Data processing method and apparatus, computing device, and storage medium

Abstract

Provided are a data processing method and apparatus, a computing device, and a storage medium. The method may comprise: acquiring image data related to a target human body area; obtaining at least one predicted value of fractional flow reserve (FFR) of the target human body area on the basis of the image data; obtaining at least one confidence coefficient corresponding to the at least one predicted value; and displaying the at least one predicted value and the corresponding at least one confidence coefficient in an associated manner.

Description

Data processing method, device, computing equipment and storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese patent application No. 2023111702083 filed on September 11, 2023, the entire contents of which are incorporated by reference into this application in their entirety. Technical Field

[0003] The present disclosure relates to the field of data processing, and in particular to a data processing method, apparatus, computing device, and storage medium. Background Art

[0004] Fractional Flow Reserve (FFR) refers to the ratio of the maximum blood flow available to a myocardial area supplied by a coronary artery in the presence of coronary artery stenosis to the theoretically maximum blood flow in the same area under normal conditions. It can reflect the health of the arteries. It is hoped that FFR predictions can be as accurate as possible based on acquired image data.

[0005] The approaches described in this section are not necessarily approaches that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any approach described in this section is prior art simply by virtue of its inclusion in this section. Similarly, unless otherwise indicated, the issues raised in this section should not be considered as having been recognized in any prior art.

[0006] Summary of the Invention

[0007] According to one aspect of the present disclosure, a data processing method is provided, comprising: acquiring image data related to a target human body region; obtaining at least one predicted value of a blood reserve fraction (FFR) for the target human body region based on the image data; obtaining at least one confidence level corresponding to the at least one predicted value; and displaying the at least one predicted value in association with the corresponding at least one confidence level.

[0008] According to another aspect of the present disclosure, a data processing device is provided, comprising: a data acquisition unit for acquiring image data related to a target human body region; a prediction unit for obtaining at least one predicted value of a blood reserve fraction (FFR) for the target human body region based on the image data; a confidence acquisition unit for obtaining at least one confidence level corresponding to the at least one predicted value; and a display unit for displaying the at least one predicted value in association with the corresponding at least one confidence level.

[0009] According to another aspect of the present disclosure, a computing device is provided, 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 data processing method according to one or more embodiments of the present disclosure.

[0010] 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 data processing method according to one or more embodiments of the present disclosure is implemented.

[0011] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the computer program implements the data processing method according to one or more embodiments of the present disclosure.

[0012] 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

[0013] 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:

[0014] FIG1 is a schematic diagram illustrating an example system in which the various methods described herein may be implemented, according to an exemplary embodiment;

[0015] FIG2 is a flowchart illustrating a data processing method according to an exemplary embodiment;

[0016] FIG3 is a schematic block diagram illustrating a data processing apparatus according to an exemplary embodiment;

[0017] FIG. 4 is a block diagram illustrating an exemplary computer device that can be used with the exemplary embodiments. DETAILED DESCRIPTION

[0018] In this disclosure, unless otherwise specified, the use of terms such as "first" and "second" to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, while in some cases, based on the context of the description, they may also refer to different instances.

[0019] The terms used in the description of the various examples described in this disclosure are for the purpose of describing specific examples only 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.

[0020] Exemplary embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0021] FIG. 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.

[0022] 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 .

[0023] 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) that is 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 a graphic code (for example, a barcode, a QR code, etc.) of the client application 112, 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.

[0024] 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 FIG1 shows that the server 120 is communicating with only one client device 110, the server 120 can provide backend services to multiple client devices simultaneously.

[0025] 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, 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 Sockets 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.

[0026] The system 100 may further include an image acquisition device 140. In some embodiments, the image acquisition device 140 shown in FIG1 may 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), and the like. For example, the image acquisition device 140 may include a digital subtraction angiography scanner, a magnetic resonance angiography scanner, a tomographic 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, an MRI scanner, a digital radiography scanner, etc. The image acquisition device 140 may be connected to a server (e.g., the server 120 in FIG. 1 or a separate server of the imaging system (not shown)) to implement image data processing, including but not limited to converting the scanned data (e.g., converting it into a medical image sequence), compressing it, performing pixel correction, and performing three-dimensional reconstruction.

[0027] The image acquisition device 140 may be connected to the client device 110 via the network 130 , for example, or directly connected to the client device in other ways to communicate with the client device.

[0028] 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. 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 a data processing method according to an embodiment of the present disclosure.

[0029] 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 other devices for data storage. It may be a conventional database, or it may include a cloud database, a distributed database, etc. For example, direct image data generated 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 from the data storage unit by the server 120 and the client device 110. In addition, the 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.

[0030] 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), view 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.

[0031] For the purposes of the embodiments of the present disclosure, in the example of FIG. 1 , the client application 112 may be an image sequence management application that can provide various functions, such as storage management, indexing, sorting, and classification of acquired image sequences. Accordingly, the server 120 may be a server used in conjunction with the image sequence management application. The server 120 may provide image sequence management services to the client application 112 running on the client device 110 based on user requests or instructions generated according to embodiments of the present disclosure. These services include managing cloud-based image sequence storage, storing and categorizing image sequences according to specified indices (including, but not limited to, sequence type, patient identifier, body part, acquisition target, acquisition phase, acquisition machine, lesion detection, severity, etc.), and retrieving and providing image sequences to the client device based on specified indices. Alternatively, the server 120 may provide or allocate such service capabilities or storage space to the client device 110, with the client application 112 running on the client device 110 providing corresponding image sequence management services based on user requests or instructions generated according to embodiments of the present disclosure. It can be understood that the above is only one example, and the present disclosure is not limited thereto.

[0032] FIG2 is a flowchart illustrating a data processing method 200 according to an exemplary embodiment. Method 200 may be executed at a client device (e.g., client device 110 shown in FIG1 ), that is, the execution entity of each step of method 200 may be client device 110 shown in FIG1 . In some embodiments, method 200 may be executed at a server (e.g., server 120 shown in FIG1 ). In some embodiments, method 200 may be executed by a combination of a client device (e.g., client device 110) and a server (e.g., server 120).

[0033] Hereinafter, each step of the method 200 is described in detail by taking the client device 110 as an example.

[0034] 2 , at step 210 , image data related to a target human body region is acquired.

[0035] At step 220 , at least one predicted value of the fractional blood reserve (FFR) for the target body region is obtained based on the image data.

[0036] At step 230 , at least one confidence level corresponding to the at least one predicted value is obtained.

[0037] At step 240 , the at least one predicted value and the corresponding at least one confidence level are displayed in association with each other.

[0038] Through the above method, in addition to predicting the FFR result, the confidence level can also be output to the user, thereby providing the user with a more favorable reference.

[0039] It should be understood that due to individual differences, a diagnosis of a user's disease or health status cannot be directly drawn based solely on the predicted values ​​obtained by method 200 without a doctor's professional analysis and confirmation. Method 200 is essentially a data processing method that processes image data and other data. The entire process does not require the participation of a doctor, and the resulting predicted values ​​can only provide a reference for doctors and medical professionals. Therefore, this method does not hinder doctors' freedom to choose treatment plans and should not be considered a disease diagnosis method.

[0040] According to some embodiments, obtaining at least one predicted value of the fractional blood reserve (FFR) for the target human body region based on the image data may include: obtaining a corresponding value space for each predicted value of the at least one predicted value.

[0041] The method of the present disclosure is described below with reference to some specific non-limiting examples.

[0042] In one example, a CT-FFR calculation model can be used to calculate coronary artery morphology and physiological parameters. However, due to the uncertainty in determining these parameters, the calculated FFR value is also uncertain. Therefore, estimating the uncertainty of CT-FFR calculation results is crucial for clinical applications.

[0043] For example, the calculation model of CT-FFR can be described by the following formula:

[0044] Y=f(X)

[0045] Where f(·) is the mathematical model for calculating CT-FFR. It can be a mechanistic mathematical model based on fluid mechanics or a data-driven mathematical model based on data. X is the input of the calculation model, including parameters such as coronary artery morphology and physiology. Its possible value space is defined as Ω. Y , Y is the output of the calculation model, including FFR value, blood flow, etc. Its possible value space is defined as Ω X .

[0046] For example, a probability density function can be used to describe the distribution of model input and output in their respective value spaces. For example, Represents parameter X i ∈X takes the value Similarly, the uncertainty of the model's output is expressed using the probability density function ρ YThe uncertainty of the output of the computational model can be estimated in a variety of ways. As an example, the Monte Carlo method can be used here.

[0047] For example, we can first select the input parameter value space Ω X In the probability density function, N is sampled S Group parameters to form the input data set Next, the mathematical model f(·) can be used to calculate each set of input parameters X (s) The corresponding model output Y (s) . You can create a model output data set Calculate uncertainty evaluation indicators such as expected value, variance, and confidence interval.

[0048] For example, the calculation formula may be as follows:

[0049] Expected Value

[0050] variance

[0051] Confidence interval

[0052] Where β is the significance level, the data set The dataset The elements are arranged in increasing order.

[0053] According to some embodiments, obtaining at least one confidence level corresponding to the at least one predicted value comprises obtaining a corresponding confidence interval for each predicted value of the at least one predicted value.

[0054] For example, one or more embodiments of the present disclosure can output the confidence interval for each predicted value to the user, thereby providing the user with more reference information. For example, the user can obtain the FFR prediction result and the corresponding confidence interval, thereby obtaining richer information in multiple dimensions. This can better assist users (e.g., doctors) in analysis and diagnosis.

[0055] According to some embodiments, method 200 may further include: obtaining one or more influencing factors of a change in one or more parameters of the image data on a first predicted value of the at least one predicted value; and displaying the one or more influencing factors in association with the one or more parameters.

[0056] Exemplarily, the one or more influencing factors are displayed in association with the one or more parameters, and it is not necessary to display the "one or more parameters" themselves, but only their identification, type, etc.

[0057] According to some embodiments, the method 200 may further include: displaying a control for adjusting at least one of the one or more parameters in association with the first predicted value and the one or more influencing factors.

[0058] Exemplarily, the method may include displaying a control for adjusting at least one of the one or more parameters in association with the first predicted value and at least one of the one or more influencing factors. For example, the parameter adjustment control may be displayed next to the first predicted value, the first predicted value may be associated with the parameter adjustment space using a line, a box, or other identifier, etc.

[0059] According to some embodiments, the one or more parameters include one or more segmented regions in the image data, and wherein the change in the one or more parameters includes an amount of change in a boundary of each of the one or more segmented regions.

[0060] For example, the method may include obtaining one or more segmented regions in the image data; and obtaining an influence factor of the boundary change amount of each segmented region in the one or more segmented regions on the first prediction value. The segmented regions may be obtained by processing the image (for example, executing a segmentation algorithm), or may be received from a user. Exemplarily, the method may include segmenting the image data to obtain one or more segmented regions therein (for example, tissues, organs, lesions, identified abnormalities, etc.). As other non-limiting embodiments, obtaining the segmented regions may be a combination of the above methods, such as providing a machine-generated segmented region to the user, and providing interactive options so that the user can adjust it, or receiving the segmented region divided by the user and smoothing it through a machine algorithm, etc., and the present disclosure is not limited to this.

[0061] According to some embodiments, method 200 may further include, after displaying the at least one predicted value and the corresponding at least one confidence level in association with each other: obtaining a boundary update for at least one segmented region among the one or more segmented regions; and obtaining at least one updated predicted value of the blood reserve fraction FFR for the target human body region based on the boundary update of the at least one segmented region.

[0062] In such an embodiment, user updates to the boundaries can be received. In particular, after knowing each segmented region and the associated influencing factors, the user can make targeted adjustments to the segmented regions. For example, the user can carefully adjust the boundaries that have a significant impact on the FFR prediction results, while allowing a certain error in boundaries that have a smaller impact on the FFR prediction results, and still obtain a desired prediction result.

[0063] According to some embodiments, method 200 may further include obtaining at least one updated confidence interval corresponding to each of the at least one updated predicted value based on the boundary update of the at least one segmented region. For example, one or more of the confidence level, confidence interval, and influencing factor may be calculated in real time or near real time, e.g., following user adjustments or other instructions, thereby displaying the impact of current parameters (e.g., segmented region boundaries) on the FFR calculation in real time or near real time, facilitating subsequent user adjustments and enabling the user to understand the extent of the error in the current FFR prediction result, thereby better assisting the user (e.g., a doctor) in diagnosis.

[0064] According to some embodiments, the one or more segmented regions include a segmented lesion region.

[0065] According to one or more embodiments of the present disclosure, the influence of the segmentation of the lesion area on the FFR calculation can be shown. For example, the errors of different lesion areas (or other tissues, abnormal boundaries, etc.) have different influences on the FFR calculation results. Therefore, showing such influencing factors to doctors or other operators is beneficial for doctors or operators to carefully adjust the errors of segmentation or boundaries with greater influence, while for segmentation or boundaries with less influence, they do not need to spend too much effort to adjust, thereby achieving more accurate FFR calculation results without wasting resources and consuming too much time.

[0066] It is understood that although the various operations are depicted in the accompanying drawings as being performed in a particular order, this should not be construed as requiring that the operations must be performed in the particular order shown or in sequential order, nor should it be construed as requiring that all illustrated operations must be performed to obtain the desired result. For example, two steps described herein in order may be performed in reverse order or concurrently. For example, step 230 may be performed before step 220 or concurrently with step 220. For another example, one or more steps in the various embodiments of the present disclosure may be omitted.

[0067] It is understood that throughout this disclosure, image data may be an image or an image sequence, may be or may include two-dimensional image data, or may be or may include three-dimensional image data. An image or an image sequence may be image data that is directly captured 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 operations. An image or an image sequence may undergo other analysis processes (for example, an analysis process for the presence or absence of pathological features or lesions) and include analysis results (for example, the circling of an area of ​​interest, the results of tissue segmentation, etc.). It is understood that this disclosure is not limited thereto.

[0068] FIG3 is a schematic block diagram illustrating a data processing device 300 according to an exemplary embodiment. The data processing device 300 may include a data acquisition unit 310, a prediction unit 320, a confidence acquisition unit 330, and a display unit 340. The data acquisition unit 310 may be configured to acquire image data related to a target human body region. The prediction unit 320 may be configured to obtain at least one predicted value of the Fractional Blood Reserve (FFR) for the target human body region based on the image data. The confidence acquisition unit 330 may be configured to obtain at least one confidence level corresponding to the at least one predicted value. The display unit 340 may be configured to display the at least one predicted value in association with the corresponding at least one confidence level.

[0069] It should be understood that the various modules of the apparatus 300 shown in FIG3 may correspond to the various steps in the method 200 described with reference to FIG2 . Thus, the operations, features, and advantages described above for the method 200 are also applicable to the apparatus 300 and the modules included therein. For the sake of brevity, certain operations, features, and advantages are not described in detail herein.

[0070] 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 data processing method according to the embodiment of the present disclosure and its variant examples.

[0071] 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 data processing method according to the embodiment of the present disclosure and its variants are implemented.

[0072] According to an embodiment of the present disclosure, a computer program product is further disclosed, including a computer program, wherein when the computer program is executed by a processor, the steps of the data processing method according to the embodiment of the present disclosure and its variants are implemented.

[0073] Although specific functions are discussed above with reference to specific modules, it should be noted that the functions of the various modules discussed herein can be divided into multiple modules, and / or at least some functions of multiple modules can be combined into a single module. The specific module discussed herein performing an action includes the specific module itself performing the action, or alternatively, the specific module calling or otherwise accessing another component or module that performs the action (or performs the action in conjunction with the specific module). Therefore, the specific module that performs an action can 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 can be combined into a single module or unit in some embodiments. For another example, in one or more embodiments of the present disclosure, two or more modules or units may be described in parallel, while in other embodiments, these modules and units may have one or more inclusion relationships. As used herein, the phrases "entity A initiates action B" or "entity A causes action B to be performed" may mean that entity A issues an instruction to perform action B, but entity A itself does not necessarily perform 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”.

[0074] It should also be understood that various technologies can be described herein in the general context of software hardware elements or program modules. The various modules described above with respect to FIG. 3 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 code / 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 according to one or more embodiments of the present disclosure can be implemented together in a system on chip (SoC). SoC can 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 can optionally execute the received program code and / or include embedded firmware to perform functions.

[0075] According to one aspect of the present disclosure, a computing device is provided, comprising 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 one of the method embodiments described above.

[0076] 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.

[0077] 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 one of the method embodiments described above are implemented.

[0078] Illustrative examples of such a computer device, non-transitory computer-readable storage medium, and computer program product are described below in conjunction with FIG. 4 .

[0079] FIG4 shows an example configuration of a computer device 400 that can be used to implement the methods described herein. For example, the server 120 and / or the client device 110 shown in FIG1 may include an architecture similar to the computer device 400. The above-mentioned data processing device / apparatus may also be implemented in whole or in part by the computer device 400 or a similar device or system.

[0080] Computer device 400 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 400 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 smartphone), 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. Thus, computer device 400 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).

[0081] Computer device 400 may include at least one processor 402, memory 404, communication interface(s) 406, a display device 408, other input / output (I / O) devices 410, and one or more mass storage devices 412, all capable of communicating with one another, such as via a system bus 414 or other appropriate connections.

[0082] The processor 402 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 402 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 operational instructions. Among other capabilities, the processor 402 may be configured to retrieve and execute computer-readable instructions stored in the memory 404, mass storage device 412, or other computer-readable media, such as program code for an operating system 416, program code for application programs 418, program code for other programs 420, and the like.

[0083] Memory 404 and mass storage device 412 are examples of computer-readable storage media for storing instructions that are executed by processor 402 to implement the various functions described above. For example, memory 404 may generally include both volatile memory and non-volatile memory (e.g., RAM, ROM, etc.). In addition, mass storage device 412 may generally include a hard drive, a solid-state drive, removable media, including external and removable drives, memory cards, flash memory, floppy disks, optical disks (e.g., CDs, DVDs), storage arrays, network attached storage, storage area networks, etc. Memory 404 and mass storage device 412 may be collectively referred to herein as memory or computer-readable storage media, and may be non-transitory media capable of storing computer-readable, processor-executable program instructions as computer program code that may be executed by processor 402 as a specific machine configured to implement the operations and functions described in the examples herein.

[0084] A number of program modules may be stored on the mass storage device 412. These programs include an operating system 416, one or more application programs 418, other programs 420, and program data 422, and they may be loaded into the memory 404 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 the method 200 (including any suitable steps of the method 200) and / or other embodiments described herein.

[0085] 4 as being stored in memory 404 of computer device 400, modules 416, 418, 420, and 422, or portions thereof, may be implemented using any form of computer-readable media accessible by computer device 400. As used herein, "computer-readable media" includes at least two types of computer-readable media, namely, computer storage media and communication media.

[0086] Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in 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 disks (DVDs), or other optical storage devices, magnetic cassettes, magnetic tape, 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.

[0087] 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.

[0088] The computer device 400 may also include one or more communication interfaces 406 for exchanging data with other devices, such as through a network, a direct connection, etc., as previously discussed. 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 an IEEE 802.11 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 TM The communication interface 406 may include a wireless network interface, a near field communication (NFC) interface, and the like. The communication interface 406 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, and the like. The communication interface 406 may also provide for communication with external storage devices (not shown) such as storage arrays, network attached storage, storage area networks, and the like.

[0089] In some examples, a display device 408 such as a monitor may be included for displaying information and images to the user. Other I / O devices 410 may be devices that receive various inputs from the user and provide various outputs to the user, and may include a touch input device, a gesture input device, a camera, a keyboard, a remote control, a mouse, a printer, an audio input / output device, and the like.

[0090] Although the present disclosure has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative and exemplary and not restrictive; the disclosure is not limited to the disclosed embodiments. Variations to the disclosed embodiments will be understood and effected 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 data processing method, comprising: Acquiring image data related to a target human body region; Obtaining at least one predicted value of the blood reserve fraction FFR for the target human body region based on the image data; Obtaining at least one confidence level corresponding to the at least one predicted value; as well as The at least one prediction value and the corresponding at least one confidence level are displayed in association with each other.

2. The method according to claim 1, wherein: Obtaining at least one predicted value of the blood reserve fraction FFR for the target human body region based on the image data includes: obtaining a corresponding value space for each predicted value of the at least one predicted value.

3. The method according to claim 1 or 2, wherein: Obtaining at least one confidence level corresponding to the at least one predicted value includes obtaining a corresponding confidence interval for each predicted value in the at least one predicted value.

4. The method according to any one of claims 1 to 3, further comprising: Obtaining one or more influence factors of a change in one or more parameters of the image data on a first predicted value among the at least one predicted value; as well as The one or more influencing factors are displayed in association with the one or more parameters.

5. The method according to claim 4, further comprising: A control for adjusting at least one of the one or more parameters is displayed in association with the first predicted value and the one or more influencing factors.

6. The method according to any one of claims 4 to 5, wherein: The one or more parameters include one or more segmented regions in the image data, and wherein a change in the one or more parameters includes an amount of change in a boundary of each of the one or more segmented regions.

7. The method according to claim 6, further comprising, after displaying the at least one predicted value and the corresponding at least one confidence level in association with each other: Obtaining a boundary update of at least one of the one or more partitioned regions; and At least one updated predicted value of the blood reserve fraction FFR for the target human body region is obtained based on the boundary update of the at least one segmented region. 8 . The method according to claim 7 , further comprising obtaining at least one updated confidence interval respectively corresponding to the at least one updated prediction value based on a boundary update of the at least one segmented region.

9. The method according to any one of claims 6 to 8, wherein: The one or more segmented regions include a segmented lesion region.

10. A data processing device, comprising: A data acquisition unit, used for acquiring image data related to a target human body region; A prediction unit, configured to obtain at least one predicted value of a blood reserve fraction FFR for the target human body region based on the image data; A confidence acquisition unit, configured to obtain at least one confidence corresponding to the at least one predicted value; as well as A display unit is used to display the at least one prediction value and the corresponding at least one confidence level in association with each other.

11. A computing device comprising: 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, wherein: 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, wherein: 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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