Medical imaging data processing method, device and computer readable medium

Abstract

The present application provides a method, device and computer-readable medium for processing medical image data. The method includes: obtaining medical image data; determining first information of a first layer, second information of a second layer and third information of a third layer according to the medical image data; wherein the first information is used to represent the analysis result of the medical image data, the second information is used to represent the lesion segmentation result of lesion segmentation of the medical image data based on the analysis result; the third information is used to represent the manual annotation result of manual annotation of the medical image data based on the lesion segmentation result; the first information of the first layer, the second information of the second layer and the third information of the third layer are fused to determine the target annotation image. It can at least be used to solve the technical problem that in the process of analyzing images, small lesions are easily missed, resulting in low accuracy of the diagnosis results based on medical image data.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to a method and device for processing medical image data and a computer-readable medium. Background Art

[0002] Digital medicine is a new type of modern medical treatment that applies modern computer technology and information technology to the entire medical process. In the rapid development of digital medicine, image analysis is crucial. In recent years, the amount of image data has increased exponentially, while the number of radiologists has increased extremely slowly. In addition, the complexity of images such as MR images is relatively high, which has led to an urgent need to improve the processing efficiency of medical image data.

[0003] In related technologies, it is generally necessary to accurately segment and quantify lesions based on medical image data to improve the accuracy of image analysis results. In some solutions, it is necessary to rely entirely on medical researchers to manually annotate lesions based on open source software such as ITK-snap to obtain lesion annotation results.

[0004] However, the inventors have discovered that there are at least the following technical problems in the related technology: since image data such as MR images are usually multi-series continuous images with a huge amount of data, and since it is necessary to manually screen out the lesion areas and mark them one by one, the workload of manual marking is huge, resulting in low efficiency in marking lesions and easy omission of tiny lesions. Summary of the invention

[0005] One object of the present application is to provide a method, device and computer-readable medium for processing medical image data, at least to solve the technical problems in the related art that the efficiency of lesion labeling is low and tiny lesions are easily missed.

[0006] To achieve the above objectives, some embodiments of the present application provide the following aspects:

[0007] In a first aspect, some embodiments of the present application also provide a medical image processing method, the method comprising: acquiring medical image data; determining first information of a first layer, second information of a second layer, and third information of a third layer based on the medical image data; wherein the first information is used to represent the analysis result of the medical image data, and the second information is used to represent the lesion segmentation result of lesion segmentation of the medical image data based on the analysis result; the third information is used to represent the manual annotation result of manual annotation of the medical image data based on the lesion segmentation result; the first information of the first layer, the second information of the second layer, and the third information of the third layer are fused to determine the target annotated image.

[0008] In a second aspect, some embodiments of the present application further provide a computer device, comprising: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, cause the processor to perform the method described above.

[0009] In a third aspect, some embodiments of the present application further provide a computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method as described above.

[0010] Compared with the related art, in the solution provided by the embodiment of the present application, medical image data is acquired; then, first information of a first layer is generated according to a parsing result obtained by parsing the medical image data; lesion segmentation is performed on the medical image data based on the parsing result to obtain a lesion segmentation result, so as to generate second information of a second layer; and based on the lesion segmentation result, the medical image data is further manually annotated to obtain a manual annotation result, so as to generate third information of a third layer; and then, the first information of the first layer, the second information of the second layer and the third information of the third layer are fused to determine a target annotated image. Through the solution provided in the embodiment of the present application, the target annotated image can quickly and accurately associate the information between each layer. Since the target annotated image includes the manual annotation results of the medical image data based on the lesion segmentation results, the lesion segmentation results can be automatically completed based on the AI ​​algorithm, which is conducive to avoiding the omission of tiny lesions; relevant personnel only need to adjust the annotation content based on the lesion segmentation results, and do not need to rely entirely on manual screening of lesion areas and annotate the lesion areas one by one. Therefore, the efficiency of lesion annotation can be improved, which is conducive to improving the accuracy of diagnostic results based on medical imaging data. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 An exemplary flowchart of a method for processing medical image data provided for some embodiments of the present application;

[0012] Figure 2 A schematic diagram of an exemplary flow chart of another method for processing medical image data provided for some embodiments of the present application;

[0013] Figure 3 In a method for processing medical image data provided in some embodiments of the present application, automatic annotation is performed according to the second information to obtain an exemplary flow chart of automatic annotation content;

[0014] Figure 4An exemplary flowchart of sub-steps of step S103 in a method for processing medical image data provided in some embodiments of the present application;

[0015] Figure 5 A schematic diagram of an exemplary flow chart of another method for processing medical image data provided for some embodiments of the present application;

[0016] Figure 6 An exemplary structural diagram of a computer device is provided for some embodiments of the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] Embodiment 1

[0019] Some embodiments of the present application provide a method for processing medical image data, such as Figure 1 and Figure 2 As shown, the method may include the following steps:

[0020] Step S101, acquiring medical imaging data;

[0021] Step S102, determining first information of a first layer, second information of a second layer, and third information of a third layer according to the medical image data; wherein the first information is used to represent the analysis result of the medical image data, the second information is used to represent the lesion segmentation result of lesion segmentation performed on the medical image data based on the analysis result; and the third information is used to represent the manual annotation result of manual annotation performed on the medical image data based on the lesion segmentation result;

[0022] Step S103: fusing the first information of the first layer, the second information of the second layer, and the third information of the third layer to determine a target annotated image.

[0023] The method can be applied to clinical auxiliary diagnosis scenarios; the above steps are described in detail below.

[0024] Regarding step S101, specifically, in some examples, the medical imaging data may be acquired through a medical imaging device, wherein the medical imaging data corresponds to Figure 2The medical imaging device may include but is not limited to: MR device, CT device, etc., and correspondingly, the medical imaging data may include but is not limited to: CT imaging data, MR imaging data, etc., which is not specifically limited in the embodiments of the present application.

[0025] With respect to step S102, specifically, in some examples, the method for determining the first information of the first layer may include: parsing the medical image data to obtain first information for representing the parsing result of the medical image data; and displaying the first information on the first layer. In some examples, the medical image data may be read by a medical imaging system installed on the medical imaging device. After the medical image data is acquired, the medical image data may be parsed to be displayed on the first layer (e.g., layer 1) for easy image browsing.

[0026] Specifically, in some examples, the method for determining the second information of the second layer may include: segmenting the lesions in the medical image data based on the analysis result to obtain second information for characterizing the lesion segmentation result of the medical image data; automatically annotating according to the second information to obtain automatic annotation content; and displaying the second information including the automatic annotation content on the second layer. In some examples, the lesions in the medical image data may be segmented based on the analysis result by an AI algorithm to be displayed on the second layer (e.g., layer 2).

[0027] Specifically, in some examples, the method for determining the third information of the third layer may include: obtaining manual annotation information issued based on the second information; obtaining third information for representing the manual annotation result of the medical image data according to the manual annotation information; and displaying the third information on the third layer. In some examples, manual annotation may be further performed on the basis of the automatically annotated content. For example, a doctor may manually annotate the lesions missed by the automatically annotated content, and the manual annotation result may be displayed on the third layer (e.g., layer 3).

[0028] Among them, in some embodiments of the present application, the third information for characterizing the manual annotation result of the medical image data according to the manual annotation information may further include: receiving the manual annotation information in the editing mode of the second information, and obtaining the third information for characterizing the manual annotation result of the medical image data; Here, the manual annotation information may include at least one of the following: a first annotation information for characterizing the deletion operation of the automatic annotation content, a second annotation information for characterizing the modification operation of the automatic annotation content, and a third annotation information for characterizing the newly added annotation content. That is to say, the user can directly change the automatic annotation content that is automatically annotated using the AI ​​algorithm. For example, if it is necessary to perform a deletion operation on the automatic annotation content in the second layer, the second layer can be unlocked so that the second information is in the editing mode and then the deletion operation is performed. Among them, the second information can also be in the locking mode. In addition, it should be noted that the deletion operation can specifically be a deletion operation on a lesion area that is incorrectly annotated in the automatic annotation content; the newly added annotation content can specifically be a new addition operation on a lesion area that is omitted in the automatic annotation content; the modification operation can specifically be based on the automatic annotation content. Perform local deletion, local addition and other operations to adjust the area range represented by a lesion area in the automatic annotation content.

[0029] For step S103, specifically, in some examples, the first information of the first layer, the second information of the second layer, and the third information of the third layer are fused to determine a target annotated image for display. In this way, the target annotated image is a lesion distribution map derived after the fusion of the automatic annotation by the AI ​​algorithm and the manual annotation, which can realize the fusion display of the lesion area annotated by AI and the lesion area annotated by manual.

[0030] Compared with the related art, in the scheme provided by the embodiment of the present application, the medical image data is obtained; then, the first information of the first layer is generated according to the analysis result obtained by analyzing the medical image data; the lesion segmentation result is obtained by performing lesion segmentation on the medical image data based on the analysis result to generate the second information of the second layer; and on the basis of the lesion segmentation result, the medical image data is further manually annotated to obtain the manual annotation result to generate the third information of the third layer; and then, the first information of the first layer, the second information of the second layer and the third information of the third layer are fused to determine the target annotated image. Through the scheme provided by the embodiment of the present application, the target annotated image can quickly and accurately associate the information between the various layers. Since the target annotated image includes the lesion segmentation result and the manual annotation result of the medical image data, it is easy to obtain a more accurate diagnosis result, which can solve the technical problem that small lesions are easily missed in the related art, and achieve the purpose of improving the accuracy of the diagnosis results based on medical image data.

[0031] Embodiment 2

[0032] Embodiment 2 of the present application is a further improvement made on the basis of Embodiment 1, and the specific improvement is: in Embodiment 2 of the present application, the second information used to characterize the lesion segmentation result of the medical image data at least includes: probability information of determining the target area as a lesion area;

[0033] Specifically, the automatic annotation according to the second information to obtain the automatic annotation content may further include the following steps: Figure 3 As shown:

[0034] Step S201, determining a preset transparency flag in combination with the probability information;

[0035] Step S202: automatically annotating the target area at the pixel level according to the preset transparency mark to obtain automatically annotated content.

[0036] Specifically, in some examples, the lesion segmentation result may include preset transparency marks corresponding to the interval of 0-255. In this way, the higher the probability value represented by the probability information of determining the target area as the lesion area, the higher the possibility that the corresponding target area is the lesion area, and the lower the transparency corresponding to the preset transparency mark; the lower the probability value represented by the probability information of determining the target area as the lesion area, the lower the possibility that the corresponding target area is the lesion area, and the higher the transparency corresponding to the preset transparency mark.

[0037] Specifically, in some examples, after the preset transparency flag is determined, the target area can be automatically annotated at the pixel level according to the preset transparency flag to obtain the automatically annotated content.

[0038] Furthermore, in some examples, when the first information of the first layer, the second information of the second layer, and the third information of the third layer are fused to determine the target annotated image for display, the doctor can adjust the transparency of the lesion displayed according to the preset transparency mark in the second layer at the pixel level based on the human-computer interaction interface, and finally obtain the manually corrected target annotated image.

[0039] Embodiment 3

[0040] Embodiment 3 of the present application is a further improvement made on the basis of embodiment 1, and the specific improvement lies in that: in embodiment 3 of the present application, a specific implementation method is provided for fusing the first information of the first layer, the second information of the second layer and the third information of the third layer to determine the target annotation image.

[0041] Specifically, in the third embodiment of the application, the first information of the first layer, the second information of the second layer and the third information of the third layer are fused to determine the target annotated image, that is, step S103 can further include the following steps: Figure 4 As shown:

[0042] Step S301, performing a downsampling operation according to the third information of the third layer to obtain a downsampling result; wherein, in the downsampling result, the resolution of the third layer is consistent with the resolution of the first layer and / or the second layer;

[0043] Step S302: Fusing the downsampling result, the first information of the first layer, and the second information of the second layer to determine a target annotated image.

[0044] Specifically, in some examples, the resolution of the second layer is the same as the resolution of the medical image data. For example, the resolution of typical MR image data in a hospital is 512*512. In the solution provided by the embodiment of the present application, the resolution of the third layer obtained is higher than the resolution of the second layer, so that it is convenient to more finely magnify the local area of ​​the image data represented by the third layer, so that the lesions can be supplemented and annotated more accurately to improve the annotation accuracy. Therefore, before the first information of the first layer, the second information of the second layer and the third information of the third layer are fused, a downsampling operation can be performed according to the third information of the third layer to obtain a downsampling result, so that the resolution of the third layer is consistent with the resolution of the first layer and / or the second layer, and then the downsampling result, the first information of the first layer and the second information of the second layer are fused to determine the target annotated image.

[0045] Furthermore, in some examples, the target annotated image, i.e., the lesion distribution map, can be fed back to the AI ​​algorithm in real time so that the AI ​​algorithm can tune the network parameters, so that the tuned AI algorithm can be used to determine the automatic annotation content subsequently, thereby further improving the accuracy of the automatic annotation content.

[0046] Furthermore, in some embodiments of the present application, the step of fusing the downsampling result, the first information of the first layer, and the second information of the second layer to determine the target annotated image may further include the following steps:

[0047] Step S3021, fusing the downsampling result, the first information of the first layer, and the second information of the second layer to determine a fused image;

[0048] Step S3022, binarizing the fused image to determine a target annotated image in a preset format; wherein the preset format at least includes: nii format.

[0049] Specifically, in some examples, if it is necessary to export a target annotated image in nii format that can be parsed by ITK-snap, the system can binarize the fused image and convert it into nii format for export. In this way, the doctor can conduct further research based on the exported target annotated image in nii format.

[0050] It is understandable that in some solutions of related technologies, medical researchers can use AI algorithms to assist in the annotation of lesions. However, it is generally necessary to manually export the AI ​​annotation results, and then use open source software such as ITK-snap for secondary analysis. In the process of secondary analysis, there are technical problems such as parsing failure and data loss due to mismatch of data communication interface. In an embodiment of the present application, by fusing the result obtained by the binarization process with the first information of the first layer and the second information of the second layer, the target annotated image in a preset format such as nii format can be directly output without manually exporting the target annotated image, and at the same time, technical problems such as parsing failure and data loss due to mismatch of data communication interface can be solved.

[0051] It should be noted that the embodiment of the present application may also be an improvement based on the second embodiment.

[0052] Embodiment 4

[0053] Embodiment 4 of the present application is a further improvement made on the basis of embodiment 1, and the specific improvement is: in embodiment 4 of the present application, after the first information of the first layer, the second information of the second layer and the third information of the third layer are fused to determine the target annotated image, the method may further include the following steps: Figure 5 As shown:

[0054] Step S401, performing pixel-level segmentation calculation according to the target annotated image to determine lesion volume data;

[0055] Step S402: displaying according to the lesion volume data and the target annotated image.

[0056] Specifically, in some examples, while displaying the target annotated image, the lesion volume data can also be displayed in real time for the doctor to make a diagnosis reference. In this way, it is convenient for the doctor to directly diagnose the patient's medical imaging data based on the lesion volume data and the target annotated image, which is conducive to further forming a structured report.

[0057] Embodiment 5

[0058] In addition, the present application also provides a computer device, the structure of which is as follows: Figure 6 As shown, the device includes a memory 10 for storing computer-readable instructions and a processor 20 for executing the computer-readable instructions, wherein when the computer-readable instructions are executed by the processor 20, the processor 20 is triggered to execute the described method.

[0059] The methods and / or embodiments in the embodiments of the present application may be implemented as computer software programs. For example, the embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. When the computer program is executed by the processing unit, the above functions defined in the method of the present application are executed.

[0060] It should be noted that the computer-readable medium described in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0061] In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0062] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0063] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0064] As another aspect, the embodiments of the present application further provide a computer-readable medium, which may be included in the device described in the above embodiments; or may exist independently without being assembled into the device. The above computer-readable medium carries one or more computer-readable instructions, which may be executed by a processor to implement the steps of the methods and / or technical solutions of the above multiple embodiments of the present application.

[0065] In a typical configuration of the present application, the terminal and the equipment of the service network each include one or more processors (CPU), input / output interface, network interface and memory.

[0066] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0067] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0068] In addition, an embodiment of the present application further provides a computer program, which is stored in a computer device, so that the computer device executes the method for controlling code execution.

[0069] It should be noted that the present application can be implemented in software and / or a combination of software and hardware, for example, can be implemented using an application specific integrated circuit (ASIC), a general purpose computer or any other similar hardware device. In certain embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including related data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the present application can be implemented using hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0070] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic features of the present application. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present application is limited by the attached claims rather than the above description, so it is intended to include all changes that fall within the meaning and scope of the equivalent elements of the claims in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A method for processing medical image data, characterized in that: The method comprises: Acquiring medical imaging data; Determine, according to the medical image data, first information of a first layer, second information of a second layer, and third information of a third layer; wherein the first information is used to represent a parsing result of the medical image data, the second information is used to represent a lesion segmentation result of lesion segmentation performed on the medical image data based on the parsing result; and the third information is used to represent a manual annotation result of manual annotation performed on the medical image data based on the lesion segmentation result; Performing a fusion process on the first information of the first layer, the second information of the second layer, and the third information of the third layer to determine a target annotated image; The method for determining the second information of the second layer includes: segmenting the lesions in the medical image data based on the analysis result to obtain second information for representing the lesion segmentation result of the medical image data; automatically annotating according to the second information to obtain automatic annotation content; and displaying the second information including the automatic annotation content on the second layer; the second information for representing the lesion segmentation result of the medical image data includes at least: probability information of determining the target area as the lesion area; The automatic annotation according to the second information to obtain the automatic annotation content includes: determining a preset transparency mark in combination with the probability information; and automatically annotating the target area at the pixel level according to the preset transparency mark to obtain the automatic annotation content; wherein the doctor can adjust the transparency of the lesion displayed according to the preset transparency mark in the second layer at the pixel level based on the human-computer interaction interface to obtain a manually corrected target annotation image; The method for determining the third information of the third layer includes: obtaining manual annotation information issued based on the second information; receiving the manual annotation information in the editing mode of the second information to obtain third information for representing the manual annotation result of the medical image data; wherein the manual annotation information includes at least one of the following: first annotation information for representing a deletion operation performed on the automatically annotated content, second annotation information for representing a modification operation performed on the automatically annotated content, and third annotation information for representing newly added annotation content; and displaying the third information on the third layer; After determining the target annotated image, the method further includes: performing pixel-level segmentation calculation according to the target annotated image to determine lesion volume data; and displaying according to the lesion volume data and the target annotated image.

2. The method according to claim 1, characterized in that The method for determining the first information of the first layer includes: The medical image data is parsed to obtain first information for representing the parsing result of the medical image data; and the first information is displayed on a first layer.

3. The method according to claim 1, characterized in that The fusing the first information of the first layer, the second information of the second layer, and the third information of the third layer to determine the target annotated image includes: Performing a downsampling operation according to the third information of the third layer to obtain a downsampling result; wherein, in the downsampling result, the resolution of the third layer is consistent with the resolution of the first layer and / or the second layer; A target annotated image is determined by fusing the downsampling result, the first information of the first layer, and the second information of the second layer.

4. The method according to claim 3, characterized in that The determining the target annotated image by fusing the downsampling result, the first information of the first layer, and the second information of the second layer includes: Fusing the downsampling result, the first information of the first layer, and the second information of the second layer to determine a fused image; Binarizing the fused image to determine a target annotated image in a preset format; Wherein, the preset format at least includes: nii format.

5. A computer device, characterized in that: The device comprises: one or more processors; and A memory storing computer program instructions, which when executed cause the processor to perform the method as claimed in any one of claims 1 to 4.

6. A computer readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method according to any one of claims 1 to 4.

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