Intelligent image processing method and system for urinary surgery

Abstract

The invention discloses an intelligent image processing method and system for urinary surgery. The method comprises the following steps: acquiring first node three-dimensional image data of a target object and extracting first image key data; second node three-dimensional image data of the target object are obtained, the type of the second node three-dimensional image data is the same as that of the first node three-dimensional image data, and second image key data are extracted from the second node three-dimensional image data; constructing a series of stepping data based on the first image key data and the second image key data; performing one-by-one replacement based on each group of data in the series of stepping data; and rendering and generating a dynamic three-dimensional image based on the replaced first node three-dimensional image data and / or second node three-dimensional image data. According to the method, the dynamic three-dimensional image data is constructed by utilizing the existing three-dimensional image data, so that more intuitive diagnosis and treatment reference data can be provided on the premise of saving the diagnosis and treatment cost, and the user experience of doctors and patients is remarkably improved.

Description

Technical Field

[0001] The present application belongs to the field of computer data processing technology, and specifically relates to a method and system for intelligent image processing in urology. Background Art

[0002] Three-dimensional image reconstruction is an important technology in the medical field. With the help of raw data from ultrasound diagnostic equipment, endoscopes, CT or MRI, three-dimensional image reconstruction uses computer image processing technology to calculate, analyze, fuse and render data, and uses three-dimensional images to display the morphology and spatial distribution of target medical objects such as tissue systems, bones, blood vessels, diseased tissues and foreign bodies, so as to intuitively, accurately and quickly separate the target from other systems / tissues, presenting a three-dimensional visual effect.

[0003] However, most of the common medical 3D images, such as vascular 3D images, bone 3D images, and tissue 3D images, are static structural descriptions. Static structural images lack vividness in content expression, making it difficult for doctors and other professionals to find focal issues such as lesions and abnormalities. The lack of reference and comparison makes it easy to miss key information and cause missed diagnoses, misdiagnoses, and wrong diagnoses. For patients who have not received professional training, it is usually difficult to understand based on the image information obtained, and professional interpretation is required. In addition, the image content is boring, and the user's diagnosis and treatment experience is poor.

[0004] Dynamic three-dimensional image construction technology can solve the above problems to a certain extent. However, the application of dynamic three-dimensional image construction technology in the medical field is not mature at present. The difficulty lies in that a series of dynamic key data required in the dynamic three-dimensional image construction process is not easy to obtain. An existing acquisition method is to use relevant diagnostic and treatment equipment to spy on or scan the patient's target area multiple times to obtain dynamic key data. However, the above acquisition method and other existing technologies currently require huge costs to overcome the above difficulties and are difficult to sustain in clinical applications.

[0005] The information disclosed in this background technology section is only intended to increase the understanding of the overall background of the application and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0006] The present invention proposes an intelligent image processing method and system for urology, which can overcome the problem that key data in the dynamic three-dimensional image construction technology in the prior art is not easy to obtain or the cost is huge. It can realize low-cost dynamic three-dimensional image construction based on existing data, which is helpful for medical examinations and improves user experience.

[0007] In order to solve the above problems, the present application discloses an embodiment to provide a urology intelligent image processing method, including:

[0008] S100, acquiring first node three-dimensional image data of a target object and extracting first image key data, and determining the type of the three-dimensional image data;

[0009] S110, acquiring second-node 3D image data of the target object, and extracting second image key data from the second-node 3D image data, wherein the type of the second-node 3D image data is the same as the type of the first-node 3D image data, and the first image key data and the second image key data have the same dimension;

[0010] S120, constructing a series of step data based on the first image key data and the second image key data;

[0011] S130, based on each group of data in the series of step data, replace the first image key data of the first node three-dimensional image data one by one and / or replace the second image key data of the second node three-dimensional image data one by one, and render and generate a dynamic three-dimensional image based on the replaced first node three-dimensional image data and / or second node three-dimensional image data.

[0012] In one embodiment, the first node three-dimensional image data in step S100 is three-dimensional image data constructed and generated based on original data acquired by a medical device for a target medical object;

[0013] In step S110 , the second node 3D image data type being the same as the first node 3D image data type indicates that the corresponding medical equipment types are the same and the target medical object types are the same.

[0014] In one embodiment, the first key image data in step S100 includes one or a combination of contour data, position coordinate data, medical status data, color data, three-dimensional feature data, and texture data.

[0015] In one embodiment, step S120 specifically includes:

[0016] S122, receiving at least one third-node 3D image data and obtaining its generation time; sorting the first-node 3D image data, the second-node 3D image data, and at least one third-node 3D image data according to the generation time, wherein the type of the third-node 3D image data is the same as the type of the first-node 3D image data;

[0017] S124, for the sorted nodes, construct sub-series step data based on the three-dimensional image data corresponding to the adjacent nodes in sequence, wherein the time step length of the sub-series step data is a divisor or common divisor of the time difference between each node;

[0018] S126, combining all obtained sub-series step data to obtain series step data.

[0019] In one embodiment, step S130 specifically includes:

[0020] Determine a first time distance from the step time of each group of data in the series of step data to the occurrence time of the first node, and a second time distance to the occurrence time of the second node;

[0021] If the first time distance is smaller than the second time distance, the group of data replaces the first image key data of the first node 3D image data; otherwise, the group of data replaces the second image key data of the second node 3D image data.

[0022] The present application also provides a urology intelligent image processing system, the system comprising:

[0023] A first acquisition module is used to acquire the first node three-dimensional image data of the target object and extract the first image key data to determine the type of the three-dimensional image data;

[0024] A second acquisition module acquires second-node three-dimensional image data of the target object, and extracts second image key data from the second-node three-dimensional image data, wherein the type of the second-node three-dimensional image data is the same as the type of the first-node three-dimensional image data, and the first image key data and the second image key data have the same dimension;

[0025] A step data construction module, used to construct a series of step data based on the first image key data and the second image key data;

[0026] A rendering generation module is used to replace the first image key data of the first node three-dimensional image data one by one and / or replace the second image key data of the second node three-dimensional image data one by one based on each group of data in the series of step data, and render and generate a dynamic three-dimensional image based on the replaced first node three-dimensional image data and / or second node three-dimensional image data.

[0027] In one embodiment, in the first acquisition module, the first node three-dimensional image data is three-dimensional image data constructed and generated based on the original data acquired by the medical device for the target medical object; in the second acquisition module, the second node three-dimensional image data type is the same as the first node three-dimensional image data type, indicating that the corresponding medical device type is the same and the target medical object type is the same.

[0028] In one embodiment, in the first acquisition module, the first image key data includes one or a combination of contour data, position coordinate data, medical status data, color data, three-dimensional feature data, and texture data.

[0029] In one embodiment, the step data construction module includes the following submodules:

[0030] A third acquisition module is used to receive at least one third node three-dimensional image data and obtain its generation time; sort the first node three-dimensional image data, the second node three-dimensional image data, and at least one third node three-dimensional image data according to the generation time, wherein the third node three-dimensional image data type is the same as the first node three-dimensional image data type;

[0031] A sub-series step data construction module is used to construct sub-series step data based on the three-dimensional image data corresponding to adjacent nodes for the sorted nodes, wherein the time step length of the sub-series step data is a divisor or common divisor of the time difference between each node;

[0032] The step data combination module is used to combine all obtained sub-series step data to obtain series step data.

[0033] In one embodiment, the rendering generation module is specifically used to determine a first time distance from the stepping time of each group of data in a series of stepping data to the occurrence time of the first node, and a second time distance to the occurrence time of the second node; if the first time distance is less than the second time distance, then the group of data replaces the first image key data of the three-dimensional image data of the first node, otherwise, replaces the second image key data of the three-dimensional image data of the second node.

[0034] This application constructs a series of stepping data of key image data based on existing three-dimensional image data, replaces key image data based on stepping data to obtain virtual three-dimensional image data, combines and renders virtual three-dimensional image data, and finally realizes the construction of dynamic three-dimensional image data. Using existing three-dimensional image data to construct dynamic three-dimensional image data can provide more intuitive diagnosis and treatment reference data while saving diagnosis and treatment costs, significantly improving the user experience of both doctors and patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a schematic diagram of a scenario in which the intelligent image processing method for urology surgery of the present application is applied;

[0036] Figure 2 is a flow chart of an intelligent image processing method for urology according to an embodiment of the present application;

[0037] Figure 3 is a module diagram of an intelligent image processing system for urology according to an embodiment of the present application;

[0038] Figure 4 It is a hardware structure diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] The present application will be described in detail below in conjunction with the various embodiments shown in the accompanying drawings. However, these embodiments do not limit the present application, and any structural, methodological, or functional changes made by a person of ordinary skill in the art based on these embodiments are all within the scope of protection of the present application.

[0040] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application 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 data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "corresponding to" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0041] See also Figure 1 In a typical system architecture of the present application, a server and a terminal may be included. A user may use the terminal to interact with the server to receive or send messages, etc. The urology intelligent image processing method disclosed in the present application may be executed by a server, and accordingly, the urology intelligent image processing system disclosed in the present application may be set in the server.

[0042] The user can instruct the server through the terminal to obtain the first-node three-dimensional image data and the second-node three-dimensional image data of the target object, and extract the first image key data and the second image key data respectively. Subsequently, the server constructs a series of step data based on the first image key data and the second image key data, and then replaces the first image key data of the first-node three-dimensional image data and / or replaces the second image key data of the second-node three-dimensional image data one by one based on each group of data therein, and renders and generates a dynamic three-dimensional image based on the replaced first-node three-dimensional image data and / or second-node three-dimensional image data.

[0043] Of course, in some system architectures, the urology intelligent image processing method disclosed in the present application may also be executed by a server cluster that can communicate with a terminal device. Accordingly, the urology intelligent image processing system disclosed in the present application may be arranged in a server cluster.

[0044] In a system architecture where the terminal can provide matching computing power, the urology intelligent image processing method disclosed in the present application can also be directly executed by the terminal. Accordingly, the urology intelligent image processing system disclosed in the present application can be set in the terminal.

[0045] In the various embodiments of the present application, the urology intelligent image processing method of the present application is exemplified by the three-dimensional image processing of urology. However, in some other scenarios, such as three-dimensional images of blood vessels and bones, the methods and systems provided in the various embodiments of the present application can also be applied, and the present application does not limit this.

[0046] Embodiment 1: Figure 2 As shown, an embodiment of the intelligent image processing method for urology provided by the present application is introduced, comprising the steps of:

[0047] S100, obtaining first node three-dimensional image data of a target object and extracting first image key data, and determining the type of the three-dimensional image data.

[0048] In one embodiment, in step S100, the first node three-dimensional image data is three-dimensional image data constructed and generated based on the original data acquired by the medical device for the target medical object. In a preferred embodiment, the three-dimensional image data is image data that complies with the DICOM standard, which at least contains patient-related information, including but not limited to basic patient information, examination equipment information, medical examination time, imaging configuration, etc.; exemplarily, the system applying the above method uses a PACS server as its data calculation and storage environment.

[0049] A common way to save DICOM files is that one medical image is saved as a DICOM file. If a medical image series is collected, such as a brain image series or a whole-body medical image series, it will be saved as a corresponding number of DICOM files. A DICOM file here refers to an independent file (such as a file with the suffix *.dcm). The image data of each DICOM file corresponds to an image of an image slice in the medical image sequence. In one embodiment, when determining the type of the three-dimensional image data, the DICOM data protocol is used to parse the three-dimensional image data to obtain type-related data.

[0050] In one embodiment, the first key image data is one of the following data or a combination thereof: contour data, position coordinate data, medical status data, color data, three-dimensional feature data, and texture data. For example, the contour data in the three-dimensional data of kidney morphology modeled can more accurately describe the kidney morphology, and the texture data can more accurately describe the smoothness, contusion, infiltration, and mucosal conditions of a certain surface; the color data of a certain kidney cyst can describe the color characteristics of the cyst; and the position coordinate data of a certain stone can accurately locate the position of the stone. Therefore, the first key image data is usually the image data that plays a major role in the formation of a three-dimensional image.

[0051] S110, obtaining the second-node three-dimensional image data of the target object, and extracting the second image key data from the second-node three-dimensional image data, wherein the second-node three-dimensional image data type is the same as the first-node three-dimensional image data type, and the first image key data and the second image key data have the same dimension; if the extraction is successful, proceed to S120, if the extraction fails, end.

[0052] In one embodiment, in step S110, the second node three-dimensional image data type being the same as the first node three-dimensional image data type means that the medical device type is the same and the target medical object type is the same. Three-dimensional image data of different types have different image data structures. The inventors have found in research and practical applications that when three-dimensional image data of different structures are used in constructing dynamic three-dimensional images, the errors of the dynamic three-dimensional images formed are extremely large, resulting in errors. The reason is that different types of three-dimensional image data lack associated key image data, resulting in large errors in the construction of series step data. Therefore, the significance of limiting the three-dimensional image data types to be the same is to keep the properties of the three-dimensional image data of different nodes synchronously corresponding to a certain extent, and the dimensions are consistent, so as to achieve it by using subsequent steps.

[0053] For example, the medical device used to obtain the first key image data is a nuclear magnetic resonance imaging device in hospital A, and the target medical object of the first key image data is a left kidney stone in the urinary system of patient A; the medical device used to obtain the second key image data is a nuclear magnetic resonance imaging device in hospital B, and the target medical object of the second key image data is the same left kidney stone in the urinary system of patient A. Then the type of the three-dimensional image data of the second node is the same as the type of the three-dimensional image data of the first node. In another embodiment, in order to improve the accuracy of dynamic three-dimensional image construction, the types of three-dimensional image data of different nodes can be more strictly limited, including but not limited to the model of the medical device, the configuration of the medical device, the type of data collected for the target medical object, etc.

[0054] The second image key data extracted from the second node three-dimensional image data should have the same dimension as the first image key data. There is no comparison significance between data of different dimensions. If the key data of the same dimension is not extracted, the construction fails. The key data of the same dimension represents the changes of the target medical object to be modeled at the time node, and this change constitutes the basis for generating dynamic three-dimensional images.

[0055] S120: constructing a series of step data based on the first image key data and the second image key data;

[0056] In one embodiment, step S120 includes the following sub-steps S122-S126:

[0057] S122, receiving at least one third-node 3D image data and obtaining its generation time; sorting the first-node 3D image data, the second-node 3D image data, and at least one third-node 3D image data according to the generation time, wherein the type of the third-node 3D image data is the same as the type of the first-node 3D image data;

[0058] S124, for the sorted nodes, construct sub-series step data based on the three-dimensional image data corresponding to the adjacent nodes in sequence, wherein the time step length of the sub-series step data is a divisor or common divisor of the time difference between each node;

[0059] S126, combining all obtained sub-series step data to obtain series step data.

[0060] A dynamic three-dimensional image can be constructed only when there are at least two nodes in the three-dimensional image data. The more nodes the three-dimensional image data has, the more accurate the dynamic three-dimensional image constructed. However, the three-dimensional image data is usually obtained by the patient through medical examinations at a cost. The present invention strikes a balance between the accuracy and cost of constructing a dynamic three-dimensional image. It does not rely on a large amount of three-dimensional image data to construct, but instead realizes the construction of a dynamic image based on the patient's existing three-dimensional image data that has been obtained at a cost.

[0061] For example, a patient undergoes an MRI examination at a certain time T1 in a certain hospital A. The examination result shows that the stone size is k1 (unit: mm), and a three-dimensional image of the stone is constructed (for example only, without discussion of medical rationality); the examination conclusion is that no intervention is required and the patient should remain under observation. Several months later, the patient undergoes another MRI examination at a certain hospital A at a certain time T2. The examination result shows that the stone size is k2 (unit: mm), and a three-dimensional image of the stone is constructed again. The examination conclusion is that no intervention is required and the patient should remain under observation. At this time, the above two three-dimensional image data are input into the system, and the key contour data are extracted respectively, which can reflect the changes in the patient's stones during the period of T1-T2.

[0062] Furthermore, based on the two sets of contour key data, a series of step data is obtained to simulate the change data of the patient's stones. On this basis, the patient finds that he has also undergone a physical examination of the same type at T' between T1 and T2, and the examination result is k' (unit mm). The conclusion of the examination is that no intervention is required and observation is maintained. Then, the physical examination 3D image data corresponding to T' can be obtained and the contour key data can be extracted, so that more accurate change data can be obtained based on the three examination data, thereby constructing a more accurate dynamic 3D image.

[0063] Furthermore, consider a new situation. After the preoperative examination, the patient obtains another set of examination data T3, and the examination result is k3 (unit: mm), which indicates that the patient has surgical signs. At this time, the dynamic three-dimensional image can be constructed based on the total four examination data to obtain accurate change data. It should be emphasized that the construction of the above dynamic three-dimensional image is almost all based on routine examination data without involving additional diagnosis and treatment costs. While saving costs for both doctors and patients, it improves the accuracy of the model, provides better and more intuitive diagnosis and treatment reference data, and significantly improves the user experience of both doctors and patients.

[0064] In a preferred embodiment, the time step of the series of step data is taken as the divisor or common divisor of the difference between the time of each node, preferably the greatest common divisor. For example, when the time of the first node is June 1 and the time of the second node is June 29, the time step can be taken as the divisor of the time difference, and it needs to be determined based on accuracy and efficiency. Here, it is assumed to be 7 days, then the time nodes of the series of step data are June 1, June 8, June 15, June 22, and June 29; when a new node is added, the new node time is obtained as July 7, then the time step is re-determined as the greatest common divisor of the time difference of 28 days and 8 days, 4 days. The above-mentioned time step value method is used to: (1) maintain the same step duration and evenly present the change process of the target medical object; (2) make the dynamic image displayed when constructing a three-dimensional dynamic image contain real node image data, thereby improving accuracy and reference.

[0065] S130: Based on each group of data in the series of step data, the first image key data of the first node three-dimensional image data and / or the second image key data of the second node three-dimensional image data are replaced one by one, and a dynamic three-dimensional image is generated based on the replaced first node three-dimensional image data and / or second node three-dimensional image data.

[0066] In one embodiment, in step S130, a first time distance from the step time of each group of data in the series of step data to the occurrence time of the first node and a second time distance to the occurrence time of the second node are determined. If the first time distance is less than the second time distance, the group of data replaces the first image key data of the three-dimensional image data of the first node, otherwise, it replaces the second image key data of the three-dimensional image data of the second node.

[0067] In another embodiment, in the step S130, since each group of data in the series of step data describes the change of the three-dimensional image, the first image key data of the first node three-dimensional image data and / or the second image key data of the second node three-dimensional image data are replaced one by one based on each group of data in the series of step data, the changing three-dimensional image can be displayed; and the changing three-dimensional image can be rendered to generate a dynamic three-dimensional image based on existing technologies (such as through key frame interpolation, data gradient).

[0068] Preferably, the key data is usually a combination of two or more multi-dimensional key data such as color, shape, position, etc. In one embodiment, the PACS server has three-dimensional image data of a patient at three nodes, the first node contains key data Key1 (M1, N1) (wherein Key1() represents a key data combination, M1 / N1 are key data of two dimensions, in this case it is a two-dimensional key data combination, the same below); the second node contains key data Key2 (M2, N2); the third node contains key data Key3 (M3, N3); after constructing a series of step data, a set of key data sets is obtained: Key1 (M1, N1), Key1-v1 (M11, N11), Key1-v2 (M12, N12), ..., Key1-vn (M1n, N1n), Key2 (M2, N2), Key2-v1 (M21, N21), Key2-v2 (M22, N22), ..., Key2-vn (M2n, N2n), Key3 (M3, N3). Each of the key data Key1-v1 to Key1-vn replaces Key1 (M1, N1) in the three-dimensional image data of the first node, thereby obtaining n virtual three-dimensional image data based on the three-dimensional image data of the first node. Similarly, each of the key data Key2-v1 to Key2-vn replaces Key2 (M2, N2) in the three-dimensional image data of the second node, thereby obtaining n virtual three-dimensional image data based on the three-dimensional image data of the second node. Each actual or virtual three-dimensional image data corresponds to a frame in the dynamic three-dimensional image, and the combination of all frames constructs the dynamic three-dimensional image data.

[0069] Embodiment 2: Figure 3 As shown, the present application also provides a urology intelligent image processing system, including:

[0070] A first acquisition module is used to acquire the first node three-dimensional image data of the target object and extract the first image key data to determine the type of the three-dimensional image data;

[0071] A second acquisition module acquires second-node three-dimensional image data of the target object, and extracts second image key data from the second-node three-dimensional image data, wherein the type of the second-node three-dimensional image data is the same as the type of the first-node three-dimensional image data, and the first image key data and the second image key data have the same dimension;

[0072] A step data construction module, used to construct a series of step data based on the first image key data and the second image key data;

[0073] A rendering generation module is used to replace the first image key data of the first node three-dimensional image data one by one and / or replace the second image key data of the second node three-dimensional image data one by one based on each group of data in the series of step data, and render and generate a dynamic three-dimensional image based on the replaced first node three-dimensional image data and / or second node three-dimensional image data.

[0074] In one embodiment, in the first acquisition module, the first node three-dimensional image data is three-dimensional image data constructed and generated based on the original data acquired by the medical device for the target medical object; in the second acquisition module, the second node three-dimensional image data type is the same as the first node three-dimensional image data type, indicating that the corresponding medical device type is the same and the target medical object type is the same.

[0075] In one embodiment, in the first acquisition module, the first image key data includes one or a combination of contour data, position coordinate data, medical status data, color data, three-dimensional feature data, and texture data.

[0076] In one embodiment, the step data construction module includes the following submodules:

[0077] A third acquisition module is used to receive at least one third node three-dimensional image data and obtain its generation time; sort the first node three-dimensional image data, the second node three-dimensional image data, and at least one third node three-dimensional image data according to the generation time, wherein the third node three-dimensional image data type is the same as the first node three-dimensional image data type;

[0078] A sub-series step data construction module is used to construct sub-series step data based on the three-dimensional image data corresponding to adjacent nodes for the sorted nodes, wherein the time step length of the sub-series step data is a divisor or common divisor of the time difference between each node;

[0079] The step data combination module is used to combine all obtained sub-series step data to obtain series step data.

[0080] In one embodiment, the rendering generation module is specifically used to determine a first time distance from the stepping time of each group of data in a series of stepping data to the occurrence time of the first node, and a second time distance to the occurrence time of the second node; if the first time distance is less than the second time distance, then the group of data replaces the first image key data of the three-dimensional image data of the first node, otherwise, replaces the second image key data of the three-dimensional image data of the second node.

[0081] In an embodiment of the present application, a series of stepping data of image key data is constructed based on existing three-dimensional image data, and virtual three-dimensional image data is obtained by replacing the image key data based on the stepping data. The virtual three-dimensional image data is combined and rendered to ultimately achieve the construction of dynamic three-dimensional image data. Using existing three-dimensional image data to construct dynamic three-dimensional image data can provide more intuitive diagnosis and treatment reference data while saving diagnosis and treatment costs, thereby significantly improving the user experience of both doctors and patients.

[0082] Figure 4 FIG. 1 shows a hardware structure diagram of an electronic device according to an embodiment of the present specification. Figure 4 As shown, the electronic device 30 may include at least one processor 31, a memory 32 (e.g., a non-volatile memory), a memory 33, and a communication interface 34, and the at least one processor 31, the memory 32, the memory 33, and the communication interface 34 are connected together via an internal bus 35. At least one processor 31 executes at least one computer-readable instruction stored or encoded in the memory 32.

[0083] It should be understood that the computer executable instructions stored in the memory 32, when executed, cause at least one processor 31 to perform the above combined operations in various embodiments of the present specification. Figure 1 to Figure 2 Describes the various operations and functions.

[0084] In the embodiments of the present specification, the electronic device 30 may include, but is not limited to, personal computers, server computers, workstations, desktop computers, laptop computers, notebook computers, mobile electronic devices, smart phones, tablet computers, cellular phones, personal digital assistants (PDAs), handheld devices, messaging devices, wearable electronic devices, consumer electronic devices, and the like.

[0085] According to one embodiment, a program product such as a machine-readable medium is provided. The machine-readable medium may have instructions (i.e., the above-mentioned elements implemented in the form of software), which, when executed by a machine, causes the machine to perform the above-mentioned combination of various embodiments of this specification. Figure 1-Figure 2Specifically, a system or device equipped with a readable storage medium may be provided, on which a software program code implementing the functions of any of the above-mentioned embodiments is stored, and a computer or processor of the system or device reads and executes the instructions stored in the readable storage medium.

[0086] In this case, the program code itself read from the machine-readable medium can realize the function of any one of the above embodiments, and thus the machine-readable code and the machine-readable storage medium storing the machine-readable code constitute part of this specification.

[0087] Examples of readable storage media include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD-RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer or a cloud via a communication network.

[0088] Those skilled in the art should understand that the various embodiments disclosed above can be modified and altered in various ways without departing from the essence of the invention. Therefore, the protection scope of this specification should be defined by the appended claims.

[0089] It should be noted that not all steps and units in the above-mentioned processes and system structure diagrams are necessary, and some steps or units can be ignored according to actual needs. The execution order of each step is not fixed and can be determined as needed. The device structure described in the above-mentioned embodiments can be a physical structure or a logical structure, that is, some units may be implemented by the same physical client, or some units may be implemented by multiple physical clients, or some components in multiple independent devices may be implemented together.

[0090] In the above embodiments, the hardware unit or module can be realized by mechanical or electrical means. For example, a hardware unit, module or processor can include permanent dedicated circuit or logic (such as special processor, FPGA or ASIC) to complete the corresponding operation. The hardware unit or processor can also include programmable logic or circuit (such as general-purpose processor or other programmable processor), which can be temporarily set by software to complete the corresponding operation. Specific implementation (mechanical method or dedicated permanent circuit or temporary circuit) can be determined based on cost and time consideration.

[0091] The specific embodiments described above in conjunction with the accompanying drawings describe exemplary embodiments, but do not represent all embodiments that can be implemented or fall within the scope of protection of the claims. The term "exemplary" used throughout this specification means "used as an example, instance or illustration" and does not mean "preferred" or "having advantages" over other embodiments. For the purpose of providing an understanding of the described technology, the specific embodiments include specific details. However, these technologies can be implemented without these specific details. In some instances, in order to avoid making the concepts of the described embodiments difficult to understand, well-known structures and devices are shown in block diagram form.

[0092] The above description of the present disclosure is provided to enable any person of ordinary skill in the art to implement or use the present disclosure. Various modifications to the present disclosure will be apparent to those of ordinary skill in the art, and the general principles corresponding to the present disclosure may be applied to other variations without departing from the scope of protection of the present disclosure. Therefore, the present disclosure is not limited to the examples and designs described herein, but is consistent with the widest range of principles and novel features disclosed herein.

Claims

1. A urology intelligent image processing method, characterized in that: The method comprises the steps of: S100, acquiring first node three-dimensional image data of a target object and extracting first image key data, and determining the type of the three-dimensional image data; S110, acquiring second-node 3D image data of the target object, and extracting second image key data from the second-node 3D image data, wherein the type of the second-node 3D image data is the same as the type of the first-node 3D image data, and the first image key data and the second image key data have the same dimension; S120, constructing a series of step data based on the first image key data and the second image key data; S130, based on each group of data in the series of step data, replace the first image key data of the first node three-dimensional image data one by one and / or replace the second image key data of the second node three-dimensional image data one by one, and render and generate a dynamic three-dimensional image based on the replaced first node three-dimensional image data and / or second node three-dimensional image data.

2. The method according to claim 1, characterized in that In step S100, the three-dimensional image data of the first node is three-dimensional image data constructed and generated based on the original data acquired by the medical device for the target medical object; In step S110 , the second node 3D image data type being the same as the first node 3D image data type indicates that the corresponding medical equipment types are the same and the target medical object types are the same.

3. The method according to claim 1, characterized in that In step S100, the first key image data includes one or a combination of contour data, position coordinate data, medical status data, color data, three-dimensional feature data, and texture data.

4. The method according to claim 1, characterized in that Step S120 specifically includes: S122, receiving at least one third-node 3D image data and obtaining its generation time; sorting the first-node 3D image data, the second-node 3D image data, and at least one third-node 3D image data according to the generation time, wherein the type of the third-node 3D image data is the same as the type of the first-node 3D image data; S124, for the sorted nodes, construct sub-series step data based on the three-dimensional image data corresponding to the adjacent nodes in sequence, wherein the time step length of the sub-series step data is a divisor or common divisor of the time difference between each node; S126, combining all obtained sub-series step data to obtain series step data.

5. The method according to claim 1, characterized in that Step S130 specifically includes: Determine a first time distance from the step time of each group of data in the series of step data to the occurrence time of the first node, and a second time distance to the occurrence time of the second node; If the first time distance is smaller than the second time distance, the group of data replaces the first image key data of the first node 3D image data; otherwise, the group of data replaces the second image key data of the second node 3D image data.

6. A urology intelligent image processing system, the system comprising: A first acquisition module is used to acquire the first node three-dimensional image data of the target object and extract the first image key data to determine the type of the three-dimensional image data; A second acquisition module acquires second-node three-dimensional image data of the target object, and extracts second image key data from the second-node three-dimensional image data, wherein the type of the second-node three-dimensional image data is the same as the type of the first-node three-dimensional image data, and the first image key data and the second image key data have the same dimension; A step data construction module, used to construct a series of step data based on the first image key data and the second image key data; A rendering generation module is used to replace the first image key data of the first node three-dimensional image data one by one and / or replace the second image key data of the second node three-dimensional image data one by one based on each group of data in the series of step data, and render and generate a dynamic three-dimensional image based on the replaced first node three-dimensional image data and / or second node three-dimensional image data.

7. The system according to claim 6, characterized in that In the first acquisition module, the first node three-dimensional image data is three-dimensional image data constructed and generated based on the original data acquired by the medical device for the target medical object; in the second acquisition module, the second node three-dimensional image data type is the same as the first node three-dimensional image data type, indicating that the corresponding medical device types are the same and the target medical object types are the same.

8. The system according to claim 6, characterized in that In the first acquisition module, the first image key data includes one or a combination of contour data, position coordinate data, medical status data, color data, three-dimensional feature data, and texture data.

9. The system according to claim 6, characterized in that The step data construction module includes the following submodules: A third acquisition module is used to receive at least one third node 3D image data and obtain its generation time; sort the first node 3D image data, the second node 3D image data, and at least one third node 3D image data according to the generation time, wherein the type of the third node 3D image data is the same as the type of the first node 3D image data; A sub-series step data construction module is used to construct sub-series step data based on the three-dimensional image data corresponding to adjacent nodes for the sorted nodes, wherein the time step length of the sub-series step data is a divisor or common divisor of the time difference between each node; The step data combination module is used to combine all obtained sub-series step data to obtain series step data.

10. The system according to claim 6, characterized in that The rendering generation module is specifically used to determine a first time distance from the stepping time of each group of data in the series of stepping data to the occurrence time of the first node, and a second time distance to the occurrence time of the second node; if the first time distance is less than the second time distance, then the group of data replaces the first image key data of the three-dimensional image data of the first node, otherwise, replaces the second image key data of the three-dimensional image data of the second node.

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