Setting apparatus and method for scan parameters

Abstract

The embodiment of the present application provides a scanning parameter setting device and method. The scanning parameter setting method comprises the following steps: obtaining an RGB image and a depth image of a scanning object from a three-dimensional camera; calculating a shape parameter of the scanning object according to the RGB image and the depth image of the scanning object; and setting a scanning parameter for scanning the scanning object according to the shape parameter of the scanning object. Thus, the scanning parameter for scanning the scanning object can be automatically set, which helps to improve the efficiency and standardization of the scanning process. Moreover, appropriate scanning parameter setting can also avoid re-scanning and low-quality scout image caused by manual errors.

Description

Technical Field

[0001] This application relates to the field of medical device technology, and in particular to a device and method for setting scanning parameters for a medical imaging system. Background Technology

[0002] For certain CT (Computed Tomography) scanning scenarios, such as contrast scans and large-size patient scans, the scan quality or image quality largely depends on the patient size and its associated scan parameter settings.

[0003] Generally, operators need to manually input the patient's height, weight, and other information before scanning, and manually set the appropriate scanning parameters accordingly.

[0004] It should be noted that the above introduction to the technical background is only for the purpose of providing a clear and complete explanation of the technical solution of this application and facilitating the understanding of those skilled in the art. Summary of the Invention

[0005] However, the inventors discovered that information such as a patient's height and weight generally comes from the patient's verbal description. In some cases, patients do not have an accurate understanding of their own height and weight. For example, special groups such as children may not know their own height and weight. On the other hand, in some situations, it is inconvenient for patients to verbally describe their height and weight. For example, in cases of serious injury, patients may have lost consciousness and be unable to speak. Furthermore, even if patients can accurately describe their height and weight, in some cases, the operator's lack of experience may lead to incorrect setting of the corresponding scanning parameters.

[0006] To address at least one of the aforementioned technical problems, embodiments of this application provide an apparatus and method for setting scanning parameters for a medical imaging system. It is anticipated that this will enable the automatic setting of scanning parameters related to patient size, thereby improving the efficiency and standardization of the scanning process.

[0007] According to one aspect of the embodiments of this application, a scanning parameter setting device is provided, the device comprising:

[0008] The acquisition unit obtains RGB and depth images of the scanned object from a 3D camera;

[0009] The first calculation unit calculates the shape parameters of the scanned object based on the RGB image and depth image of the scanned object;

[0010] The setting unit sets the scanning parameters for scanning the object based on the shape parameters of the object being scanned.

[0011] In some embodiments, the first computing unit includes:

[0012] The segmentation unit uses a deep learning neural network to segment the RGB image to obtain the two-dimensional contour information of the scanned object;

[0013] A mapping unit maps the two-dimensional contour information onto the depth image to obtain the upper surface information of the scanned object;

[0014] The second calculation unit calculates the shape parameters of the object being scanned based on the information of the upper surface of the object.

[0015] In some embodiments, the first computing unit further includes:

[0016] An estimation unit estimates information about the lower surface of the object being scanned based on information about the support supporting the object.

[0017] The second calculation unit calculates the shape parameters of the scanned object based on the upper and lower surface information of the scanned object.

[0018] In some embodiments, the information of the support includes at least one of the support's shape, size, and set height.

[0019] In some embodiments, the apparatus further includes:

[0020] A preprocessing unit performs preprocessing on the RBG image, the preprocessing including at least one of the following: denoising; normalization; cropping; and scaling.

[0021] The first calculation unit calculates the shape parameters of the scanned object based on the preprocessed RBG image and depth image of the scanned object.

[0022] In some embodiments, the shape parameters of the scanned object include at least one of the following:

[0023] The length of the object being scanned;

[0024] The thickness of the object being scanned;

[0025] Width of the object being scanned;

[0026] The volume of the object being scanned; and

[0027] The weight of the person being scanned.

[0028] In some embodiments, the scanning parameters include at least one of the following:

[0029] Body Mass Index (BMI);

[0030] The dosage and flow rate of the contrast agent;

[0031] Scan voltage and current;

[0032] Indicator of whether the scan is outside the field of view;

[0033] Scope of medication use;

[0034] Height of the support;

[0035] Instructions regarding whether it pertains to a special population.

[0036] In some embodiments, the scanning object is the entirety of the scanning object or a part of the scanning object.

[0037] According to another aspect of the embodiments of this application, a method for setting scanning parameters is provided, characterized in that the method includes:

[0038] Obtain RGB and depth images of the scanned object from a 3D camera;

[0039] Calculate the shape parameters of the scanned object based on its RGB and depth images;

[0040] The scanning parameters for scanning the object are set according to the shape parameters of the object being scanned.

[0041] According to another aspect of the embodiments of this application, an electronic device is provided, including a memory and a processor, the memory storing a computer program, and the processor being configured to execute the computer program to implement the scanning parameter setting method as described above.

[0042] One of the beneficial effects of the embodiments of this application is that, according to the embodiments of this application, scanning parameters for scanning the object can be automatically set, which helps to improve the efficiency and standardization of the scanning process. Furthermore, appropriate scanning parameter settings can also avoid rescanning and poor quality scout images due to manual errors.

[0043] Referring to the following description and accompanying drawings, specific implementation methods of the embodiments of this application are disclosed in detail, indicating how the principles of the embodiments of this application can be adopted. It should be understood that the implementation methods of this application are not limited in scope. Within the spirit and scope of the appended claims, the implementation methods of this application include many changes, modifications, and equivalents. Attached Figure Description

[0044] The accompanying drawings, which form part of the specification, are used to provide a further understanding of the embodiments of this application and illustrate the implementation methods of this application, together with the textual description, to explain the principles of this application. Obviously, the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other implementation methods based on these drawings without creative effort. In the drawings:

[0045] Figure 1 This is a schematic diagram of a CT imaging device according to an embodiment of this application;

[0046] Figure 2 This is a schematic diagram of a CT imaging system according to an embodiment of this application;

[0047] Figure 3 This is a schematic diagram of a method for setting scanning parameters according to an embodiment of this application;

[0048] Figure 4 This is a schematic diagram illustrating an example of calculating the shape parameters of a scanned object;

[0049] Figure 5 This is a schematic diagram of an example of an RGB image of the object being scanned;

[0050] Figure 6 This is a schematic diagram illustrating an example of scanning information on the upper surface of an object;

[0051] Figure 7 This is a schematic diagram illustrating another example of calculating the shape parameters of the scanned object;

[0052] Figure 8 This is a schematic diagram of a scanning parameter setting device according to an embodiment of this application;

[0053] Figure 9 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0054] Referring to the accompanying drawings, the foregoing and other features of the embodiments of this application will become apparent from the following description. Specific embodiments of this application are specifically disclosed in the description and drawings, illustrating partial implementations in which the principles of the embodiments of this application can be adopted. It should be understood that this application is not limited to the described embodiments; rather, the embodiments of this application include all modifications, variations, and equivalents falling within the scope of the appended claims.

[0055] In the embodiments of this application, the terms "first," "second," etc., are used to distinguish different elements by name, but do not indicate the spatial arrangement or chronological order of these elements, and these elements should not be limited by these terms. The term "and / or" includes any one or more of the terms listed in association and all combinations thereof. The terms "comprising," "including," "having," etc., refer to the presence of the stated features, elements, components, or assemblies, but do not exclude the presence or addition of one or more other features, elements, components, or assemblies.

[0056] In the embodiments of this application, the singular forms "a," "the," etc., including the plural forms, should be broadly understood as "a kind" or "a class" rather than limited to the meaning of "an." Furthermore, the term "the" should be understood to include both the singular and plural forms, unless the context explicitly indicates otherwise. Additionally, the term "according to" should be understood as "at least partially based on…," and the term "based on" should be understood as "at least partially based on…," unless the context explicitly indicates otherwise.

[0057] Features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, combined with features in other embodiments, or substituted for features in other embodiments. The term "comprising / including" as used herein means the presence of a feature, integral, step, or component, but does not exclude the presence or addition of one or more other features, integrals, steps, or components.

[0058] The devices described herein for acquiring medical imaging data are applicable to a variety of medical imaging modalities, including but not limited to computed tomography (CT) devices, magnetic resonance imaging (MRI) devices, C-arm imaging devices, positron emission tomography (PET) devices, single photon emission computed tomography (SPECT) devices, or any other suitable medical imaging devices.

[0059] The system for acquiring medical imaging data may include the aforementioned medical imaging equipment, a separate computer device connected to the medical imaging equipment, or a computer device connected to an internet cloud, which is connected via the internet to the medical imaging equipment or a storage device for storing medical images. The imaging method may be implemented independently or in combination by the aforementioned medical imaging equipment, the computer device connected to the medical imaging equipment, and the computer device connected to the internet cloud.

[0060] Exemplary examples are described below with reference to X-ray computed tomography (CT) equipment. Those skilled in the art will understand that embodiments of this application can also be applied to other medical imaging equipment.

[0061] Figure 1 This is a schematic diagram of a CT imaging device according to an embodiment of this application, illustrating the CT imaging device 100. For example... Figure 1 As shown, the CT imaging device 100 includes a scanning gantry 101 and a patient table 102; the scanning gantry 101 has an X-ray source 103 that projects an X-ray beam toward a detector assembly or collimator 104 on the opposite side of the scanning gantry 101. The subject 105 can lie flat on the patient table 102 and move into the scanning gantry opening 106 as the patient table 102 moves; medical image data of the subject 105 can be obtained by scanning with the X-ray source 103.

[0062] Figure 2 This is a schematic diagram of a CT imaging system according to an embodiment of this application, illustrating a block diagram of the CT imaging system 200. Figure 2 As shown, the detector assembly 104 includes multiple detector units 104a and a data acquisition system (DAS) 104b. The multiple detector units 104a sense projected X-rays passing through the object being detected 105.

[0063] The DAS 104b converts the collected information into projection data for subsequent processing based on the sensing of the detector unit 104a. During the scan that acquires the X-ray projection data, the scanning gantry 101 and the components mounted thereon rotate around the rotation center 101c.

[0064] The rotation of the scanning gantry 101 and the operation of the X-ray source 103 are controlled by the control mechanism 203 of the CT imaging system 200. The control mechanism 203 includes an X-ray controller 203a that provides power and timing signals to the X-ray source 103, and a scanning gantry motor controller 203b that controls the rotational speed and position of the scanning gantry 101. The image reconstruction unit 204 receives projection data from the DAS 104b and performs image reconstruction. The reconstructed image is transmitted as input to the computer 205, which stores the image in a mass storage device 206.

[0065] Computer 205 also receives commands and scanning parameters from the operator via console 207. Console 207 has some form of operator interface, such as a keyboard, mouse, voice-activated controller, or any other suitable input device. An associated display 208 allows the operator to view reconstructed images and other data from computer 205. Commands and parameters provided by the operator are used by computer 205 to provide control signals and information to DAS 104b, X-ray controller 203a, and scanning gantry motor controller 203b. Additionally, computer 205 operates patient table motor controller 209, controlling patient table 102 to position the subject 105 and scanning gantry 101. Specifically, patient table 102 moves the subject 105, wholly or partially, through... Figure 1 The scanning rack opening is 106.

[0066] The above illustrations depict devices and systems for acquiring medical imaging data (or medical images or medical image data) according to embodiments of this application, but this application is not limited thereto. Medical imaging devices may be CT devices, MRI devices, PET devices, SPECT devices, or any other suitable imaging devices. Storage devices may be located within the medical imaging device, on a server outside the medical imaging device, in a separate medical image storage system (such as a PACS, Picture Archiving and Communication System), and / or in a remote cloud storage system.

[0067] Furthermore, medical imaging workstations can be located locally on the medical imaging equipment, meaning they are situated close to the equipment, and both can be located in the same scanning room, radiology department, or within the same hospital. Meanwhile, the medical image cloud platform analysis system can be located away from the medical imaging equipment, for example, in the cloud where it communicates with the equipment.

[0068] As an example, after a medical institution completes an imaging scan using medical imaging equipment, the scanned data is stored in a storage device. A medical imaging workstation can directly read the scanned data and perform image processing through its processor. As another example, a medical image cloud platform analysis system can remotely access medical images stored in the storage device to provide "Software as a Service" (SaaS). SaaS can exist between hospitals, between hospitals and imaging centers, or between hospitals and third-party online medical service providers.

[0069] The above illustrations illustrate medical image scanning. The following detailed descriptions of the embodiments of this application are provided in conjunction with the accompanying drawings.

[0070] First aspect of the embodiments

[0071] This application provides a method for setting scanning parameters. Figure 3 This is a schematic diagram illustrating the method for setting scanning parameters according to an embodiment of this application, as shown below. Figure 3 As shown, the method includes:

[0072] 301. Obtain the RGB image and depth image of the scanned object from the 3D camera;

[0073] 302. Calculate the shape parameters of the scanned object based on the RGB image and depth image of the scanned object;

[0074] 303, Set the scanning parameters for scanning the object according to the shape parameters of the object being scanned.

[0075] It is worth noting that the above appendix Figure 3 The embodiments of this application have only been illustrated schematically, and the application is not limited thereto. For example, the execution order between various operations can be appropriately adjusted, and other operations can be added or some operations can be removed. Those skilled in the art can make appropriate modifications based on the above description, and are not limited to the above-described embodiments. Figure 3 The records.

[0076] In 301, the 3D camera can be positioned at any location that allows it to acquire RGB and depth images of the object being scanned, for example... Figure 1 The object can be viewed from the ceiling or other suitable location in the scene shown. Using this 3D camera, RGB and depth images of the object can be obtained. The RGB image is a two-dimensional image, where each pixel is represented by red, green, and blue color components. The depth image is a three-dimensional image, where each pixel represents the actual distance between the 3D camera and the object. Depending on the depth camera, the meaning of each pixel in the depth image may vary slightly; for example, each pixel may also represent the vertical distance from the object to the camera plane. For details, please refer to relevant technologies; this is omitted here. Typically, the RGB and depth images are registered, thus there is a correspondence between their pixels. For definitions of RGB and depth images, please refer to relevant technologies; this is omitted here.

[0077] In 302 and 303, the shape parameters of the scanned object are calculated based on the RGB and depth images of the object, instead of relying on the patient's verbal description. The scanning parameters for scanning the object are automatically set based on the automatically obtained shape parameters, instead of manually setting the scanning parameters. This helps to improve the efficiency and standardization of the scanning process and avoids rescanning and poor quality of scout images due to manual errors.

[0078] In some embodiments, operation 302 can be performed via Figure 4 The method shown is used to achieve this, such as Figure 4 As shown, the method includes:

[0079] 401. The RGB image is segmented using a deep learning neural network to obtain the two-dimensional contour information of the scanned object;

[0080] 403, Map the two-dimensional contour information onto the depth image to obtain the upper surface information of the scanned object;

[0081] 405. Calculate the shape parameters of the scanned object based on the upper surface information of the scanned object.

[0082] In section 401, there are no restrictions on the type of deep learning neural network or the segmentation method. For example, Mask-R-CNN (Mask-Region-Convolutional Neural Networks) can be used to segment RGB images to obtain the two-dimensional contour information of the scanned object.

[0083] Figure 5 This is an example illustration of an RGB image of a scanned object (human body) obtained through a 3D camera, such as... Figure 5 As shown, by employing a deep learning neural network, including but not limited to Mask-R-CNN, to segment the RGB image, the two-dimensional contour information P of the scanned object (human body) can be obtained.

[0084] In section 403, since there is a correspondence between the pixels of the RGB image and the depth image, the upper surface information of the scanned object can be obtained by mapping the two-dimensional contour information of the scanned object from the RGB image onto the depth image. This upper surface information includes the three-dimensional coordinates of each point on the upper surface of the scanned object.

[0085] Figure 6 This is a schematic diagram illustrating an example of obtaining the upper surface information of a scanned object by mapping the two-dimensional contour information obtained from the 401 image onto a depth image, as shown below. Figure 6 As shown, the upper surface information S1 is three-dimensional, containing the three-dimensional coordinates of each point on the upper surface of the human body (scanning object).

[0086] In step 405, the shape parameters of the scanned object are obtained through calculation based on the information of its upper surface. This application does not limit the specific calculation method; different calculation methods can be used depending on the required shape parameters.

[0087] In this embodiment of the application, the scanning object is not limited to the entire human body, but can also be a part of the human body, such as a certain part of the human body. Furthermore, in this embodiment of the application, the scanning object is not limited to the human body, but can also be an animal body or other organism, or a part of an animal body or other organism, etc.

[0088] Depending on the object being scanned, its shape parameters will also differ. Taking the human body as an example, in some embodiments, the shape parameters of the object being scanned include at least one of the following:

[0089] The length of the object being scanned;

[0090] The thickness of the object being scanned;

[0091] Width of the object being scanned;

[0092] The volume of the object being scanned; and

[0093] The weight of the person being scanned.

[0094] The length of the object being scanned indicates the height of the human body. The length, thickness, and width of the object being scanned can be used to calculate the volume or weight of the human body.

[0095] The above-mentioned shape parameters of the scanned object are just examples. Depending on the type of the scanned object or the needs of the scan, other shape parameters can also be calculated based on the upper surface information of the scanned object obtained by 403. This is omitted here.

[0096] In some embodiments, operation 302 can be performed via Figure 7 The method shown is used to achieve this, such as Figure 7 As shown, the method includes:

[0097] 701. The RGB image is segmented using a deep learning neural network to obtain the two-dimensional contour information of the scanned object;

[0098] 703, The two-dimensional contour information is mapped onto the depth image to obtain the upper surface information of the scanned object;

[0099] 705. Estimate the lower surface information of the object being scanned based on the information of the support supporting the object being scanned;

[0100] 707. Calculate the shape parameters of the scanned object based on the upper surface information and lower surface information of the scanned object.

[0101] The processing of 701 and 703 mentioned above and Figure 4 The handling of 401 and 403 is the same, so the explanation is omitted here.

[0102] In 705, the information of the lower surface of the object being scanned can be estimated based on the information of the support supporting the object.

[0103] In the above embodiments, such as Figure 1 and Figure 5 As shown, during a CT scan, the subject typically lies flat on a patient table; therefore, the lower surface information of the subject cannot be obtained from RGB images and / or depth images collected from a 3D camera. In this embodiment, information about the support structure supporting the subject is used as prior information to estimate the lower surface information of the subject, providing further reference for calculating the shape parameters of the subject.

[0104] In the above embodiments, the support can be, for example, a patient table, or other support. The information of the support includes the shape, size, and height of the support, but this application is not limited to this. The information of the support may also include the length, width, and depth of the recess.

[0105] In 707, the shape parameters of the scanned object are calculated by referring to the information of the upper and lower surfaces of the scanned object, making the shape parameters more accurate.

[0106] In step 303, after obtaining the shape parameters of the object to be scanned through step 302, such as the length, thickness, width, volume, weight, etc. of the object, the scanning parameters for scanning the object can be set according to these shape parameters.

[0107] In some embodiments, the scan parameters include at least one of the following:

[0108] Body Mass Index (BMI);

[0109] The dosage and flow rate of the contrast agent;

[0110] Scan voltage and current;

[0111] Indicator of whether the scan is outside the field of view;

[0112] Scope of medication use;

[0113] Height of the support;

[0114] Instructions regarding whether it pertains to a special population.

[0115] Among these, body mass index, indication of whether the scan is outside the field of view, height of the support, and indication of whether it is a special population (e.g., children) can be directly converted from the aforementioned body shape parameters. The contrast agent dosage and flow rate, scanning voltage and current, and drug application range can be obtained by pre-setting different scanning parameter values ​​corresponding to different body shape parameter ranges, and then comparing one or more body shape parameters with that range.

[0116] For example, different weight ranges are preset, and different weight ranges correspond to different contrast agent doses and flow rates. After the weight of the scanned object is obtained based on the body shape parameters, the weight is compared with different weight ranges, and the contrast agent dose and flow rate corresponding to the weight range to which the weight falls are used as the contrast agent dose and flow rate for the scanned object.

[0117] The scanning parameters above are just examples. Depending on the scanning needs, other clinically required scanning parameters can be set based on the obtained information about the upper surface of the object being scanned, or both the upper and lower surface information.

[0118] In this embodiment of the application, in order to make the two-dimensional contour information of the scanned object more accurate, the RGB image obtained by the three-dimensional camera can also be preprocessed. In step 302, the shape parameters of the scanned object are calculated based on the preprocessed RGB image and depth image of the scanned object.

[0119] In the above embodiments, the preprocessing method is not limited. The preprocessing can be one or more of the following: noise reduction processing, normalization processing, cropping processing, and scaling processing. For the specific implementation of noise reduction processing, normalization processing, cropping processing, and scaling processing, please refer to the relevant technology. This application does not impose any restrictions.

[0120] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0121] According to embodiments of this application, scanning parameters for scanning the object can be automatically set, which helps improve the efficiency and standardization of the scanning process. Furthermore, appropriate scanning parameter settings can also avoid rescanning due to manual errors and poor scout image quality.

[0122] Second aspect of the embodiments

[0123] This application provides a device for setting scanning parameters, and the same content as the first aspect of the embodiment will not be repeated.

[0124] Figure 8 This is a schematic diagram of a scanning parameter setting device according to an embodiment of this application. Figure 8 As shown, the scanning parameter setting device 800 includes:

[0125] The acquisition unit 801 acquires RGB and depth images of the scanned object from a 3D camera.

[0126] The first calculation unit 802 calculates the shape parameters of the scanned object based on the RGB image and depth image of the scanned object; and

[0127] The setting unit 803 sets the scanning parameters for scanning the object based on the shape parameters of the object being scanned.

[0128] In some embodiments, such as Figure 8 As shown, the first computing unit 802 includes:

[0129] The segmentation unit 8021 uses a deep learning neural network to segment the RGB image to obtain the two-dimensional contour information of the scanned object;

[0130] Mapping unit 8022 maps the two-dimensional contour information onto the depth image to obtain the upper surface information of the scanned object; and

[0131] The second calculation unit 8023 calculates the shape parameters of the scanned object based on the upper surface information of the scanned object.

[0132] In some embodiments, such as Figure 8 As shown, the first computing unit 802 further includes:

[0133] The estimation unit 8024 estimates the lower surface information of the object being scanned based on information about the support supporting the object being scanned.

[0134] In the above embodiment, the second calculation unit 8023 calculates the shape parameters of the scanned object based on the upper surface information and lower surface information of the scanned object.

[0135] In some embodiments, the information of the support includes at least one of the support's shape, size, and set height.

[0136] In some embodiments, such as Figure 8 As shown, the device 800 also includes:

[0137] The preprocessing unit 804 preprocesses the RBG image, the preprocessing including at least one of the following: denoising; normalization; cropping; and scaling.

[0138] In the above embodiment, the first calculation unit 802 calculates the shape parameters of the scanned object based on the preprocessed RBG image and depth image of the scanned object.

[0139] In some embodiments, the shape parameters of the scanned object include at least one of the following:

[0140] The length of the object being scanned;

[0141] The thickness of the object being scanned;

[0142] Width of the object being scanned;

[0143] The volume of the object being scanned; and

[0144] The weight of the person being scanned.

[0145] In some embodiments, the scanning parameters include at least one of the following:

[0146] Body Mass Index (BMI);

[0147] The dosage and flow rate of the contrast agent;

[0148] Scan voltage and current;

[0149] Indicator of whether the scan is outside the field of view;

[0150] Scope of medication use;

[0151] Height of the support;

[0152] Instructions regarding whether it pertains to a special population.

[0153] In some embodiments, the scanning object is the entirety of the scanning object or a part of the scanning object.

[0154] For the sake of simplicity, Figure 8 The diagram only exemplifies the connection relationships or signal flow between various components or modules; however, those skilled in the art should understand that various related technologies, such as bus connections, can be employed. The aforementioned components or modules can be implemented using hardware facilities such as processors and memory; this application does not limit the scope of the embodiments.

[0155] The above embodiments are merely illustrative examples of embodiments of this application, but this application is not limited thereto, and appropriate modifications can be made based on the above embodiments. For example, the above embodiments can be used alone, or one or more of the above embodiments can be combined.

[0156] According to embodiments of this application, scanning parameters for scanning the object can be automatically set, which helps improve the efficiency and standardization of the scanning process. Furthermore, appropriate scanning parameter settings can also avoid rescanning due to manual errors and poor scout image quality.

[0157] Third aspect of the embodiments

[0158] This application provides an electronic device including a scanning parameter setting device 800 as described in the second aspect of the embodiment, the contents of which are incorporated herein by reference. This electronic device may be, for example, a computer, server, workstation, laptop computer, smartphone, etc.; however, this application is not limited thereto.

[0159] Figure 9 This is a schematic diagram of an electronic device according to an embodiment of this application. For example... Figure 9 As shown, the electronic device 900 may include: one or more processors (e.g., a central processing unit, CPU) 910 and one or more memories 920; the memories 920 are coupled to the processors 910. The memories 920 may store various types of data; in addition, they may store a program 921 for information processing, and execute the program 921 under the control of the processors 910.

[0160] In some embodiments, the functionality of the scan parameter setting device 800 is integrated into the processor 910. The processor 910 is configured to implement the scan parameter setting method as described in the first aspect embodiment.

[0161] In some embodiments, the scanning parameter setting device 800 is configured separately from the processor 910. For example, the scanning parameter setting device 800 can be configured as a chip connected to the processor 910, and the function of the scanning parameter setting device 800 can be implemented through the control of the processor 910.

[0162] For example, the processor 910 is configured to perform the following control: acquire RGB and depth images of the object to be scanned from a 3D camera; calculate the shape parameters of the object to be scanned based on the RGB and depth images of the object; and set scanning parameters for scanning the object based on the shape parameters of the object.

[0163] In addition, such as Figure 9 As shown, the electronic device 900 may also include: an input / output (I / O) device 930 and a display 940, etc.; the functions of the above components are similar to those in the prior art, and will not be described in detail here. It is worth noting that the electronic device 900 is not necessarily required to include... Figure 9 All components shown; in addition, the electronic device 900 may also include Figure 9For components not shown, please refer to relevant technologies.

[0164] This application also provides a computer-readable program, wherein when the program is executed in an electronic device, the program causes the computer in the electronic device to perform the scanning parameter setting method as described in the first aspect embodiment.

[0165] This application also provides a storage medium storing a computer-readable program, wherein the computer-readable program causes a computer in an electronic device to perform the method for setting scanning parameters as described in the first aspect embodiment.

[0166] The apparatus and methods described above in this application can be implemented in hardware or in combination with software. This application relates to a computer-readable program that, when executed by a logic component, enables the logic component to implement the apparatus or components described above, or to implement the various methods or steps described above. This application also relates to storage media for storing the above programs, such as hard disks, magnetic disks, optical disks, DVDs, flash memory, etc.

[0167] The methods / apparatus described in conjunction with the embodiments of this application can be directly embodied in hardware, software modules executed by a processor, or a combination of both. For example, one or more and / or combinations of one or more functional block diagrams shown in the figures can correspond to various software modules in a computer program flow, or to various hardware modules. These software modules can correspond to the various steps shown in the figures, respectively. These hardware modules can be implemented, for example, using a field-programmable gate array (FPGA) to embed these software modules.

[0168] The software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. A storage medium can be coupled to the processor, enabling the processor to read information from and write information to the storage medium; or the storage medium can be an integral part of the processor. The processor and storage medium can reside in an ASIC. The software module can be stored in the memory of a mobile terminal or in a memory card that can be inserted into the mobile terminal. For example, if the device (such as a mobile terminal) uses a high-capacity MEGA-SIM card or a high-capacity flash memory device, the software module can be stored in the MEGA-SIM card or the high-capacity flash memory device.

[0169] One or more and / or one or more combinations of functional blocks described in the accompanying drawings can be implemented as a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, or any suitable combination thereof for performing the functions described herein. One or more and / or one or more combinations of functional blocks described in the accompanying drawings can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in communication with a DSP, or any other such configuration.

[0170] The present application has been described above with reference to specific embodiments. However, those skilled in the art should understand that these descriptions are exemplary and not intended to limit the scope of protection of the present application. Those skilled in the art can make various modifications and variations to the present application based on the principles thereof, and these modifications and variations are also within the scope of the present application.

[0171] Regarding the implementation methods including the above embodiments, the following notes are also disclosed:

[0172] 1. A method for setting scanning parameters, wherein the method includes:

[0173] Obtain RGB and depth images of the scanned object from a 3D camera;

[0174] Calculate the shape parameters of the scanned object based on its RGB and depth images;

[0175] The scanning parameters for scanning the object are set according to the shape parameters of the object being scanned.

[0176] 2. The method according to Appendix 1, wherein calculating the shape parameters of the scanned object based on the RGB image and depth image of the scanned object includes:

[0177] The RGB image is segmented using a deep learning neural network to obtain the two-dimensional contour information of the scanned object;

[0178] The two-dimensional contour information is mapped onto the depth image to obtain the upper surface information of the scanned object;

[0179] The shape parameters of the object being scanned are calculated based on the information of the upper surface of the object being scanned.

[0180] 3. The method according to Appendix 2, wherein calculating the shape parameters of the scanned object based on the RGB image and depth image of the scanned object further includes:

[0181] Estimate the lower surface information of the object being scanned based on the information of the support supporting the object being scanned;

[0182] The shape parameters of the object being scanned are calculated based on the information of its upper and lower surfaces.

[0183] 4. The method according to Appendix 3, wherein the information of the support includes at least one of the shape, size, and installation height of the support.

[0184] 5. The method according to Appendix 1, wherein the method further comprises:

[0185] The RBG image is preprocessed, and the preprocessing includes at least one of the following: noise reduction; normalization; cropping; and scaling.

[0186] The shape parameters of the scanned object are calculated based on the preprocessed RGB image and depth image of the scanned object.

[0187] 6. The method according to Appendix 1, wherein the shape parameters of the scanned object include at least one of the following:

[0188] The length of the object being scanned;

[0189] The thickness of the object being scanned;

[0190] Width of the object being scanned;

[0191] The volume of the object being scanned; and

[0192] The weight of the person being scanned.

[0193] 7. The method according to Appendix 1, wherein the scanning parameters include at least one of the following:

[0194] Body Mass Index (BMI);

[0195] The dosage and flow rate of the contrast agent;

[0196] Scan voltage and current;

[0197] Indicator of whether the scan is outside the field of view;

[0198] Scope of medication use;

[0199] Height of the support;

[0200] Instructions regarding whether it pertains to a special population.

[0201] 8. The method according to any one of Appendix 1 to 7, wherein the scanning object is the whole of the scanning object or a part of the scanning object.

[0202] 9. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor being configured to execute the computer program to implement a method for setting blood scan parameters as described in any one of Appendices 1 to 8.

[0203] 10. A storage medium storing a computer-readable program, wherein the computer-readable program causes a computer in an electronic device to perform a method for setting scan parameters as described in any one of Appendices 1 to 8.

Claims

1. A device for setting scanning parameters, characterized in that, The device includes: The acquisition unit obtains RGB and depth images of the scanned object from a 3D camera; The first calculation unit calculates the shape parameters of the scanned object based on the RGB image and depth image of the scanned object; The setting unit sets the scanning parameters for scanning the object based on the shape parameters of the object being scanned; The first computing unit includes: The segmentation unit uses a deep learning neural network to segment the RGB image to obtain the two-dimensional contour information of the scanned object; A mapping unit maps the two-dimensional contour information onto the depth image to obtain the upper surface information of the scanned object; An estimation unit estimates information about the lower surface of the object being scanned based on information about the support supporting the object. The second calculation unit calculates the shape parameters of the scanned object based on the upper and lower surface information of the scanned object.

2. The apparatus according to claim 1, characterized in that, The information about the support includes at least one of the following: the shape, size, and height of the support.

3. The apparatus according to claim 1, characterized in that, The device further includes: A preprocessing unit performs preprocessing on the RGB image, the preprocessing including at least one of the following: noise reduction; normalization; cropping; and scaling. The first calculation unit calculates the shape parameters of the scanned object based on the preprocessed RGB image and depth image of the scanned object.

4. The apparatus according to claim 1, characterized in that, The shape parameters of the scanned object include at least one of the following: The length of the object being scanned; The thickness of the object being scanned; Width of the object being scanned; The volume of the object being scanned; and The weight of the person being scanned.

5. The apparatus according to claim 1, characterized in that, The scanning parameters include at least one of the following: Body Mass Index (BMI); The dosage and flow rate of the contrast agent; Scan voltage and current; Indicator of whether the scan is outside the field of view; Scope of medication use; Height of the support; Instructions regarding whether it pertains to a special population.

6. The apparatus according to any one of claims 1 to 5, characterized in that, The object being scanned can be the entire object or a part of the object being scanned.

7. A method for setting scanning parameters, characterized in that, The method includes: Obtain RGB and depth images of the scanned object from a 3D camera; Calculate the shape parameters of the scanned object based on its RGB and depth images; The scanning parameters for scanning the object are set according to the shape parameters of the object being scanned; The calculation of the shape parameters of the scanned object includes: The RGB image is segmented using a deep learning neural network to obtain the two-dimensional contour information of the scanned object; The two-dimensional contour information is mapped onto the depth image to obtain the upper surface information of the scanned object; Estimate the lower surface information of the scanned object based on information about the support structure supporting the scanned object; and The shape parameters of the object being scanned are calculated based on the information of its upper and lower surfaces.

8. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor being configured to execute the computer program to implement the method for setting scan parameters as described in claim 7.

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