Medical image processing method, device, computing device and storage medium

Abstract

A medical image processing method, apparatus, computing device and storage medium are provided. The method may include: obtaining location information from a user, the location information indicating a first location corresponding to a first position or region in a medical image; obtaining segmentation data of the medical image; obtaining a second location corresponding to a position or region in the medical image based on the segmentation data, the second location being associated with the first location; and displaying the second location in association with the first location.

Description

Technical Field

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

[0002] Currently, doctors often use medical images scanned by medical scanning equipment to make medical diagnoses. It is hoped to obtain a solution for processing medical images.

[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 medical image processing method is provided, comprising: obtaining location information from a user, the location information indicating a first location corresponding to a first position or region in a medical image; obtaining segmentation data of the medical image; obtaining a second location corresponding to a position or region in the medical image based on the segmentation data, the second location being associated with the first location; and displaying the second location in association with the first location.

[0005] According to another aspect of the present disclosure, a medical image processing device is provided, including: a first obtaining unit, used to obtain location information from a user, the location information indicating a first location corresponding to a first position or region in a medical image; a segmentation obtaining unit, used to obtain segmentation data of the medical image; a second obtaining unit, used to obtain a second location corresponding to a position or region in the medical image based on the segmentation data, the second location having an associated relationship with the first location; and a display unit, used to display the second location in association with the first location.

[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 a medical image processing 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, a medical image processing 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 medical image processing 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 a medical image processing method according to an exemplary embodiment;

[0013] Figure 3A-3E is a schematic diagram of a medical image according to an exemplary embodiment of the present disclosure;

[0014] Figure 4 is a schematic block diagram illustrating a medical image processing 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 a medical image processing 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 a medical image processing 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, location information is obtained from a user, where the location information indicates a first location corresponding to a first position or region in a medical image.

[0033] At step 220 , segmentation data of the medical image is obtained.

[0034] At step 230, a second location corresponding to a position or region in the medical image is obtained based on the segmented data, and the second location is associated with the first location.

[0035] At step 240, the second location is displayed in association with the first location.

[0036] Through the above method, it is possible to prompt the associated area in the medical image based on the user input, thereby facilitating the user (for example, a doctor) to analyze the medical image.

[0037] According to some embodiments, obtaining a second location corresponding to a position or region in the medical image based on the segmented data comprises obtaining the second location based on a pre-trained association.

[0038] Exemplarily, the pre-trained associations may be obtained through a large language model.

[0039] According to some embodiments, the association relationship between the second location and the first location includes that the human body region corresponding to the second location and the human body region corresponding to the first location satisfy at least one of the following relationships: belonging to the same organ or tissue, belonging to the same organ or tissue type, and belonging to the same lesion or disease.

[0040] According to some embodiments, the association relationship between the second location and the first location includes: for a medical analysis target associated with the medical image, the human body region corresponding to the second location contains signs or features that are used to supplement the signs or features existing in the human body region corresponding to the first location.

[0041] According to some embodiments, the signs or features existing in the human body region corresponding to the second location that are used to supplement the signs or features existing in the human body region corresponding to the first location include signs that the signs existing in the human body region corresponding to the second location are edge signs of the signs existing in the human body region corresponding to the first location.

[0042] According to some embodiments, the second location being associated with the first location includes: for the medical analysis target associated with the medical image and the human body region corresponding to the first location, there are one or more associated measurements to be measured in the human body region corresponding to the second location.

[0043] Exemplarily, the one or more associated measurements to be measured may include at least one of length, width, and CT value.

[0044] According to some embodiments, the method may further comprise displaying the one or more associated measures to be measured in the second location in association with the first location.

[0045] According to some embodiments, obtaining location information from a user includes obtaining a framed area from the user.

[0046] According to some embodiments, obtaining location information from a user includes obtaining a click from the user.

[0047] According to some embodiments of the present disclosure, the association between the lesion and the area related to the lesion can be learned in advance. As some specific usage scenarios, the doctor can click on certain locations on the image, such as Figure 3A Point A shown in FIG. 1 is used as the first location selected by the user. The first location may correspond to a lesion or other signs or features of interest to the user. After receiving the first location selected by the user, based on the pre-learned association, the associated areas B and C may be associated and displayed as the second location, such as Figure 3B In other examples, it may be indicated which quantities in the image should be measured.

[0048] According to one or more embodiments of the present disclosure, it is possible to provide assistance when a user (eg, a doctor) is uncertain about an image, the user makes an area selection, and a prompt area is given based on the user's selection.

[0049] Exemplarily, these associations may be learned based on the behavior of other users, or may be obtained from a knowledge base built based on other users. For example, the knowledge base built based on other users may come from the knowledge contributions of other mature professional doctors, thereby enabling experience sharing between doctors of different levels.

[0050] As another specific, non-limiting example, refer to Figure 3C The given image shows two locations E and F, which may correspond to developed lesions, tissues, signs, etc. An experienced doctor will realize, or will realize in combination with images taken from another angle, that locations E and F are related, for example, they belong to the same entity. Figure 3D As shown in the region H in FIG. 1 , the complete lesion or tissue without obstruction or taken at other angles is shown. In this case, the undeveloped portion between the regions E and F (eg, Figure 3E Region G in the image should also belong to the organ, tissue, lesion or sign that should be paid attention to at present. The doctor can annotate the image, or the doctor's behavior habits can be learned to obtain the association between these regions.

[0051] After learning such an association, according to one or more embodiments of the present disclosure, when another user (e.g., a less experienced doctor) clicks or selects location E or F, G can be determined as the current associated location through the pre-learned association, and G is displayed in association, for example, Figure 3E shown.

[0052] According to one or more embodiments of the present disclosure, relevant prompt information related to the target may be presented to the user by receiving a user selection. Exemplarily, the information related to the target may be pre-learned by a large model.

[0053] For example, for any segmented part or target in medical image segmentation, the associated information is maintained, and the association between the segmented part or target and the associated information is learned through a large model. Afterwards, when the user's selection is received and the user's selection matches the corresponding segmented part or target, the corresponding associated information is retrieved and output to the user.

[0054] As a specific non-limiting example, for each lesion region in the image segmentation, relevant association information can be established and maintained, such as length, width, edge signs, numerical values ​​of surrounding areas (e.g., CT values), etc. The model can learn the association between each region and the corresponding association information. Thus, when receiving the user's selection, the association information can be extracted and presented.

[0055] According to one or more embodiments of the present disclosure, the capabilities of large models can be utilized to assist doctors in a more targeted manner. Compared with generally outputting possible related information, after receiving the user's selection (for example, the user is interested in a certain area and is confused about the division of a certain area), the segmentation information and pre-learned associations can be used to output related information in a targeted manner, thereby helping the user to analyze medical images.

[0056] 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.

[0057] 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.

[0058] It is to be understood that throughout the present disclosure, images, image data, or image sequences may be or may include two-dimensional image data, or may be or include three-dimensional image data. Images, image data, or image sequences may be image data that is directly collected and stored or otherwise sent to a terminal device for use by a user. Images, image data, or image sequences may also be processed image data after various image processing. Images, image data, or image sequences may undergo other analysis processes (e.g., an analysis process for whether there are pathological features or lesions) and include analysis results (e.g., the encircling of an area of ​​interest, the segmentation results of tissues, etc.). It is to be understood that the present disclosure is not limited thereto.

[0059] Figure 4 4 is a schematic block diagram illustrating a medical image processing apparatus 400 according to an exemplary embodiment. The medical image processing apparatus 400 may include a first obtaining unit 410 , a segmentation obtaining unit 420 , a second obtaining unit 430 , and a display unit 440 .

[0060] The first obtaining unit 410 may be used to obtain location information from a user, the location information indicating a first location corresponding to a first position or region in a medical image. The segmentation obtaining unit 420 may be used to obtain segmentation data of the medical image. The second obtaining unit 430 may be used to obtain a second location corresponding to a position or region in the medical image based on the segmentation data, the second location being associated with the first location. The display unit 440 may be used to display the second location in association with the first location.

[0061] It should be understood that Figure 4 The various modules of the apparatus 400 shown in FIG. 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.

[0062] According to some embodiments, obtaining a second location corresponding to a position or region in the medical image based on the segmented data comprises obtaining the second location based on a pre-trained association.

[0063] According to some embodiments, the association relationship between the second location and the first location includes that the human body region corresponding to the second location and the human body region corresponding to the first location satisfy at least one of the following relationships: belonging to the same organ or tissue, belonging to the same organ or tissue type, and belonging to the same lesion or disease.

[0064] According to some embodiments, the association relationship between the second location and the first location includes: for a medical analysis target associated with the medical image, the human body region corresponding to the second location contains signs or features that are used to supplement the signs or features existing in the human body region corresponding to the first location.

[0065] According to some embodiments, the signs or features existing in the human body region corresponding to the second location that are used to supplement the signs or features existing in the human body region corresponding to the first location include signs that the signs existing in the human body region corresponding to the second location are edge signs of the signs existing in the human body region corresponding to the first location.

[0066] According to some embodiments, the second location being associated with the first location includes: for the medical analysis target associated with the medical image and the human body region corresponding to the first location, there are one or more associated measurements to be measured in the human body region corresponding to the second location.

[0067] According to some embodiments, the apparatus further comprises displaying the one or more associated cells to be measured in the second location in association with the first location.

[0068] According to some embodiments, obtaining location information from a user includes obtaining a framed area from the user.

[0069] According to some embodiments, obtaining location information from a user includes obtaining a click from the user.

[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 medical image 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 medical image processing method according to the embodiment of the present disclosure and its variant examples are implemented.

[0072] 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 medical image processing method according to the embodiment of the present disclosure and its variant examples 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 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”.

[0074] It should also be understood that various techniques may be described herein in the general context of software hardware elements or program modules. Figure 4 The 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.

[0075] 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.

[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 method embodiment described above are implemented.

[0078] 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.

[0079] 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 medical image processing device / apparatus may also be implemented in whole or at least in part by the computer device 500 or a similar device or system.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] Although in Figure 5504 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.

[0086] 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.

[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 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.

[0089] 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.

[0090] 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 medical image processing method, comprising: Obtaining location information from a user, the location information indicating a first location corresponding to a first position or region in the medical image; Obtaining segmentation data of the medical image; Obtaining a second location corresponding to a position or region in the medical image based on the segmented data, the second location being associated with the first location; and The second location is displayed in association with the first location.

2. The method according to claim 1, wherein: Obtaining a second location corresponding to a position or region in the medical image based on the segmented data includes obtaining the second location based on a pre-trained association.

3. The method according to claim 1 or 2, wherein: The association relationship between the second location and the first location includes that the human body area corresponding to the second location and the human body area corresponding to the first location satisfy at least one of the following relationships: belonging to the same organ or tissue, belonging to the same organ or tissue type, and belonging to the same lesion or disease.

4. The method according to any one of claims 1 to 3, wherein: The association relationship between the second location and the first location includes: for the medical analysis target associated with the medical image, the human body area corresponding to the second location contains signs or features that are used to supplement the signs or features existing in the human body area corresponding to the first location.

5. The method according to claim 4, wherein: The signs or features existing in the human body region corresponding to the second location that are used to supplement the signs or features existing in the human body region corresponding to the first location include signs that the signs existing in the human body region corresponding to the second location are edge signs of the signs existing in the human body region corresponding to the first location.

6. The method according to any one of claims 1 to 5, wherein: The second location is associated with the first location, including: for the medical analysis target associated with the medical image and the human body area corresponding to the first location, there are one or more associated measurements in the human body area corresponding to the second location. 7 . The method according to claim 6 , further comprising displaying the one or more associated measures to be measured in the second location in association with the first location.

8. The method according to any one of claims 1 to 7, wherein: Obtaining location information from a user includes obtaining a selected area from the user.

9. The method according to any one of claims 1 to 8, wherein: Obtaining location information from a user includes obtaining a click from the user.

10. A medical image processing device, comprising: A first obtaining unit, configured to obtain location information from a user, wherein the location information indicates a first location corresponding to a first position or region in a medical image; A segmentation obtaining unit, used for obtaining segmentation data of the medical image; A second obtaining unit, configured to obtain, based on the segmented data, a second location corresponding to a position or region in the medical image, wherein the second location is associated with the first location; and A display unit is configured to display the second location in association with the first location.

11. The device according to claim 10, wherein: Obtaining a second location corresponding to a position or region in the medical image based on the segmented data includes obtaining the second location based on a pre-trained association.

12. The device according to claim 10 or 11, wherein: The association relationship between the second location and the first location includes that the human body area corresponding to the second location and the human body area corresponding to the first location satisfy at least one of the following relationships: belonging to the same organ or tissue, belonging to the same organ or tissue type, and belonging to the same lesion or disease.

13. The device according to any one of claims 10 to 12, wherein: The association relationship between the second location and the first location includes: for the medical analysis target associated with the medical image, the human body area corresponding to the second location contains signs or features that are used to supplement the signs or features existing in the human body area corresponding to the first location.

14. The device according to claim 13, wherein: The signs or features existing in the human body region corresponding to the second location that are used to supplement the signs or features existing in the human body region corresponding to the first location include signs that the signs existing in the human body region corresponding to the second location are edge signs of the signs existing in the human body region corresponding to the first location.

15. The device according to any one of claims 10 to 14, wherein: The second location is associated with the first location, including: for the medical analysis target associated with the medical image and the human body area corresponding to the first location, there are one or more associated measurements in the human body area corresponding to the second location. 16 . The apparatus according to claim 15 , further comprising displaying the one or more associated cells to be measured in the second location in association with the first location.

17. The device according to any one of claims 10 to 16, wherein: Obtaining location information from a user includes obtaining a selected area from the user.

18. The device according to any one of claims 10 to 17, wherein: Obtaining location information from a user includes obtaining a click from the user.

19. 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.

20. 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.

21. 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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