Image analysis method and device, computing equipment and storage medium

Abstract

The invention provides an image analysis method and device, computing equipment and a storage medium. The method may comprise: obtaining a diagnostic demand description text from a user, the diagnostic demand description text being a natural language text describing a diagnostic demand related to a medical image; determining at least one operation to be executed based on the diagnosis demand description text, wherein each operation in the at least one operation corresponds to an independent operation calling interface; and processing the medical image based on an operation calling interface corresponding to the at least one determined operation to obtain analysis for the medical image.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, and in particular to an image analysis method, apparatus, computing device and storage medium. Background Art

[0002] With the rapid development of medical imaging technology, the demand for human image analysis in the medical field is increasing. How to analyze human images efficiently and accurately has become an important direction of current technological development.

[0003] The methods described in this section are not necessarily methods that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any method described in this section is considered to be prior art simply because it is included in this section. Similarly, unless otherwise indicated, the issues mentioned in this section should not be considered to have been recognized in any prior art. Summary of the invention

[0004] According to one aspect of the present disclosure, a computer-implemented image analysis method is provided, comprising: obtaining a diagnostic requirement description text from a user, wherein the diagnostic requirement description text is a natural language text that describes a diagnostic requirement related to a medical image; determining at least one operation to be performed based on the diagnostic requirement description text, wherein each of the at least one operation corresponds to a separate operation call interface; and processing the medical image based on the operation call interface corresponding to the determined at least one operation to obtain an analysis result for the medical image.

[0005] According to another aspect of the present disclosure, an image analysis device is provided, comprising: a text obtaining unit, used to obtain a diagnostic requirement description text from a user, wherein the diagnostic requirement description text is a natural language text that describes the diagnostic requirement related to a medical image; an operation determination unit, used to determine at least one operation to be performed based on the diagnostic requirement description text, wherein each of the at least one operation corresponds to a separate operation calling interface; and an analysis unit, used to process the medical image based on the operation calling interface corresponding to the determined at least one operation, to obtain an analysis result for the medical image.

[0006] According to another aspect of the present disclosure, a computing device is provided, comprising: a memory, a processor, and a computer program stored on the memory, wherein the processor is configured to execute the computer program to implement an image analysis method according to one or more embodiments of the present disclosure.

[0007] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the image analysis method according to one or more embodiments of the present disclosure is implemented.

[0008] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program implements the image analysis method according to one or more embodiments of the present disclosure when executed by a processor.

[0009] These and other aspects of the disclosure will be apparent from and elucidated with reference to the embodiments described hereinafter. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0011] Figure 1 is a schematic diagram illustrating an example system in which the various methods described herein may be implemented according to an exemplary embodiment;

[0012] Figure 2 is a flow chart illustrating an image analysis method according to an exemplary embodiment;

[0013] Figure 3 is a schematic diagram of an image application scenario according to an exemplary embodiment;

[0014] Figure 4 is a schematic block diagram illustrating an image analysis apparatus according to an exemplary embodiment;

[0015] Figure 5 is a block diagram illustrating an exemplary computer device that can be applied to the exemplary embodiments. DETAILED DESCRIPTION

[0016] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0017] The terms used in the description of various examples described in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. As used herein, the term "plurality" means two or more, and the term "based on" should be interpreted as "based at least in part on". In addition, the terms "and / or" and "at least one of..." cover any one of the listed items and all possible combinations.

[0018] Exemplary embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0019] Figure 1 is a schematic diagram illustrating an example system 100 in which the various methods described herein may be implemented, according to an example embodiment.

[0020] refer to Figure 1 The system 100 includes a client device 110 , a server 120 , and a network 130 that communicatively couples the client device 110 and the server 120 .

[0021] The client device 110 includes a display 114 and a client application (APP) 112 that can be displayed via the display 114. The client application 112 can be an application that needs to be downloaded and installed before running or a small program (liteapp) as a lightweight application. In the case where the client application 112 is an application that needs to be downloaded and installed before running, the client application 112 can be pre-installed on the client device 110 and activated. In the case where the client application 112 is a small program, the user 102 can directly run the client application 112 on the client device 110 by searching for the client application 112 in the host application (for example, by the name of the client application 112, etc.) or scanning the graphic code of the client application 112 (for example, a bar code, a QR code, etc.), without installing the client application 112. In some embodiments, the client device 110 can be any type of mobile computer device, including a mobile computer, a mobile phone, a wearable computer device (for example, a smart watch, a head-mounted device, including smart glasses, etc.) or other types of mobile devices. In some embodiments, the client device 110 may alternatively be a stationary computer device, such as a desktop computer, a server computer or other types of stationary computer devices. In some optional embodiments, the client device 110 may also be or include a medical image printing device.

[0022] The server 120 is typically a server deployed by an Internet Service Provider (ISP) or an Internet Content Provider (ICP). The server 120 may represent a single server, a cluster of multiple servers, a distributed system, or a cloud server that provides basic cloud services (such as cloud databases, cloud computing, cloud storage, and cloud communications). It will be understood that although Figure 1 The server 120 is shown in FIG. 1 in communicating with only one client device 110 , but the server 120 may provide background services for multiple client devices simultaneously.

[0023] Examples of network 130 include a local area network (LAN), a wide area network (WAN), a personal area network (PAN), and / or a combination of communication networks such as the Internet. Network 130 can be a wired or wireless network. In some embodiments, the data exchanged through network 130 is processed using technologies and / or formats including hypertext markup language (HTML), extensible markup language (XML), etc. In addition, encryption technologies such as secure socket layer (SSL), transport layer security (TLS), virtual private network (VPN), Internet protocol security (IPsec) can also be used to encrypt all or some links. In some embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above-mentioned data communication technologies.

[0024] The system 100 may also include an image acquisition device 140. In some embodiments, Figure 1 The image acquisition device 140 shown can be a medical scanning device, including but not limited to scanning or imaging devices used in positron emission tomography (PET), positron emission tomography with computerized tomography (PET / CT), single photon emission computed tomography with computerized tomography (SPECT / CT), computerized tomography (CT), medical ultrasonography, nuclear magnetic resonance imaging (NMRI), magnetic resonance imaging (MRI), cardiovascular angiography (CA), digital radiography (DR), etc. For example, the image acquisition device 140 may include a digital subtraction angiography scanner, a magnetic resonance angiography scanner, a tomography angiography scanner, a positron emission tomography scanner, a positron emission computed tomography scanner, a single photon emission computed tomography scanner, a computed tomography scanner, a medical ultrasound examination device, a nuclear magnetic resonance imaging scanner, a magnetic resonance imaging scanner, a digital radiography scanner, etc. The image acquisition device 140 may communicate with a server (e.g., Figure 1The image processing system is connected to the server 120 in the figure or a separate server of the imaging system (not shown in the figure) to realize image data processing, including but not limited to converting the scan data (for example, converting it into a medical image sequence), compressing it, correcting its pixels, and reconstructing it in three dimensions.

[0025] The image acquisition device 140 may be connected to the client device 110 , for example, via the network 130 , or directly connected to the client device in other ways to communicate with the client device.

[0026] Optionally, the system may further include an intelligent computing device or computing card 150. The image acquisition device 140 may include or be connected (e.g., removably connected) to such a computing card 150, etc. As an example, the computing card 150 may implement image data processing, including but not limited to conversion, compression, pixel correction, reconstruction, etc. As another example, the computing card 150 may implement an image analysis method according to an embodiment of the present disclosure.

[0027] The system may also include other parts not shown, such as a data storage unit. The data storage unit may be a database, a data repository, or one or more devices for data storage in other forms, and may be a conventional database, or may include a cloud database, a distributed database, etc. For example, direct image data formed by the image acquisition device 140 or a medical image sequence or three-dimensional image data obtained through image processing may be stored in the data storage unit for subsequent retrieval by the server 120 and the client device 110 from the data storage unit. In addition, the above-mentioned image acquisition device 140 may also directly provide the image data or the medical image sequence or three-dimensional image data obtained through image processing to the server 120 or the client device 110, etc.

[0028] The user can use the client device 110 to control the acquisition of images or videos, view the acquired images or videos (including preliminary image data or images that have been analyzed and processed, etc.), view the analysis results, interact with the acquired images or analysis results, input acquisition instructions, configure data, etc. The client device 110 can send configuration data, instructions or other information to the image acquisition device 140 to control the acquisition of the image acquisition device, process data, etc.

[0029] For the purpose of the embodiments of this disclosure, Figure 1In the example of , the client application 112 may be an image sequence management application, which may provide various functions, such as storage management, indexing, sorting, and classification of the acquired image sequences. Correspondingly, the server 120 may be a server used together with the image sequence management application. The server 120 may provide image sequence management services to the client application 112 running in the client device 110 based on user requests or instructions generated according to the embodiments of the present disclosure, such as managing the image sequence storage in the cloud, storing and classifying the image sequence according to a specified index (including, for example, but not limited to, sequence type, patient identification, body part, acquisition target, acquisition stage, acquisition machine, whether lesions are detected, severity, etc.), and retrieving and providing the image sequence to the client device according to the specified index, etc. Alternatively, the server 120 may also provide or allocate such service capabilities or storage space to the client device 110, and the client application 112 running in the client device 110 may provide corresponding image sequence management services according to user requests or instructions generated according to the embodiments of the present disclosure, etc. It is to be understood that the above is only an example, and the present disclosure is not limited thereto.

[0030] Figure 2 2 is a flowchart illustrating an image analysis method 200 according to an exemplary embodiment. The method 200 may be performed on a client device (eg, Figure 1 The execution of each step of the method 200 may be performed at the client device 110 shown in FIG. Figure 1 In some embodiments, the method 200 may be performed on a server (e.g., Figure 1 In some embodiments, method 200 may be performed by a client device (eg, client device 110) and a server (eg, server 120) in combination.

[0031] In the following, each step of the method 200 is described in detail.

[0032] refer to Figure 2 At step 210, a diagnosis requirement description text is obtained from a user, where the diagnosis requirement description text is a natural language text describing the diagnosis requirement related to the medical image.

[0033] At step 220 , at least one operation to be performed is determined based on the diagnosis requirement description text, and each of the at least one operation corresponds to a separate operation calling interface.

[0034] At step 230, the medical image is processed based on an operation calling interface corresponding to the determined at least one operation to obtain an analysis result for the medical image.

[0035] Through the above method, the operation interface can be called based on the diagnostic needs to analyze and process the image to obtain the analysis result. Therefore, it can integrate language understanding ability and various image processing and image analysis capabilities to generate interpretable analysis results.

[0036] For example, the diagnosis requirement description text may be a natural language text describing a diagnosis requirement related to at least one medical purpose, and the analysis result may be an analysis result regarding the diagnosis requirement.

[0037] Exemplarily, the operation call interface may be a call interface for a device, apparatus, or resource with operation capability that can implement a corresponding operation, such as a call interface for a specific computing resource, an image processing resource, etc., and the present disclosure is not limited thereto. Exemplarily, the method may include determining an operation call interface instruction of an operation call interface corresponding to at least one operation to be performed, and processing the medical image based on the operation call interface corresponding to the determined at least one operation may include calling a resource with corresponding operation capability through an operation call interface instruction, etc., thereby processing the medical image. It is understandable that the operation call interface instruction may include hardware instructions, software instructions, computer program codes, or other instruction forms that can be understood by those skilled in the art.

[0038] According to some embodiments, the at least one operation to be performed may be determined by processing the diagnostic requirement description text by a large language model. Exemplarily, the diagnostic requirement description text may be processed by a large language model to obtain a corresponding operation or operation interface. Exemplarily, an instruction for calling a corresponding operation calling interface, such as a program code, may be generated with the aid of a large language model.

[0039] Exemplarily, the diagnosis requirement description text may describe one or more operations required for diagnosis defined by the doctor, such as one or more functional functions, etc. Through the method of the embodiment of the present disclosure, the algorithm execution process can be strung together in combination with the understanding ability of the large language model. For example, the doctor may define how to split the function, and the large language model understands such a language definition and calls them separately to perform the operations required by the doctor.

[0040] For example, an operation function library can be established or otherwise maintained, and the understanding ability of the large language model can be used for connection. Doctors can define the medical function implementation in the form of a diagnostic requirement description text. For example, the diagnostic requirement description text can include "by comparing CT and DSA images, see xxxx", and based on this, relevant functional operations such as target extraction, registration, and target measurement can be called.

[0041] According to one or more embodiments of the present disclosure, functions can be integrated based on diagnostic requirements.

[0042] As a specific non-limiting embodiment, the medical image may include an ultrasound image and an enhanced ultrasound image, in which the lymph and surrounding areas are collected, the area of ​​interest is the lymph area, and the doctor wants to view the signal difference of the lymph area in the two areas. The doctor can specify two image sources in the requirement text and specify the signal difference of the lymph area of ​​interest. According to the embodiments of the present disclosure, such a requirement text can be converted into a corresponding functional algorithm for identifying the lymph area in the two images, aligning the two images, and performing subtraction operations, and the operation call interface corresponding to these functions can be called.

[0043] In some cases, the provided diagnostic requirement description text may not meet the requirements. For example, after analyzing the diagnostic requirement description text and decomposing the instructions through algorithms such as large language models, it is found that there are problems with the connection or logic between the instructions, or after calling the corresponding operation interface for processing, it is found that the operation cannot be continued. In this case, a question text can be output to the user as a prompt.

[0044] In such an embodiment, the method may further include obtaining a user's response to the challenge text, and calling the predetermined operation instruction library may also be based on the response.

[0045] Continuing with the previous example of lymphatic images, for example, if it is found that an accurate recognition result cannot be obtained or the confidence is insufficient during the recognition process of the lymphatic area, or if the results recognized in the ultrasound image and the enhanced ultrasound image cannot be aligned, a query text can be output as a prompt to allow the user to perform manual input, such as indicating which is the correct area through language, circling the lymphatic area or the approximate lymphatic area through a drawing tool, clicking on the correct area, or excluding the wrong area, etc.

[0046] Exemplarily, the user's reply to the query text may be a reply text, such as a reply text using natural language logic. For example, the reply text may be processed to obtain additional operations, operation call interfaces, operation parameters, etc. Exemplarily, obtaining the user's reply to the query text may include receiving a user's voice input, recognizing the voice, and further processing the recognized text, such as using a large language model to process, to obtain a modified operation, an additional operation, or an updated operation, etc.

[0047] According to some embodiments, the query text may be used to inquire the user about the diagnostic requirement description text. For example, based on missing information, logical jumps, incomplete steps, ambiguous information, etc. in the diagnostic requirement description text, a query text may be generated to be output to the user. Exemplarily, the query text may be output to the user in a voice manner.

[0048] Exemplarily, the challenge text may be output to the user in response to the challenge condition being satisfied. As described below in conjunction with various exemplary embodiments, the challenge condition may include ambiguity, contradiction, missing, selection, mismatch, etc., and it is understood that the present disclosure is not limited thereto. Based on the specific details of satisfying the challenge condition, the corresponding challenge text may be obtained in a generated or other manner, thereby obtaining a targeted response.

[0049] According to some embodiments, the at least one operation determined may include a first operation and a second operation performed after the first operation. In such an example, the method may further include: processing the medical image based on the first operation to obtain a first intermediate result; and in response to determining that the first intermediate result is inconsistent with the second operation, determining that the challenge condition is satisfied.

[0050] Exemplarily, determining that the first intermediate result and the second operation are inconsistent may include determining that the first intermediate result is incompatible with the operation call interface corresponding to the second operation. For example, the interface corresponding to the second operation requires certain specific quantities, such as medical images, processed medical images, data or intermediate variables extracted from medical images, or other information related to medicine or patients to be analyzed, but the prior part (e.g., the first operation) described in the diagnostic requirement description text cannot obtain or generate the relevant quantity.

[0051] In such a case, a challenge text may be output to the user to make up for the deficiency between the two operations, such as requiring an additional operation, correcting the second operation, etc. Exemplarily, the method may further include generating a challenge text based on the determined contradiction.

[0052] According to some embodiments, the method may further include modifying the at least one operation or obtaining at least one additional operation based on the reply, thereby obtaining at least one updated operation. For example, a large language model may be used to modify the at least one determined operation or obtain at least one additional operation based on the reply text (and in some optional embodiments, images, user instructions, marks, etc. accompanying the reply). Exemplarily, an operation call interface corresponding to the updated operation may be generated, such as an operation call instruction, code, etc. Exemplarily, obtaining the analysis result for the medical image may include processing the medical image based on the at least one updated operation, such as calling a corresponding operation call interface based on the at least one updated operation.

[0053] According to some embodiments, each operation of at least one determined operation may correspond to an operation instruction calling interface in an operation instruction library, and wherein processing the medical image based on the operation calling interface corresponding to at least one determined operation includes calling the operation instruction library based on the determined operation instruction calling interface.

[0054] For example, the determined at least one operation may be represented as an operation sequence, and a predetermined operation instruction library may be called in sequence based on an operation calling interface corresponding to the operation sequence.

[0055] Exemplarily, the method may include obtaining one or more medical images about the target human body, such as acquiring, controlling acquisition, or retrieving, reading, or otherwise obtaining one or more medical images about the target human body from an already acquired image library. Exemplarily, the step of obtaining one or more medical images about the target human body may be before, after, or simultaneously with step 210. As a specific non-limiting example, at least a portion of the one or more medical images may be acquired according to the determined operation, for example, the determined operation may indicate which images are read or acquired.

[0056] Exemplarily, the determined operation may correspond to a flow chart, an operation step, etc. Exemplarily, the diagnosis requirement description text may be a diagnosis requirement description text from a user. Exemplarily, the large language model may be used to directly generate instructions or program codes for calling a corresponding computing resource interface.

[0057] Exemplarily, at least one operation may include an image acquisition operation. In such an example, the method may further include obtaining the medical image or at least one additional medical image based on the image acquisition operation, and the analysis result may be determined by processing and analyzing the medical image or the additional medical image obtained by the image acquisition operation. In such an example, the operation call interface corresponding to the image acquisition operation may be an interface to an operation resource having image acquisition capability, such as an image acquisition device, an image storage device, an image input device, etc.

[0058] Exemplarily, the image acquisition operation may correspond to a description of a medical image required to achieve a medical purpose in the diagnosis requirement description text. Exemplarily, the method further comprises acquiring at least a portion of the one or more medical images based on at least one image acquisition operation in the determined operations.

[0059] For example, the image acquisition operation may correspond to acquiring a medical image for the target human body through at least one image acquisition device, and the corresponding operation call interface may be an acquisition operation call interface for the image acquisition device. For another example, the image acquisition operation includes an operation for acquiring a medical image related to the target human body from a medical image library, and the corresponding operation call interface may be a retrieval operation call interface for the image library.

[0060] The image acquisition operation may indicate at least one of the following parameters: for the example of the acquisition operation, the acquisition device type, the acquisition parameters, and the acquisition location; for the example of the retrieval operation, the medical image acquisition time, the medical image type, the medical image parameters, the medical image acquisition location, etc. Exemplarily, the medical image acquisition time may correspond to the image acquisition time, for example, the image comparison follow-up in the last few days, the last year, and every three months, etc. The medical image type may correspond to the acquisition device, whether it is contrasted, whether it is enhanced, etc. The medical image acquisition location may correspond to the lungs, the brain, or other contents that can be understood by those skilled in the art. The medical image parameters may correspond to various medical image parameters that can be understood by those skilled in the art, and as a non-limiting example, may include image clarity, acquisition device version, 2D or 3D, etc. As a specific non-limiting example, based on natural language such as "take a lung CT of the patient", it can be determined that the corresponding image acquisition operation is acquisition, the acquisition device is a CT scanning system, the corresponding interface needs to be called, the acquisition location is the lung area of ​​the target human body (and the corresponding scanning coordinate axis or scanning movement range can be further generated), and other parameters such as scanning accuracy, intensity, etc. can be determined. These parameters can be passed into the corresponding calling interface to perform the collection operation.

[0061] As a further non-limiting example, at least one operation to be performed may include first determining whether there are existing images that meet the conditions, and searching if the conditions are met, and outputting text to the user to confirm whether to collect if the conditions are not met. For example, in such a case, the absence of existing images that meet the conditions or the number, accuracy, quality, etc. of existing images that are not enough for processing can be regarded as meeting the query conditions. Exemplarily, after obtaining the reply text from the user, additional image acquisition / image retrieval operations can be obtained based on the reply text, and the image acquisition device can be controlled to collect and retrieve images through the corresponding operation call interface, etc. For example, corresponding to the natural language of "see if the patient's lung CT has xxx", the existing image library, patient information or other medical database can be first retrieved or otherwise queried to determine whether there are images that meet the conditions, and if so, read them, if not or the conditions are not met (for example, not recent, the acquisition accuracy does not meet the requirements, or there are other problems that do not meet the image processing requirements, etc.), it can be determined that the query conditions are met, and the query text is output to the user to determine whether to obtain additional images or input additional images by the user, etc.

[0062] The determined operation may, for example, include an operation for processing at least one of the one or more medical images to obtain at least one processing result. For example, such an operation may correspond to a description of the processing of the medical image and / or one or more variables required to achieve the medical purpose in the diagnostic requirement description text. For example, the corresponding operation resource may identify at least one target in at least one of the one or more medical images, and at least one intermediate processing result may include an identification result for the at least one target, and the present disclosure is not limited to this.

[0063] For example, such an image processing operation may correspond to an explicit description in the diagnostic requirement description text such as "want to determine whether there is a plaque", or may correspond to an implicit description that includes "plaque in the blood vessel" but does not explicitly indicate that it is to be identified or judged. For example, the diagnostic requirement description text may not explicitly specify that a specific target needs to be identified, but may only mention a specific target in the image, or mention other processing such as comparison and measurement of a specific target in the image. For example, the diagnostic requirement description text may correspond to first identifying the specific target, and then performing other optional corresponding processing. Based on such a diagnostic requirement description text, the corresponding interfaces may be called in sequence for processing.

[0064] As a specific non-limiting example, the diagnostic requirement description text may include "need to measure the stenosis rate based on the plaque in the blood vessel", which does not explicitly indicate the identification of the plaque in the blood vessel. Correspondingly, by processing the diagnostic requirement description text, it can be determined that the corresponding operation first includes the identification of the vascular plaque, optionally includes segmentation, and then includes measurement, etc. As another example, the diagnostic requirement description text may include "need to measure the stenosis rate", and it can be determined through the understanding ability of the large language model that the measurement process of the stenosis rate includes the identification of the vascular plaque, etc.

[0065] In some embodiments, there may be separate measurement operation resources, such as resources dedicated to measuring specific locations or distances between objects in a segmented image, and thus after segmentation, the segmentation result may be used to call a corresponding measurement resource interface.

[0066] Continuing with the example of stenosis rate, there may be stenosis rate = [1 - (stenotic artery diameter ÷ normal artery diameter)] x 100%. More specifically, for carotid artery stenosis rate, there may be a NASCET stenosis rate calculation method and an ECST stenosis rate calculation method.

[0067] For the NASCET stenosis rate calculation method, stenosis rate = (normal diameter distal to the stenosis - the narrowest diameter of the stenosis segment) / normal diameter distal to the stenosis. Figure 3 , you can have

[0068]

[0069] The calculation method for ECST stenosis rate is: stenosis rate = (estimated initial diameter at the narrowest diameter of the stenosis segment - the narrowest diameter of the stenosis segment) / estimated initial diameter at the narrowest diameter of the stenosis segment. Figure 3 , you can have

[0070]

[0071] Therefore, for the NASCET stenosis rate calculation method, the values ​​a and b can be measured. For the ECST stenosis rate, c and a can be measured. These measurement values ​​or corresponding measurement positions corresponding to specific medical purposes or medical analysis purposes can be pre-stored or read through an interface with a predetermined medical resource library.

[0072] As a specific non-limiting example, the diagnostic requirement description text includes "measure stenosis rate". In the process of calling the corresponding computing resources, it is found that there are two ways to calculate the stenosis rate. In other words, in such a case, it can be considered that there is ambiguity. In the case of ambiguity, it can be considered that the query condition is met, and the query text can be output to the user to disambiguate, such as "Do you want the NASCET stenosis rate calculation method or the ECST stenosis rate calculation method?"

[0073] As another non-limiting example, in the process of calling the corresponding computing resources, it may be found that there is a mismatch or missing between the conditions given in the diagnostic requirement description text and the interface of the computing resource. For example, the called computing resource requires three variables to complete the measurement or analysis of a certain result, but there are only two variable definitions currently. In this case, it can be considered that the query condition is met, and the query text can be output to the user to supplement the missing information, such as "Calculating xx requires yy data, how can yy data be obtained?" or "What is the patient's yy information?"

[0074] For another example, continuing with the example of stenosis rate in the previous text, when there are multiple calculation methods and measurement methods, a query text can be directly output to the user to let the user determine which specific locations to measure data. For example, in this case, the query text may include "Where do I start measuring the diameter of the non-stenotic part?", and for example, the user reply text "Measure the distal diameter" can be received. Exemplarily, an image (or part of an image) can be output while outputting the query text, and the reply received from the user may not be limited to text, but may also additionally or alternatively include marks made by the user on the image, etc. For example, a marking tool and a box selection tool may be provided so that the user can mark the parts to be measured and identified in the image, etc., and the information input in this way can be input as parameters, etc. into the corresponding operation call interface for corresponding operation processing.

[0075] Exemplarily, the determined operation may include calculating at least a portion of the at least one intermediate result according to a rule corresponding to the diagnosis requirement description text to obtain a calculated intermediate result.

[0076] Exemplarily, the rules corresponding to the diagnosis requirement description text may be rules defined in the diagnosis requirement description text, such as rules defined explicitly or implicitly, or may correspond to the diagnosis requirements in the diagnosis requirement description text in other ways.

[0077] Continuing with the example of stenosis rate, the calculation operation can determine the corresponding intermediate results based on the calculation formulas, calculation rules, etc. that are pre-stored, read from the medical resource library through the corresponding interface, or otherwise obtained, and the calculation operation can call other computing resources with computing capabilities. Exemplarily, the calculation operation may include outputting the intermediate results, such as controlling the display system to output. Alternatively, the intermediate results may not be output to the user but only stored or archived, provided to the user only when the user actively requests, or output to the user together with the final result or final analysis report, and the present disclosure is not limited to this.

[0078] Exemplarily, the measurement and calculation functions may be combined into a single operation, for example, a single computing resource having the capability to measure and calculate stenosis, or they may be separate computing resources and use separate interfaces, and the present disclosure is not limited thereto.

[0079] Exemplarily, the determined operation may include an analysis operation for obtaining an analysis result for the target human body. For example, the need for an analysis operation may be determined based on a statement in the diagnostic requirement description text that explicitly indicates the analysis result or a statement that implicitly mentions the analysis result. As a specific non-limiting example, the explicit statement may be, for example, "determine the lesion grade", and the implicit instruction may be, for example, "based on the lesion grade, perform xx treatment" or "if the grade result is XX, then there is a possibility that XX exists", etc. For example, the analysis result may include a grade result for at least one target identified in the one or more medical images.

[0080] As another specific non-limiting example scenario, for a CT image, a doctor may need to obtain its segmentation and centerline, and perform some post-processing operations based on the centerline, such as generating a straightening image or a short axis image. In addition, when viewing the image, the doctor may need to correspond the post-processed image to the original CT image, that is, to obtain the "linkage" between the post-processed image and the original image. For example, a link relationship between the two images can be established through the centerline.

[0081] According to one or more embodiments of the present disclosure, doctors can customize such linkage. For example, a user (doctor) can speak or type a demand text in the form of: enlarge the image, add sharpness, align / subtract / invert the two images. According to such an embodiment, the doctor can define the function and call the existing technology to realize the processing of relevant medical images or images. For example, in such an example, the diagnosis requirement description text may include "linking two images through the centerline, and the centerline is extracted by segmentation". As another example, the diagnosis requirement description text may include "linking two images through the centerline", and the operation interface of the centerline extraction algorithm can be called automatically according to the method of the present disclosure. As yet another example, in the case where the diagnosis requirement description text only includes "linking two images through the centerline", the question text "How to extract the centerline?" can be output, and the corresponding algorithm operation is called based on the user's further supplementary reply.

[0082] As some examples, the underlying processing operations on medical images may include segmentation, tissue recognition, registration, strategy, calculation of indicators through mathematical / physical models, centerline, boundary extraction, joint points, abnormality recognition, tissue recognition, target tracking / tracing, 3D visualization / rendering / reconstruction, etc. As some other specific non-limiting embodiments, the processing operations on medical images may include extraction of statistical indicators, multimodal mathematical operations, morphological operations (such as dilation, erosion), grayscale operations, etc. As some further specific non-limiting embodiments, the processing operations on medical images may include application-level planning, advanced measurement, target extraction, etc.

[0083] Result grading can utilize the understanding ability of the large language model. For example, result grading can include fuzzy grading, or the diagnostic requirement description text can include fuzzy instructions, such as "when xxxx, it is likely to be malignant". The diagnostic requirement description text can contain multiple judgment conditions, such as multiple fuzzy judgment conditions, for example, "the lesion length is greater than xx cm or there is xx tissue around it, which may indicate that the lesion is malignant" or "if the stenosis rate is too high and the surrounding xx tissue presents xx morphology, then there is a high probability that xx exists". The large language model can understand these judgment conditions separately, judge them separately, and give the final judgment result. Exemplarily, the large language model can give the judgment result and judgment probability corresponding to each judgment condition, and integrate based on each judgment result to give the final analysis result. For example, the analysis result may include the probability of at least one disease or condition related to the target human body.

[0084] Continuing with the previous example, for example, the "too high" in "the stenosis rate is too high" can be a fuzzy judgment, and "xx presents xx morphology" can also have a certain morphological approximation range, and illustratively, the large language model can output "the stenosis rate is 60%, which is relatively high, the morphology of xx tissue is approximately xx shape, and the difference from the standard xx morphology is xx%. Based on the above judgment conditions, the probability of the existence of xx risk is xx", and so on.

[0085] It is understandable that in various examples herein, the operation call interface corresponding to various image processing or analysis operations may be an interface to various operation resources with image processing capabilities, and image processing includes but is not limited to image enhancement, image noise reduction, image segmentation, centerline recognition, target recognition, measurement, calculation, analysis, etc., and may include other means, resources, programs and / or devices for processing medical images that can be understood by those skilled in the art, including various online or offline resources, open source or commercial resources, etc. In such examples, the corresponding operation call interface may be an interface to the corresponding operation resource, including various forms that can be understood by those skilled in the art, such as data interface, physical interface, etc., and the present disclosure is not limited thereto.

[0086] According to the embodiments of the present disclosure, it is possible to obtain an analysis process based on defined diagnostic needs based on the diagnostic experience and ideas from the user, with the help of the language understanding ability of the large model and in combination with various operational resources, thereby obtaining interpretable results that can gain the trust of the user.

[0087] For example, the diagnosis requirement description text may be a diagnosis requirement description text from a user. The user here may be a doctor, an expert, a professor, etc. Exemplarily, the intermediate variable may include a segmentation result, a recognition result (e.g., a recognition result that is not directly related to the disease to be recognized, but a recognition result of other related signs), etc. Exemplarily, the operation may include segmenting, enhancing, converting, detecting, recognizing, etc. the image.

[0088] According to some embodiments, the diagnostic requirement description text may come from a first user, and the analysis result may be used to present to the first user. According to some embodiments, the diagnostic requirement description text may come from a first user, and the analysis result may be used to present to a second user different from the first user. For example, the first user and the second user may be different doctors, doctors from different hospitals, professors and students or interns, and so on. Thus, a tool is provided that can effectively draw on the experience and wisdom of doctors, for example, in places where doctors' resources, knowledge, and qualifications are relatively limited, interpretable image analysis can also be achieved.

[0089] According to some embodiments, the at least one diagnostic requirement description text may come from a user in a predetermined user pool. For example, a qualified doctor resource pool may be maintained to achieve the effect of sharing a doctor knowledge base, so that even in scenarios where medical resources are limited, doctor knowledge is limited, or the current doctor wants to view or refer to the advice and diagnostic requirements of other peers, an interpretable analysis result can be obtained.

[0090] Since imaging diagnosis still requires a lot of medical cognition and reasoning, doctors in many hospitals have their own diagnostic experience and ideas when diagnosing diseases. Therefore, if the imaging diagnosis software provided allows doctors to define diagnostic needs and call corresponding functional operations based on the user's diagnostic needs, it will greatly help doctors in diagnosis.

[0091] According to one or more embodiments, the method may include obtaining voice information associated with the collected image, the voice information including the diagnostic requirement. Then, the voice information may be recognized to obtain a diagnostic requirement description text. The diagnostic requirement description text may be input into a large language model to obtain a corresponding operation and / or operation interface call instruction, and the image may be processed based on the determined operation. The method may include outputting one or more intermediate results and a final analysis result.

[0092] It is understood that throughout this article, any large language model known to those skilled in the art or appearing after the filing date of this application can be adopted, and those skilled in the art will understand its training method and interface calling mode, and will not be repeated here. In addition, it is understood that throughout this article, any reference to "large language model" can refer to the same large language model, or to different large language models, including different iterative versions of the same large language model, or multiple large language models trained independently. As a specific non-limiting example, different large language models can be used for diagnostic demand texts from different users, for example, different large language models can be selected according to region, language, user preference, network resources, resource usage of each large language model, etc. In another embodiment, the same model can be used to extract texts from different users and generate operations / operation interface call instructions. It is understood that the present disclosure is not limited to this.

[0093] For example, the pre-written big model interface can be used to input the diagnosis requirements of doctors or other users into the big model. As a specific non-limiting example, the prompt text input to the model can be in the form of "The following is the diagnosis requirement text from the doctor. Existing image processing and image analysis resource libraries include xxxx, etc. Please generate instructions for calling the corresponding processing resources in these resource libraries according to the doctor's diagnostic needs, and connect the intermediate results between these libraries for appropriate processing of the image to meet the instructions of the diagnostic requirement text", but it can be understood that the above is only an example and the present disclosure is not limited to this.

[0094] It is understood that although the various operations are depicted in the drawings as being in a particular order, this should not be understood as requiring that these operations must be performed in the particular order shown or in a sequential order, nor should it be understood as requiring that all the operations shown must be performed to obtain the desired results. For example, two steps described in order herein may be performed in reverse order, or may be performed concurrently. For another example, one or more steps in the various embodiments of the present disclosure may be omitted.

[0095] In addition, it is understood that the method for predicting or determining data involved in one or more embodiments of the present disclosure is not a method for a doctor to directly determine the diagnosis result, but involves data processing or information processing in the medical process, and the data processing result can be used for reference by the doctor, thereby assisting the doctor's medical operation. It is understood that the information processing method, data prediction method, determination method, decision-making method, etc. involved in one or more embodiments of the present disclosure are executed by a computer or a device including a computer.

[0096] It is to be understood that throughout the present disclosure, an image or an image sequence may be or may include two-dimensional image data, or may be or include three-dimensional image data. An image or an image sequence may be image data that is directly acquired and stored or otherwise sent to a terminal device for use by a user. An image or an image sequence may also be processed image data after various image processing. An image or an image sequence may undergo other analysis processes (e.g., an analysis process for whether there are pathological features or lesions) and include analysis results (e.g., circling of an area of ​​interest, segmentation results of a tissue, etc.). It is to be understood that the present disclosure is not limited thereto.

[0097] According to some embodiments, obtaining one or more medical images of the target human body may include: selecting images from a medical imaging device. The medical imaging device may include an X-ray device, a CT scanning device, an MRI scanning device, an ultrasound device, or a digital pathology slice scanning device.

[0098] According to some embodiments, the determined operation may further include an operation for generating an image analysis report, the image analysis report including, for example, image processing results, diagnostic conclusions, and recommended treatment plans. For example, an analysis report may be generated based on an analysis report requirement or an analysis report template input, predetermined, or otherwise obtained by a user. As a specific non-limiting example, the query text output to the user may include inquiries about the form, format, data volume, data range, etc. of the analysis report, and the analysis report may be output based on the user's response.

[0099] Figure 4 4 is a schematic block diagram illustrating an image analyzing apparatus 400 according to an exemplary embodiment. The image analyzing apparatus 400 may include a text obtaining unit 410, an operation determining unit 420, and an analyzing unit 430.

[0100] The text obtaining unit 410 includes a function for obtaining a diagnostic requirement description text from a user, wherein the diagnostic requirement description text is a natural language text describing diagnostic requirements related to a medical image. The operation determining unit 420 includes a function for determining at least one operation to be performed based on the diagnostic requirement description text, wherein each operation in the at least one operation corresponds to a separate operation calling interface. The analyzing unit 430 includes a function for processing the medical image based on the operation calling interface corresponding to the determined at least one operation to obtain an analysis result for the medical image.

[0101] According to some embodiments, the apparatus may further include an interface calling unit for calling an interface of a corresponding operating resource, for example, for obtaining a required medical image, processing the image, and so on.

[0102] According to some embodiments, the apparatus may further include a user interface module, which is used to receive a diagnosis requirement description text and / or a processing target, etc. input by a user in a voice form, a text form, or other forms.

[0103] It should be understood that Figure 4 The various modules of the apparatus 400 shown in FIG. 4 can be used in conjunction with the reference Figure 2 The steps in the method 200 described above correspond to each other. Therefore, the operations, features and advantages described above for the method 200 and its variants are also applicable to the device 400 and the modules included therein. For the sake of brevity, some operations, features and advantages are not repeated here.

[0104] According to an embodiment of the present disclosure, a computing device is also disclosed, including a memory, a processor, and a computer program stored on the memory, wherein the processor is configured to execute the computer program to implement the steps of the image analysis method according to the embodiment of the present disclosure and its variant examples.

[0105] According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium is also disclosed, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the image analysis method according to the embodiment of the present disclosure and its variant examples are implemented.

[0106] According to an embodiment of the present disclosure, a computer program product is also disclosed, including a computer program, wherein when the computer program is executed by a processor, the steps of the image analysis method according to the embodiment of the present disclosure and its variant examples are implemented.

[0107] Although specific functions are discussed above with reference to specific modules, it should be noted that the functions of the various modules discussed herein may be divided into multiple modules, and / or at least some functions of multiple modules may be combined into a single module. The specific module discussed herein performs an action including the specific module itself performing the action, or alternatively the specific module calls or otherwise accesses another component or module that performs the action (or performs the action in conjunction with the specific module). Therefore, the specific module that performs the action may include the specific module itself that performs the action and / or another module that the specific module calls or otherwise accesses to perform the action. For example, the various modules or units described in accordance with one or more embodiments of the present disclosure may be combined into a single module or unit in some embodiments. For another example, two or more modules or units may be described in parallel in one or more embodiments of the present disclosure, while in some other embodiments, there may be one or more inclusion relationships between these modules and units. As used herein, the phrase "entity A initiates action B" or "entity A causes action B to be performed" may refer to entity A issuing an instruction to perform action B, but entity A itself does not necessarily perform the action B. For example, the phrase “the display module causes display ..." may mean that the display module instructs a display (not shown) or other possible display device to display, while the display module itself does not need to perform the action of “displaying”.

[0108] It should also be understood that various techniques may be described herein in the general context of software hardware elements or program modules. Figure 3The various modules described can be implemented in hardware or in hardware in combination with software and / or firmware. For example, these modules can be implemented as computer program codes / instructions, which are configured to be executed in one or more processors and stored in a computer-readable storage medium. Alternatively, these modules can be implemented as hardware logic / circuits. For example, in some embodiments, one or more of the modules or units described in accordance with one or more embodiments of the present disclosure can be implemented together in a system on chip (System on Chip, SoC). SoC may include an integrated circuit chip (which includes a processor (e.g., a central processing unit (CPU), a microcontroller, a microprocessor, a digital signal processor (DSP), etc.), a memory, one or more communication interfaces, and / or one or more components in other circuits), and may optionally execute the received program code and / or include embedded firmware to perform functions.

[0109] According to one aspect of the present disclosure, a computing device is provided, which includes a memory, a processor, and a computer program stored in the memory. The processor is configured to execute the computer program to implement the steps of any method embodiment described above.

[0110] According to one aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any method embodiment described above are implemented.

[0111] According to one aspect of the present disclosure, a computer program product is provided, which includes a computer program. When the computer program is executed by a processor, the steps of any method embodiment described above are implemented.

[0112] In the following, combined Figure 5 Illustrative examples of such a computer device, non-transitory computer-readable storage medium, and computer program product are described.

[0113] Figure 5 An example configuration of a computer device 500 that can be used to implement the methods described herein is shown. For example, Figure 1 The server 120 and / or the client device 110 shown in FIG. 5 may include an architecture similar to the computer device 500. The above-mentioned image analysis device / apparatus may also be implemented in whole or at least in part by the computer device 500 or a similar device or system.

[0114] Computer device 500 can be a variety of different types of devices, such as a server of a service provider, a device associated with a client (e.g., a client device), a system on a chip, and / or any other suitable computer device or computing system. Examples of computer device 500 include, but are not limited to, a desktop computer, a server computer, a laptop or netbook computer, a mobile device (e.g., a tablet computer, a cellular or other wireless phone (e.g., a smart phone), a notepad computer, a mobile station), a wearable device (e.g., glasses, a watch), an entertainment device (e.g., an entertainment appliance, a set-top box communicatively coupled to a display device, a game console), a television or other display device, a car computer, and the like. Therefore, computer device 500 can range from a full-resource device with a large amount of memory and processor resources (e.g., a personal computer, a game console) to a low-resource device with limited memory and / or processing resources (e.g., a traditional set-top box, a handheld game console).

[0115] Computer device 500 may include at least one processor 502, memory 504, communication interface(s) 506, a display device 508, other input / output (I / O) devices 510, and one or more mass storage devices 512, all capable of communicating with one another, such as via a system bus 514 or other appropriate connections.

[0116] The processor 502 may be a single processing unit or multiple processing units, all of which may include a single or multiple computing units or multiple cores. The processor 502 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and / or any device that manipulates signals based on operating instructions. Among other capabilities, the processor 502 may be configured to obtain and execute computer-readable instructions stored in the memory 504, mass storage device 512, or other computer-readable media, such as program code of an operating system 516, program code of an application program 518, program code of other programs 520, and the like.

[0117] The memory 504 and the mass storage device 512 are examples of computer-readable storage media for storing instructions that are executed by the processor 502 to implement the various functions described above. For example, the memory 504 may generally include both volatile memory and non-volatile memory (e.g., RAM, ROM, etc.). In addition, the mass storage device 512 may generally include a hard drive, a solid-state drive, a removable medium, including external and removable drives, a memory card, a flash memory, a floppy disk, an optical disk (e.g., a CD, a DVD), a storage array, a network attached storage, a storage area network, etc. The memory 504 and the mass storage device 512 may all be collectively referred to herein as memory or computer-readable storage media, and may be a non-transitory medium capable of storing computer-readable, processor-executable program instructions as computer program code, which may be executed by the processor 502 as a specific machine configured to implement the operations and functions described in the examples herein.

[0118] A number of program modules may be stored on mass storage device 512. These programs include operating system 516, one or more application programs 518, other programs 520, and program data 522, and they may be loaded into memory 504 for execution. Examples of such applications or program modules may include, for example, computer program logic (e.g., computer program code or instructions) for implementing components / functionality including method 200 (including any suitable steps of method 200) and / or other embodiments described herein.

[0119] Although in Figure 5 504 of the computer device 500, but modules 516, 518, 520, and 522, or portions thereof, may be implemented using any form of computer-readable media accessible by the computer device 500. As used herein, "computer-readable media" includes at least two types of computer-readable media, namely, computer storage media and communication media.

[0120] Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented by any method or technology for storage of information, such as computer readable instructions, data structures, program modules or other data. Computer storage media includes but is not limited to RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD), or other optical storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other non-transmission media that can be used to store information for access by a computer device.

[0121] In contrast, communication media may embody computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism. Computer storage media as defined herein does not include communication media.

[0122] The computer device 500 may also include one or more communication interfaces 506 for exchanging data with other devices, such as through a network, direct connection, etc., as discussed above. Such communication interfaces may be one or more of the following: any type of network interface (e.g., a network interface card (NIC)), a wired or wireless (such as IEEE802.11 wireless LAN (WLAN)) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a wireless network interface. TM The communication interface 506 may facilitate communication within a variety of network and protocol types, including wired networks (e.g., LAN, cable, etc.) and wireless networks (e.g., WLAN, cellular, satellite, etc.), the Internet, etc. The communication interface 506 may also provide for communication with external storage devices (not shown) such as in a storage array, network attached storage, storage area network, etc.

[0123] In some examples, a display device 508 such as a monitor may be included for displaying information and images to the user. Other I / O devices 510 may be devices that receive various inputs from the user and provide various outputs to the user, and may include touch input devices, gesture input devices, cameras, keyboards, remote controls, mice, printers, audio input / output devices, and the like.

[0124] Although the present disclosure has been illustrated and described in detail in the drawings and the foregoing description, such illustration and description should be considered illustrative and exemplary rather than restrictive; the present disclosure is not limited to the disclosed embodiments. Variations to the disclosed embodiments will be understood and implemented by those skilled in the art in practicing the claimed subject matter by studying the drawings, the disclosure and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps that are not listed, and the word "a" or "an" does not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

Claims

1. A computer-implemented image analysis method, include: Obtaining a diagnosis requirement description text from a user, wherein the diagnosis requirement description text is a natural language text describing a diagnosis requirement related to a medical image; Determining at least one operation to be performed based on the diagnosis requirement description text, each operation in the at least one operation corresponds to a separate operation calling interface; as well as The medical image is processed based on an operation calling interface corresponding to the determined at least one operation to obtain an analysis result for the medical image.

2. The method according to claim 1, in, The at least one operation to be performed is determined by processing the diagnosis requirement description text through a large language model.

3. The method according to claim 1 or 2, further comprising obtaining a user's response to the challenge text, and wherein the calling of the predetermined operation instruction library is also based on the response.

4. The method according to claim 3, in, The inquiry text is used to inquire the user about the diagnosis requirement description text.

5. The method according to claim 3 or 4, in, The challenge text is output to the user in response to the challenge condition being met.

6. The method according to claim 5, in, The at least one operation determined includes a first operation and a second operation performed after the first operation, and the method further includes: Processing the medical image based on the first operation to obtain a first intermediate result; and In response to determining that the first intermediate result is inconsistent with the second operation, it is determined that the challenge condition is satisfied. The method of claim 6 , further comprising generating a challenge text based on the determined contradiction.

8. The method according to any one of claims 3 to 7, further comprising obtaining at least one updated operation based on the reply, and in, Obtaining an analysis result for the medical image includes processing the medical image based on the at least one updated operation.

9. The method according to any one of claims 1 to 8, in, Each operation of the at least one determined operation corresponds to an operation instruction calling interface in an operation instruction library, and wherein processing the medical image based on the operation calling interface corresponding to the at least one determined operation includes calling the operation instruction library based on the determined operation instruction calling interface.

10. An image analysis device, include: A text obtaining unit, used to obtain a diagnosis requirement description text from a user, wherein the diagnosis requirement description text is a natural language text describing the diagnosis requirement related to the medical image; An operation determination unit, configured to determine at least one operation to be performed based on the diagnosis requirement description text, wherein each operation of the at least one operation corresponds to a separate operation calling interface; as well as An analysis unit is used to process the medical image based on an operation calling interface corresponding to the determined at least one operation to obtain an analysis result for the medical image.

11. A computing device, include: a memory, a processor and a computer program stored on the memory, The processor is configured to execute the computer program to implement the steps of the method according to any one of claims 1 to 9.

12. A non-transitory computer-readable storage medium having a computer program stored thereon, in, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

13. A computer program product comprising a computer program, in, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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