine structure.

[0094] The geometric modeling method for soft tissue structures provided by this invention, which reconstructs soft tissue models by drawing relative surfaces, can clearly describe the internal structure of the tissue. Compared with traditional ray casting algorithms, the tissue boundaries of this invention are clearer and have higher visual resolution.

[0095] Figure 7 The diagram illustrates a brain model constructed using a surface-based 3D reconstruction method.

[0096] like Figure 7 As shown, surface-based 3D reconstruction methods have been able to construct complex models such as the brain, but the lack of internal information representation limits their further development. When the human brain model is separated from the center, the surface-based 3D model only shows the red inner surface, failing to display the actual internal tissue structure.

[0097] Figure 8 A schematic diagram of soft tissue constructed using a one-dimensional transformation function is shown.

[0098] like Figure 8 As shown, experiments were conducted using volumetric data of the liver, surrounding bones, blood vessels, and parts of the heart, etc. The volumetric model reconstructed by a one-dimensional transformation function was unclear due to the use of only grayscale values ​​to map color and light opacity, resulting in unclear tissue boundaries and connections between different tissues, and blurred cross-sectional edges.

[0099] Figure 9 The illustration shows a schematic diagram of soft tissue constructed based on the object feature transfer function of a multidimensional parameter tensor according to an embodiment of the present invention.

[0100] like Figure 9As shown, experiments were conducted using volumetric data of the liver, surrounding bones and blood vessels, and parts of the heart, etc. A volumetric model reconstructed based on the object feature transfer function of a multidimensional parameter tensor was used, along with... Figure 8 In comparison, the resolution of different tissues with the same gray value is significantly improved, the contours between tissues are clear, and the edges of cross sections are distinct.

[0101] Figure 10 The diagram schematically illustrates a structural block diagram of a geometric modeling apparatus for soft tissue structures according to an embodiment of the present invention.

[0102] like Figure 10 As shown, a geometric modeling device 800 for soft tissue structures according to an embodiment of the present invention includes an acquisition module 810, a first calculation module 820, a second calculation module 830, and a construction module 840.

[0103] The acquisition module 810 is used to acquire the volume data values ​​of volume data points in soft tissue.

[0104] The first calculation module 820 is used to preprocess the volume data values ​​to obtain the multidimensional parameter tensor of the volume data points.

[0105] The second calculation module 830 is used to construct an image feature transfer function based on a multidimensional parameter tensor and a color index table to obtain the color information of the volume data points. The color information includes color values ​​and transparency values. The color index table includes the organ types of soft tissues and the corresponding multidimensional parameter matrices.

[0106] Module 840 is used to construct a geometric model of soft tissue structures by assigning geometric properties to each sampling point based on color information using an improved ray casting algorithm.

[0107] In some specific embodiments, any and more modules among the acquisition module 810, the first calculation module 820, the second calculation module 830, and the construction module 840 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functionality of one or more of these modules can be combined with at least some of the functionality of other modules and implemented in one module.

[0108] In some specific embodiments, at least one of the acquisition module 810, the first computing module 820, the second computing module 830, and the construction module 840 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable method of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three methods. Alternatively, at least one of the acquisition module 810, the first computing module 820, the second computing module 830, and the construction module 840 can be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.

[0109] Figure 11 A block diagram of an electronic device illustrating a geometric modeling method for soft tissue structures according to an embodiment of the present invention is shown schematically.

[0110] like Figure 11 As shown, an electronic device 600 according to an embodiment of the present invention includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage portion 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0111] RAM 603 stores various programs and data required for the operation of electronic device 600. Processor 601, ROM 602, and RAM 603 are interconnected via bus 604. Processor 601 executes various operations of the method flow according to embodiments of the present invention by executing programs in ROM 602 and / or RAM 603. It should be noted that the program may also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 may also execute various operations of the method flow according to embodiments of the present invention by executing programs stored in one or more memories.

[0112] In some specific embodiments, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device 600 may also include one or more of the following components connected to the I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 610 as needed so that computer programs read from it can be installed into the storage section 608 as needed.

[0113] One embodiment of the present invention discloses a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to an embodiment of the present invention.

[0114] In some specific embodiments, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of the invention, the computer-readable storage medium may include ROM 602 and / or RAM 603 and / or one or more memories other than ROM 602 and RAM 603 described above.

[0115] One embodiment of the present invention illustrates a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in this embodiment of the present invention.

[0116] When the computer program is executed by the processor 601, it performs the functions defined in the system / apparatus of this invention. According to embodiments of the invention, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0117] In some specific embodiments, the computer program may rely on tangible storage media such as optical storage devices or magnetic storage devices. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via communication section 609, and / or installed from removable medium 611. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0118] In some specific embodiments, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, it performs the functions defined in the system of this embodiment of the invention. According to embodiments of the invention, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0119] In some specific embodiments, program code for executing the computer programs provided in the embodiments of the present invention can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0120] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0121] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A geometric modeling method for soft tissue structure, characterized in that: include: Obtaining volume data values ​​of volume data points of soft tissue; Preprocessing the volume data values ​​to obtain a multidimensional parameter tensor of the volume data points; Based on the multidimensional parameter tensor and the color index table, an object-image feature transfer function is constructed to obtain color information of the volume data points; as well as Based on the color information, an improved ray casting algorithm is used to assign geometric attributes to each sampling point and construct a geometric model of the soft tissue structure. Wherein, the color information includes a color value and a transparency value; and The color index table includes organ types of soft tissues and corresponding multi-dimensional parameter matrices.

2. The method according to claim 1, characterized in that: The multidimensional parameter tensor includes grayscale value, density value, gradient value and second-order derivative value, Wherein, the volume data value includes a grayscale value; The density value is the average of the volume data values ​​of 27 volume data points adjacent to the coordinate position of the volume data point being sought; The gradient value is obtained by calculating the central difference of the volume data value; and The second-order derivative value is calculated by performing central difference calculation on the gradient value.

3. The method according to claim 1, characterized in that The constructing of an object-image feature transfer function based on the multidimensional parameter tensor and the color index table to obtain the color information of the volume data point comprises: Based on the multidimensional parameter tensor, determining the organ type corresponding to the soft tissue using the color index table; Based on the organ type, determining a corresponding multidimensional parameter matrix using the color index table; and The color information of the volume data points is calculated based on the multidimensional parameter matrix and the multidimensional parameter tensor.

4. The method according to claim 1, characterized in that: The step of assigning geometric attributes to each sampling point based on the color information and constructing a geometric model of the soft tissue structure by using an improved ray casting algorithm includes: The sampling points are obtained by performing ray casting sampling in a parallel projection manner; Accumulating the color value and transparency value of the sampling point through the alpha blending technique, and when the accumulated opacity is 1 or the light passes through the soft tissue, calculating the color synthesis value to obtain the final color value and the final transparency value; and Based on the final color value and the final transparency value, a three-dimensional soft tissue model including internal fine structure information is constructed.

5. The method according to claim 4, characterized in that When the sampling point is not located on a volume data point with a value, a plane interpolation algorithm is used to calculate the volume data value of the sampling point.

6. The method according to claim 4, characterized in that The formula for the alpha blending technique includes: in, is the color value of the i-th sampling point, is the transparency value of the i-th sampling point, is the accumulated color value of all sampling points before the i-th sampling point and It is the accumulated transparency value of all sampling points before the i-th sampling point.

7. The method according to claim 3, characterized in that The formula for calculating the color information of the volume data point based on the multidimensional parameter matrix and the multidimensional parameter tensor is: (R,G,B,A)=(p,g,d,s)∙Tab.A Among them, R is the value corresponding to the red color value, G is the value corresponding to the green color value, B is the value corresponding to the blue color value, A is the transparency value, p is the gray value, g is the gradient value, d is the density value, s is the second-order derivative value, and Tab.A is the four-dimensional parameter matrix corresponding to the index table.

8. A geometric modeling device for soft tissue structure, characterized in that: The device comprises: An acquisition module, used for acquiring volume data values ​​of volume data points of soft tissue; A first calculation module, used for preprocessing the volume data value to obtain a multi-dimensional parameter tensor of the volume data point; A second calculation module is used to construct an object-image feature transfer function based on the multidimensional parameter tensor and the color index table to obtain color information of the volume data point; and A construction module is used to assign geometric attributes to each sampling point based on the color information by using an improved ray casting algorithm to construct a geometric model of the soft tissue structure. Wherein, the color information includes a color value and a transparency value; and The color index table includes organ types of soft tissues and corresponding multi-dimensional parameter matrices.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the method according to any one of claims 1 to 7.

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