Medical image scanning method and system

Abstract

The embodiment of the invention provides a medical image scanning method and system, and the method comprises the steps: obtaining at least one scanning image of a target object based on a first scanning sequence; based on a preset algorithm, whether the image quality of the at least one scanned image meets an image quality requirement is judged, and the image quality requirement is at least related to one or more of the following: whether artifacts exist, the severity level of the artifacts, whether a region of interest exists and whether a focus exists; determining the scanned image which does not meet the image quality requirement as an abnormal scanned image, marking the abnormal scanned image, and displaying the abnormal scanned image to a user; and obtaining a processing operation of the user on the abnormal scanning image, and printing the medical image after the processing operation.

Description

Technical Field

[0001] The present invention relates to the field of medical image processing technology, and in particular to a medical image scanning method and system. Background Art

[0002] During the scanning process of medical equipment, artifacts may be generated due to patient movement, system equipment errors, etc., resulting in missed diagnosis or misdiagnosis during the doctor's diagnosis of the scanned image. During the scanning process and when printing medical images after scanning, technicians often need to check the image quality and manually select the image printing layout to screen qualified scanned images for doctors or patients, which is inefficient. At the same time, this process is extremely dependent on the technician's personal experience and level, and everyone's evaluation standards are not unified, lacking a complete and consistent image quality control standard.

[0003] Therefore, it is necessary to provide a medical image scanning method and system to achieve automated, unified and efficient quality control and image printing of scanned images. Summary of the invention

[0004] One or more embodiments of the present specification provide a medical image scanning method, the method comprising: based on a first scanning sequence, acquiring at least one scanned image of a target object; based on a preset algorithm, determining whether the image quality of the at least one scanned image meets image quality requirements, the image quality requirements being at least related to a combination of one or more of the following: whether there are artifacts, the severity level of the artifacts, whether there are regions of interest, and whether there are lesions; determining that the scanned image that does not meet the image quality requirements is an abnormal scanned image, and marking the abnormal scanned image and displaying it to a user; acquiring the user's processing operation on the abnormal scanned image, and printing the medical image after the processing operation.

[0005] In some embodiments, judging whether the image quality of the at least one scanned image meets the image quality requirements based on a preset algorithm includes: processing the at least one scanned image based on an image quality judgment model to judge whether the image quality of the at least one scanned image meets the image quality requirements, and determining the abnormality level and abnormality cause of the at least one scanned image; the image quality judgment model is a machine learning model.

[0006] In some embodiments, the method further includes: starting automatic image swing based on the user operation of the user; the automatic image swing includes: automatically swinging the scan image of interest based on a swing mode, the scan image of interest is one or more of the scan images after deleting the abnormal scan image, and the swing mode includes at least one of swinging according to sequence, swinging according to orientation, and swinging according to organ.

[0007] In some embodiments, the method further includes: determining scan images of no interest, and displaying the scan images of no interest to the user after marking the scan images of no interest; the scan images of no interest include scan positioning images, one or more of a preset number of scan images acquired first and / or last in the at least one scan image.

[0008] In some embodiments, the identification includes: distinguishing and displaying the abnormal scan image and the scan image not of interest, and highlighting one or more of an abnormal area, an abnormal level, and an abnormal cause in the abnormal scan image.

[0009] In some embodiments, the method further includes: detecting whether a lesion exists in each of the at least one scan image; determining that the scan image in which the lesion exists and the lesion size is smaller than a preset size is a target scan image; and determining a second scan sequence based on the first scan sequence corresponding to the target scan image, so that the updated lesion size in the updated scan image corresponding to the second scan sequence is not smaller than the preset size.

[0010] One or more embodiments of the present specification provide a medical image scanning system, the system comprising: an image acquisition module, for acquiring at least one scanned image of a target object based on a first scanning sequence; a quality analysis module, for determining whether the image quality of the at least one scanned image meets image quality requirements based on a preset algorithm, the image quality requirements being at least related to a combination of one or more of the following: whether there are artifacts, the severity level of the artifacts, whether there are regions of interest, and whether there are lesions; a display module, for determining that the scanned image that does not meet the image quality requirements is an abnormal scanned image, and marking the abnormal scanned image and displaying it to a user; a printing module, for obtaining the user's processing operation on the abnormal scanned image, and printing the medical image after the processing operation.

[0011] In some embodiments, the quality analysis module is further used to: process the at least one scanned image based on an image quality judgment model, determine whether the image quality of the at least one scanned image meets the image quality requirements, and determine the abnormality level and abnormality cause of the at least one scanned image, and the image quality judgment model is a machine learning model.

[0012] In some embodiments, the printing module is further used to: start automatic image swing based on the user's operation; the automatic image swing includes: automatically swinging the scan image of interest based on a swing method, the scan image of interest is one or more of the scan images after deleting the abnormal scan image, and the swing method includes at least one of swinging according to sequence, swinging according to orientation, and swinging according to organ.

[0013] One or more embodiments of the present specification provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the medical image scanning method as described in any one of the above embodiments.

[0014] Some embodiments of the present specification include at least the following beneficial effects: (1) through the intelligent interactive interface, the time cost of technicians or doctors in reading films is reduced, and the objective factors of film selection are increased; (2) through the intelligent image quality control workflow, the image quality of the acquired scanned images can be judged, the efficiency of users in checking and controlling the image quality can be improved, and low-level technicians can be helped to improve their operation level. At the same time, the technicians can increase their new understanding of the disease (for example, the system can assist in identifying tiny lesions, that is, parts that cannot be recognized by the naked eye, to help technicians make judgments), etc.; (3) through the intelligent image quality control workflow, a complete and consistent image quality control standard can be achieved, reducing the quality control irregularities caused by uneven human judgment levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0016] Figure 1 is a schematic diagram of an application scenario of a medical image scanning system according to some embodiments of this specification;

[0017] Figure 2 is an exemplary module diagram of a medical image scanning system according to some embodiments of this specification;

[0018] Figure 3 is an exemplary flow chart of a medical image scanning method according to some embodiments of this specification;

[0019] Figure 4 is an exemplary schematic diagram of a medical image scanning method according to some embodiments of this specification;

[0020] Figure 5 is an exemplary flow chart of another medical image scanning method according to some embodiments of this specification;

[0021] Figure 6 is another exemplary schematic diagram of the medical image scanning method shown in some embodiments of the present specification. DETAILED DESCRIPTION

[0022] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0023] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0024] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0025] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0026] The medical image scanning system provided in some embodiments of this specification can be embedded in the existing system of the hospital, or it can be an independent platform for access by multiple hospitals. The medical image scanning system provided in some embodiments of this specification can be turned on or off according to the needs of users (e.g., doctors, technicians, etc.). For example, the user can choose whether to turn on the medical image scanning system through a switch. After the medical image scanning system is turned on, the image quality of the acquired scanned image can be judged, the efficiency of the user's inspection and quality control of the image quality can be improved, and low-level technicians can be helped to improve their operating level. At the same time, the technician's new understanding of the disease can be increased (e.g., the system can assist in identifying tiny lesions, that is, areas that cannot be recognized by the naked eye, to help technicians make judgments), etc.

[0027] Figure 1 Schematic diagram of application scenarios of medical image scanning systems according to some embodiments of this specification. Figure 1 As shown, the application scenario 100 of the medical image scanning system may include an imaging device 110 , a processor 120 , a network 130 , a storage device 140 , a terminal 150 and a printing device 160 .

[0028] The imaging device 110 can be used to scan the target object and generate a scanned image. The target object may include a biological object (e.g., a human body, an animal, etc.), a non-biological object (e.g., a phantom), etc. In other embodiments, the target object may also include a specific part, organ, and / or tissue of a patient. For example, the target object may include the head, chest, legs, etc., or any combination thereof, without limitation. In some embodiments, the target object may include a specific part, organ, and / or tissue of a patient and other organs and / or tissues within a certain range around the patient. The target object may also be referred to as a patient hereinafter.

[0029] In some embodiments, the imaging device 110 may include a magnetic resonance imaging (MRI) scanner, a computed tomography (CT) scanner, a positron emission tomography (PET) scanner, a single-photon emission computed tomography (SPECT) scanner, an ultrasound scanner, an ultrasound scanner, an X-ray scanner, an optical coherence tomography (OCT) scanner, an ultrasound scanner, an intravascular ultrasound (IVUS) scanner, a near infrared spectrum (NIRS) scanner, a far infrared (FIR) scanner, etc., or any combination thereof. Accordingly, the scanned image may include multiple types. For example, a magnetic resonance scan image, a CT scan image, a PET scan image, etc. In some embodiments, the processor 120 and the storage device 140 may become part of the imaging device 110.

[0030] In some embodiments, the imaging device 110 may scan the target object according to a specific scanning sequence. For example, the imaging device 110 may perform a first scan on the target object based on the first scanning sequence or the second scanning sequence determined by the processing device 120. In some embodiments, the imaging device 110 may send the scan result to the processor 120, the storage device 130 and / or the terminal 150 through the network 130 for further processing. For example, the imaging device 110 may send the actual scan result (e.g., at least one scanned image) obtained when scanning according to the first scanning sequence to the processor 120. The processor 120 may determine whether the image quality of at least one scanned image meets the image quality requirements, and identify the abnormal scanned images that do not meet the image quality requirements and display them to the user.

[0031] The processor 120 may be used to process data and / or information obtained from the imaging device 110, the storage device 140, and / or the terminal 150. For example, the processor 120 may obtain at least one scanned image from the imaging device 110, and determine whether the image quality of the at least one scanned image meets the image quality requirement. For another example, the processor 120 may identify an abnormal scanned image. For another example, the processor 120 may automatically position the scanned image of interest based on the positional position mode.

[0032] In some embodiments, the processor 120 may send the processing result to the terminal 150. For example, the processor 120 may send the marked abnormal scan image to the terminal 150 and display it on one or more display devices in the terminal 150.

[0033] In some embodiments, the processor 120 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 120 may be local or remote. In some embodiments, the processor 120 may be connected to the imaging device 110, the storage device 140, the terminal 150 and / or the printing device 160 through the network 130 or directly to access the information and / or data stored thereon. In some embodiments, the processor 120 may be integrated in the imaging device 110. In some embodiments, the processor 120 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc. or any combination thereof.

[0034] The network 130 may include any suitable network that can facilitate information and / or data exchange of the medical image scanning system. In some embodiments, one or more components of the medical image scanning system (e.g., the imaging device 110, the processor 120, the storage device 140, the terminal 150, or the printing device 160) may be connected and / or communicated with other components of the medical image scanning system via the network 130. For example, the processor 120 may obtain a scanned image from the imaging device 110 via the network 130. For another example, the processor 120 may obtain a user's processing operation from the terminal 150 via the network 130.

[0035] In some embodiments, the network 130 may be any one or more of a wired network or a wireless network. For example, the network 130 may include a cable network, an optical fiber network, a telecommunications network, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth network, a ZigBee network (ZigBee), a near field communication (NFC), a bus within a device, a line within a device, a cable connection, or any combination thereof. The network connection between the various parts may be in one of the above-mentioned ways, or in a variety of ways. In some embodiments, the network 130 may be a point-to-point, shared, centralized, or a combination of various topologies. In some embodiments, the network 130 may include one or more network access points. For example, the network 130 may include wired and / or wireless network access points, such as base stations and / or Internet access points, through which one or more components of the medical image scanning system may be connected to the network 130 to exchange data and / or information.

[0036] The storage device 140 may store data, instructions, and / or any other information. In some embodiments, the storage device 140 may store data obtained from the imaging device 110, the processor 120, and / or the terminal 150. In some embodiments, the storage device 140 may store data and / or instructions used by the processor 120 to execute or use the exemplary methods described in this specification to complete.

[0037] In some embodiments, the storage device 140 may include a mass storage device, a removable storage device, a volatile read-write memory, a read-only memory (ROM), etc. or any combination thereof. In some embodiments, the storage device 140 may be implemented on a cloud platform.

[0038] In some embodiments, the storage device 140 may be connected to the network 130 to communicate with one or more components of the medical image scanning system (e.g., the imaging device 110, the processor 120, the terminal 150, etc.). One or more components of the medical image scanning system may access data or instructions stored in the storage device 140 via the network 130. In some embodiments, the storage device 140 may be part of the processor 120.

[0039] The terminal 150 may communicate and / or connect with the imaging device 110, the processor 120, the storage device 140, and / or the printing device 160. For example, the terminal 150 may send one or more control instructions to the imaging device 110 via the network 130 to control the imaging device 110 to scan the target object according to the instructions. In some embodiments, the terminal 150 may include one or any combination of other devices with input and / or output functions such as a mobile device 150-1, a tablet computer 150-2, a laptop computer 150-3, etc. In some embodiments, the terminal 150 may remotely operate the imaging device 110. In some embodiments, the terminal 150 may operate the imaging device 110 via a wireless connection. In some embodiments, the terminal 150 may receive information and / or instructions input by a user, and send the received information and / or instructions to the imaging device 110, the processor 120, or the printing device 160 via the network 130. In some embodiments, the terminal 150 may receive data and / or information from the processor 120. In some embodiments, the terminal 150 may be part of the processor 120. In some embodiments, the terminal 150 may be integrated with the processor 120 to serve as an operating console for the imaging device 110. In some embodiments, the terminal 150 may be omitted.

[0040] The printing device 160 may be used to print the medical image. In some embodiments, the processor 120 may send the medical image to the printing device 160 via the network 130 for printing.

[0041] It should be noted that the application scenario 100 of the medical image scanning system is provided for illustrative purposes only and is not intended to limit the scope of this specification. For those of ordinary skill in the art, various modifications or variations can be made based on the description of this specification. For example, the application scenario 100 can be implemented on other devices to achieve similar or different functions. However, the changes and modifications will not deviate from the scope of this specification.

[0042] Figure 2 2 is an exemplary module diagram of a medical image scanning system according to some embodiments of the present specification. In some embodiments, the medical image scanning system 200 may include an image acquisition module 210 , a quality analysis module 220 , a display module 230 , and a printing module 240 .

[0043] The image acquisition module 210 may be used to acquire at least one scanned image of the target object based on the first scan sequence. For more information about acquiring at least one scanned image, please refer to step 310 and its related description.

[0044] The quality analysis module 220 may be configured to determine whether the image quality of at least one scanned image meets the image quality requirement based on a preset algorithm.

[0045] In some embodiments, the quality analysis module 220 may further process the at least one scanned image based on the image quality judgment model to judge whether the image quality of the at least one scanned image meets the image quality requirements, and determine the abnormality level and abnormality cause of the at least one scanned image. For more information about judging whether the image quality of the scanned image meets the image quality requirements and the image quality judgment model, please refer to step 320 and its related description.

[0046] The display module 230 can be used to determine that the scanned images that do not meet the image quality requirements are abnormal scanned images, and mark the abnormal scanned images and then display them to the user. For more information about determining and marking abnormal scanned images, please refer to step 330 and its related description.

[0047] The printing module 240 may be used to obtain the user's processing operation on the abnormal scan image and print the medical image after the processing operation.

[0048] In some embodiments, the printing module 240 can be further used to start automatic image swing based on the user's operation; the automatic image swing includes: automatically swinging the scanned image of interest based on the swing mode. The scanned image of interest is one or more scanned images after the abnormal scanned images are deleted. For more information about printing of medical images and automatic image swing, please refer to step 340 and its related description.

[0049] In some embodiments, the medical image scanning system 200 may further include a detection module ( Figure 2 (not shown). The detection module can be used to detect whether there is a lesion in each of the at least one scanned image; determine the scanned image with a lesion and a lesion size smaller than a preset size as the target scanned image; and determine the second scanned sequence based on the first scanned sequence corresponding to the target scanned image, so that the updated lesion size in the updated scanned image corresponding to the second scanned sequence is not smaller than the preset size. For more information on detecting lesions and determining the second scanned sequence, please refer to Figure 5 and its related description.

[0050] It should be noted that the above description of the medical image scanning system 200 and its modules is only for convenience of description and does not limit the present specification to the scope of the embodiments. It is understandable that, after understanding the principle of the system, those skilled in the art may arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from the principle. In some embodiments, Figure 2 The image acquisition module 210, quality analysis module 220, display module 230 and printing module 240 disclosed in the specification can be different modules in a system, or a module can realize the functions of two or more modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the protection scope of this specification.

[0051] Figure 3 is an exemplary flow chart of a medical image scanning method according to some embodiments of the present specification. In some embodiments, the medical image scanning method can be executed by the processor 120 or the medical image scanning system 200. For example, process 300 can be stored in a storage device (e.g., storage device 140) in the form of a program or instruction, and when the processor 120 or the medical image scanning system 200 executes the program or instruction, process 300 can be implemented. The operational diagram of process 300 presented below is illustrative. In some embodiments, the process can be completed using one or more additional operations not described and / or one or more operations not discussed. In addition, Figure 3 The order in which the operations of process 300 are illustrated and described below is not intended to be limiting.

[0052] Step 310 : Acquire at least one scanned image of the target object based on the first scan sequence. In some embodiments, step 310 may be performed by the image acquisition module 210 .

[0053] The scanning sequence is related to the scanning method. When the scanning method is CT scanning, the scanning sequence may include but is not limited to a head scanning sequence, a neck scanning sequence, a chest scanning sequence, etc. When the scanning method is MRI scanning, the types of scanning sequences may include but are not limited to a spin echo sequence, a fast spin echo sequence, a gradient echo sequence, etc.

[0054] The first scanning sequence is a scanning sequence used for preliminary scanning of the target object. In actual application, when there is a lesion in the scanned image acquired by the first scanning sequence and the lesion size is smaller than the preset size, a second scanning sequence can be determined, and the target object can be scanned more finely according to the second scanning sequence. For more information about the second scanning sequence, please refer to Figure 5 and its related description.

[0055] The first scanning sequence may be determined in a variety of ways. For example, the first scanning sequence may be preset. For another example, the first scanning sequence may be obtained by obtaining input from a user (eg, a doctor or a technician).

[0056] In some embodiments, the first scanning sequence can be obtained based on the target object through a preset data comparison table. The preset data comparison table records the first scanning sequences corresponding to different target objects. The preset data comparison table can be preset based on prior knowledge or historical data.

[0057] In some embodiments, the image acquisition module 210 can scan the target object based on the first scanning sequence through an imaging device to acquire a scanned image. The way of acquiring the scanned image is related to the type of imaging device.

[0058] In some embodiments, when the imaging device is a magnetic resonance device, the image acquisition module 210 can perform magnetic resonance scanning on the target object through the magnetic resonance device to acquire raw data, and reconstruct the raw data to obtain at least one scanned image. Exemplary reconstruction methods may include but are not limited to partial Fourier reconstruction methods, parallel reconstruction methods, compressed sensing reconstruction methods, and deep learning-based reconstruction methods.

[0059] In some embodiments, when the imaging device is a CT scanning device, the image acquisition module 210 can perform CT scanning on the target object through the CT scanning device to obtain raw data, and reconstruct the raw data to obtain at least one scanned image. Exemplary reconstruction methods include but are not limited to back-projection methods, iterative reconstruction methods, analytical methods (including filtered back-projection methods and Fourier transform methods), etc.

[0060] Step 320 , based on a preset algorithm, determines whether the image quality of at least one scanned image meets the image quality requirement. In some embodiments, step 320 may be performed by the quality analysis module 220 .

[0061] Image quality requirements refer to requirements that the image quality of the scanned image needs to meet. In some embodiments, the image quality requirements are at least related to one or more of the following combinations: whether there are artifacts, the severity of the artifacts, whether there are regions of interest, and whether there are lesions.

[0062] Artifacts refer to various forms of images that appear on the scanned image even though the target object does not exist. For example, artifacts of CT scan images include but are not limited to motion artifacts, metal artifacts, etc. For another example, artifacts of MRI scan images include but are not limited to chemical shift artifacts, curling artifacts, truncation artifacts, etc.

[0063] The severity level of an artifact refers to a parameter used to evaluate the degree of difference between a scanned image with an artifact and a normal scanned image. The greater the difference between a scanned image with an artifact and a normal scanned image, the greater the severity level of the artifact. The severity level of an artifact can be represented in a variety of ways, such as numbers and levels. For example, the severity level of an artifact can include mild, moderate, severe, and the like.

[0064] In some embodiments, the image quality requirement may be that there are no artifacts in the scanned image. When there are no artifacts in the scanned image, it indicates that the scanned image meets the image quality requirement.

[0065] In some embodiments, the image quality requirement may be that there are artifacts in the scanned image, but the severity level of the artifacts is lower than a preset level threshold. For example, the preset level threshold may be moderate, and when the severity level of the artifacts is mild, it indicates that the scanned image meets the image quality requirement. The preset level threshold may be a system default value, an experience value, a manually preset value, or any combination thereof, and may be set according to actual needs, and this specification does not limit this.

[0066] In some embodiments, the image quality requirement may be that there is a region of interest in the scanned image. When there is a region of interest in the scanned image, it indicates that the scanned image meets the image quality requirement.

[0067] In some embodiments, the image quality requirement may also be that there is a lesion in the scanned image. When there is a lesion in the scanned image, it means that the scanned image meets the image quality requirement.

[0068] In some embodiments, the image quality requirement may be a combination of any one or more of the above embodiments, and may be set and adjusted according to actual needs, which is not limited in this specification.

[0069] The preset algorithm can be in various forms.

[0070] In some embodiments, the preset algorithm may include: calculating the image similarity between the acquired scanned image and the reference scanned image, and determining the scanned image whose image similarity is less than the similarity threshold as a scanned image that does not meet the image quality requirements. Among them, the reference scanned image is a scanned image that meets the image quality requirements. The reference scanned image can be acquired based on historical scan data or simulated scanning. For example, a historical scanned image that meets the image quality requirements in the historical scan data can be used as a reference scanned image. The reference scanned image can include multiple types. For example, the reference scanned image can be a scanned image without artifacts. For another example, the reference scanned image can be a scanned image with artifacts, but the severity level of the artifacts is lower than the preset level threshold.

[0071] In some embodiments, the preset algorithm may also include: determining whether the scanned image meets the image quality requirements based on a machine learning model.

[0072] In some embodiments, the quality analysis module 220 may process at least one scanned image based on the image quality judgment model, judge whether the image quality of at least one scanned image meets the image quality requirements, and determine the abnormality level and abnormality cause of at least one scanned image. In some embodiments, the quality analysis module 220 may judge a scanned image whose abnormality level is lower than a preset level threshold as not meeting the image quality requirements. The preset level threshold may be a system default value, an experience value, a manually preset value, or any combination thereof, and may be set according to actual needs, and this specification does not limit this.

[0073] The image quality judgment model may be a machine learning model for judging whether the image quality of the scanned image meets the image quality requirements and determining the abnormality level and abnormality cause. For example, the image quality judgment model may include a convolutional neural network (CNN) and the like.

[0074] In some embodiments, the input of the image quality judgment model may include at least one scanned image; the output may include a judgment result of whether the image quality of the at least one scanned image meets the image quality requirements, and the abnormality level and abnormality reason corresponding to the at least one scanned image. The output of the image quality judgment model can be represented by a vector, for example, (1, 0, 0) indicates that the image quality of the scanned image meets the image quality requirements, and the abnormality level and abnormality reason are 0; (0, a, b) indicates that the image quality of the scanned image does not meet the image quality requirements, the abnormality level is a, and the abnormality reason is b.

[0075] The abnormality level refers to the level used to evaluate whether the scanned image does not meet the image quality requirements. For example, the abnormality level may include but is not limited to slightly not meeting the image quality requirements, moderately not meeting the image quality requirements, and severely not meeting the image quality requirements.

[0076] The abnormal reason refers to the reason why the scanned image does not meet the image quality requirements. For example, the abnormal reason may include but is not limited to the presence of motion artifacts, the presence of metal artifacts, the absence of a region of interest, etc.

[0077] In some embodiments, the image quality judgment model can be trained by a plurality of first training samples with first labels.

[0078] In some embodiments, the first training sample may include a sample scan image, and the first label may include a sample abnormality level and a sample abnormality reason corresponding to the group of first training samples. In some embodiments, the first training sample may be obtained based on historical data (e.g., historical scan images), and the first label may be obtained by manual annotation.

[0079] In some embodiments of the present specification, at least one scanned image is processed through an image quality judgment model to determine the abnormality level and abnormality cause corresponding to the scanned image. The influence of multiple factors can be considered at the same time, making the determination of the abnormality level and the abnormality cause more efficient and accurate, and avoiding errors in manual determination.

[0080] Step 330 , determining that the scanned image that does not meet the image quality requirement is an abnormal scanned image, and marking the abnormal scanned image and then displaying it to the user. In some embodiments, step 330 may be performed by the display module 230 .

[0081] An abnormal scan image refers to a scan image that does not meet the image quality requirements. For example, an abnormal scan image may include a scan image with artifacts, a scan image without lesions, etc.

[0082] In some embodiments, the presentation module 230 may determine the scanned image that does not meet the image quality requirement as an abnormal scanned image.

[0083] In some embodiments, the display module 230 can mark abnormal scan images in a variety of ways, such as marking abnormal scan images with a red box, marking abnormal scan images with a red exclamation mark, marking abnormal scan images with text descriptions, etc., or any combination thereof.

[0084] In some embodiments, the display module 230 may display the abnormal scan image differently, and highlight one or more of the abnormal area, abnormal level, and abnormal cause in the abnormal scan image.

[0085] In some embodiments, the differential display may include differentially displaying abnormal scan images and normal scan images, wherein the normal scan images refer to scan images that meet image quality requirements.

[0086] In some embodiments, the display module 230 can display abnormal scan images and normal scan images in a variety of ways. For example, the display module 230 can display abnormal scan images and normal scan images in different colors. For another example, the display module 230 can add elements of different shapes (such as exclamation marks, triangles, etc.) to display abnormal scan images and normal scan images.

[0087] In some embodiments, highlighting may include displaying one or more of an abnormal area, an abnormal level, and an abnormal cause in the abnormal scan image in distinction from the entire abnormal scan image. The abnormal area refers to a partial image area in the abnormal scan image that does not meet the image quality requirements. For example, the abnormal area may be a partial image area in the abnormal scan image that has artifacts.

[0088] In some embodiments, the display module 230 can highlight one or more of the abnormal area, abnormal level, and abnormal cause in the abnormal scan image in a variety of ways. For example, the display module 230 can frame the abnormal area with a red box to highlight the abnormal area. For another example, the display module 230 can display the abnormal level and / or abnormal cause in the abnormal scan image using text or other methods.

[0089] In some embodiments of the present specification, by distinguishing between abnormal scan images and normal scan images and highlighting the abnormal areas, abnormal levels and causes of the abnormalities, users can intuitively distinguish the categories of different scan images, their abnormal areas and severity levels, so that users can process different categories of scan images in different ways.

[0090] In some embodiments, the display module 230 can send the marked abnormal scan image to a terminal (e.g., terminal 150) and display it to the user through the terminal. In some embodiments, the display module 230 can display the marked abnormal scan image to the user in various situations. For example, the display module 230 can mark the detected abnormal scan image in real time and display it to the user during the process of the imaging device scanning the target object. For another example, the display module 230 can display the marked abnormal scan image to the user after the scan is completed and before the image printing is started. For another example, the display module 230 can display the marked abnormal scan image to the user when the scan is completed and it is determined to print the image. For another example, the display module 230 can display the marked abnormal scan image to the user after the scan is completed and the image is placed, before the image printing is started. For more information about image printing and placing the image, please refer to step 340 and its related description.

[0091] In some embodiments, the display module 230 can also display the marked abnormal scan image to the user through voice playback, etc. For example, the display module 230 can use voice playback "An abnormal scan image is detected, the abnormal level is xx, the abnormal reason may be xx, please confirm." and other prompt information.

[0092] Step 340 , obtaining the user's processing operation on the abnormal scan image, and printing the medical image after the processing operation. In some embodiments, step 340 may be performed by the printing module 240 .

[0093] In some embodiments, the processing operation may include multiple operation options, such as deleting the abnormal scan image, retaining the abnormal scan image, viewing the abnormal scan image, and the like.

[0094] In some embodiments, the user can input the processing operation through the terminal, and the printing module 240 can obtain the user's processing operation by communicating with the terminal. The way of inputting the processing operation through the terminal includes touch screen input, keyboard input, voice input, etc.

[0095] Medical image printing refers to developing the scanned image on media such as photographic paper and film.

[0096] In some embodiments, the printing module 240 can send the scanned image after the processing operation to the printing device, and realize the printing of the medical image through the printing device. In some embodiments, the printing module 240 can send part of the scanned image (for example, part of the scanned image after deleting the abnormal scanned image) to the printing device for medical image printing. In some embodiments, the printing module 240 can send all the scanned images to the printing device for medical image printing.

[0097] In some embodiments, after obtaining the user's processing operation on the abnormal scanned image, the medical image printing stage can be automatically entered. In some embodiments, after obtaining the user's processing operation on the abnormal scanned image, the user can manually determine to enter the medical image printing stage.

[0098] In some embodiments, the printing module 240 may start automatic image placement based on a user operation, and then print the medical image after the image placement is completed.

[0099] In some embodiments, the user operation may include a user selecting whether to start the automatic drawing. When the user selects to start the automatic drawing, the printing module 240 performs the automatic drawing. The user operation may be obtained through the terminal.

[0100] In some embodiments, the automatic image swing process includes: automatically swinging the scanned image of interest based on the image swing mode.

[0101] In some embodiments, the scan image of interest refers to one or more scan images after abnormal scan images are deleted.

[0102] The arrangement mode refers to the arrangement mode of at least one scanned image on a photographic paper, a film, etc. In some embodiments, the arrangement mode may include at least one of arranging the image in sequence, arranging the image in orientation, and arranging the image in accordance with an organ.

[0103] Arranging the images in sequence means arranging the images in accordance with the scanning sequence. For example, the scanned images corresponding to the same scanning sequence can be placed on the same photographic paper or film.

[0104] Arranging the images according to the orientation refers to arranging the images according to the scanning orientations such as sagittal, coronal, and transverse. For example, the scanned images corresponding to the same scanning orientation can be placed at adjacent positions on the photographic paper or film.

[0105] Organ placement refers to placing images according to organ types. For example, scanned images corresponding to the same organ type can be placed on the same photographic paper or film.

[0106] The layout mode can be determined in a variety of ways. The layout mode can be determined based on the user's user operation. For example, after the user chooses to start the automatic layout, he can also select (in this case, the terminal provides the user with a variety of selectable layout modes) or input the layout mode (for example, through voice input, keyboard input, etc.). The layout mode can also be determined based on other methods. For example, the processor can determine the layout mode commonly used by the user as the default layout mode.

[0107] In some embodiments of the present invention, automatic image placement is initiated through user operation, and the image placement method is determined based on the user's selection. This not only simplifies the user's operating process, but also allows the scanned image to be placed and printed according to the user's habits, making it easier for the user to view and diagnose the film.

[0108] In some embodiments of the present specification, by determining abnormal scan images and marking them and then displaying them to the user, not only is automatic recognition of abnormal scan images achieved, but the user can also more intuitively distinguish which ones are abnormal scan images, making it easier for the user to perform related operations on the abnormal scan images.

[0109] In some embodiments, the process of performing image quality control by the quality analysis module 220 can be enabled or disabled by the user. Figure 4 It is an exemplary schematic diagram of a medical image scanning method according to some embodiments of the present specification.

[0110] By way of example only, in some embodiments, Figure 4As shown, the medical image scanning method may include: the user turns on the image quality control function, and the processor performs image quality screening on at least one scanned image. When there is an unqualified scanned image (i.e., it does not meet the image quality requirements), the processor identifies it as an abnormal scanned image and gives the abnormal reason for the abnormal scanned image. After the scan is completed, the user can choose whether to need smart printing. If the user chooses no (i.e., smart printing is not required), the user confirms whether to print the medical image. In response to the user confirming to print the medical image, the processor controls the printing device to print the medical image. If the user chooses yes (i.e., smart printing is required), the processor can obtain the image swing mode. After confirming the image swing mode, the processor controls the printing interface of the printing device to display the marked abnormal scanned image, and reminds the user to verify the image quality of the abnormal scanned image. After the verification is completed, the user confirms whether to delete the abnormal scanned image. After the user confirms to print the medical image, the medical image is printed by the printing device. In this embodiment, when the user confirms that the abnormal scan image needs to be deleted, the printing device can arrange and print the partial scan image after the abnormal scan image is deleted; when the user confirms that the abnormal scan image does not need to be deleted, the printing device can arrange and print all the scan images.

[0111] In some embodiments, the display module 230 may also be used to: determine scanned images that are not of interest, and mark the scanned images that are not of interest and then display them to the user.

[0112] A scan image of no interest refers to a scan image that has no diagnostic significance. For example, a scan image of no interest may be a scan image that does not contain a target object.

[0113] In some embodiments, the non-interested scanned images may include a scanned locator image, one or more of a preset number of scanned images acquired first and / or last in at least one scanned image, wherein the preset number may be a preset value, a default value, an empirical value, etc. or any combination thereof.

[0114] The scanning positioning image refers to an image used to determine the scanning range and scanning parameters of the target object.

[0115] The first or last scanned image obtained refers to the first or last scanned image obtained when scanning is performed using the scanning sequence.

[0116] In some embodiments, the display module 230 can display non-interesting images in a differentiated manner. For example, the display module 230 can display non-interesting images, normal scan images, and abnormal scan images in a differentiated manner in different ways. Exemplarily, the display module 230 can display non-interesting images, normal scan images, and abnormal scan images in a differentiated manner in ways such as text, labels, and colors.

[0117] In some embodiments of the present specification, by marking the scanned images of no interest and presenting them to the user, the user can more intuitively distinguish which scanned images are of no interest, so that the user can perform further operations (such as deletion) on the scanned images of no interest.

[0118] Figure 5 is an exemplary flow chart of another medical image scanning method shown in some embodiments of the present specification. In some embodiments, the medical image scanning method can be executed by the processor 120 or the medical image scanning system 200 (for example, a detection module). For example, process 500 can be stored in a storage device (for example, storage device 140) in the form of a program or instruction, and when the processor 120 or the medical image scanning system 200 executes the program or instruction, process 500 can be implemented. The operational diagram of process 500 presented below is illustrative. In some embodiments, the process can be completed using one or more additional operations not described and / or one or more operations not discussed. In addition, Figure 5 The order in which the operations of flow 500 are illustrated and described below is not intended to be limiting.

[0119] Step 510: Detect whether there is a lesion in each of the at least one scanned image.

[0120] The detection module can detect the lesion in a variety of ways. In some embodiments, the detection module can perform image processing on the scanned image and determine whether there is a lesion according to the result of the image processing. Exemplary image processing methods include image segmentation (e.g., image segmentation based on threshold-based methods, clustering-based methods, region-based methods, active boundary-based and level set-based methods), image feature extraction (e.g., extracting the shape and texture of the scanned image), image enhancement display (e.g., including PCA jitter, random cropping reflection transformation, flip transformation, scaling transformation, translation transformation, contrast transformation, noise disturbance, color change, etc.), etc.

[0121] In some embodiments, the detection module can also process the scanned image through a lesion recognition model to determine whether a lesion exists. In some embodiments, the lesion recognition model is a machine learning model, such as CNN.

[0122] In some embodiments, the input of the lesion recognition model may include a scanned image, and the output may include whether a lesion exists and the location data and size data of the lesion. The output of the lesion recognition model may be represented by a vector. For example, it may be represented as (0, 0, 0) or (1, c, d), where (0, 0, 0) indicates that there is no lesion in the scanned image, and (1, c, d) indicates that there is a lesion in the scanned image and the location data of the lesion is c and the size data is d.

[0123] In some embodiments, the lesion recognition model can be obtained by training a large number of second training samples with second labels. In some embodiments, the second training samples include sample scanned images, and the second labels include the result of whether the sample scanned images contain lesions, and when the lesions exist, the size data and location data of the lesions, etc. In some embodiments, the second training samples can be obtained through historical scan data, and the second labels can be determined by manual annotation.

[0124] In some embodiments, the detection module may also process the scanned image through an image quality judgment model to determine whether there is a lesion. When determining the abnormality level and abnormality cause of the scanned image, the image quality judgment model may also determine whether there is a lesion in the scanned image and the location data, size data, etc. of the lesion. Accordingly, the first label used to train the image quality judgment model may also include the result of whether there is a lesion in the sample scanned image and the size data and location data of the lesion when there is a lesion. In this embodiment, the first label may be determined by manual annotation.

[0125] In some embodiments of the present specification, scanned images are processed through a machine learning model to determine whether there are lesions and information such as the size of the lesions in the scanned images. The self-learning ability of machine learning can be fully utilized to find patterns from a large number of scanned images, obtain the correlation between the scanned images and the lesions, and improve the accuracy of determining whether there are lesions and the size of the lesions.

[0126] Step 520: Determine that the scanned image in which the lesion exists and the size of the lesion is smaller than a preset size is the target scanned image.

[0127] The preset size can be used to screen lesions whose size can be directly recognized by the human eye. When the lesion size is not less than the preset size, the lesion can be directly recognized by the human eye. The preset size system default value, experience value, artificial pre-set value, etc. or any combination thereof can be set according to actual needs, and this manual does not limit this.

[0128] Step 530: Determine a second scanning sequence based on the first scanning sequence corresponding to the target scanning image, so that the updated lesion size in the updated scanning image corresponding to the second scanning sequence is not less than a preset size.

[0129] The second scanning sequence refers to an updated scanning sequence, which can scan the target object more finely, so that originally tiny lesions can be directly identified by human eyes.

[0130] The updated scan image refers to a scan image obtained after scanning the target object based on the second scan sequence. The updated scan image may include one or more.

[0131] The updated lesion size refers to the lesion size obtained when lesion detection is performed on the updated scan image.

[0132] The detection module can determine the second scanning sequence in a variety of ways based on the first scanning sequence corresponding to the target scanning image. In some embodiments, the detection module can adjust the first scanning sequence corresponding to the target scanning image based on a preset adjustment rule to obtain a second scanning sequence. In some embodiments, the preset adjustment rule can be to determine the adjustment scheme for the first scanning sequence by querying a first preset comparison table based on the difference between the preset size and the current lesion size. Among them, the first preset comparison table includes the correspondence between different differences and different adjustment schemes, and the first preset comparison table can be pre-set based on prior knowledge or historical data. In some embodiments, the preset adjustment rule can also be to determine the adjustment scheme for the second scanning sequence by querying a second preset comparison table based on the organ or tissue location where the lesion is located. Among them, the second preset comparison table includes the correspondence between different organ or tissue locations and different adjustment schemes, and the second preset comparison table can be pre-set based on prior knowledge or historical data. For example, the first scanning sequence of the target scanning image can be axial T2. When the lesion location is determined to be in the brainstem or lateral ventricle, the adjustment scheme can be determined to be the addition of sagittal T2 scanning; when the lesion location is determined to be in the skull base and CPA, the adjustment scheme can be determined to be the addition of coronal imaging.

[0133] In some embodiments of the present specification, when the size of a lesion in a scanned image is smaller than a preset size, a second scanning sequence is determined by a first scanning sequence corresponding to the scanned image, and the target object is re-scanned to obtain an updated scanned image, which not only ensures that the lesions on the scanned image can be clearly viewed by the user, but also avoids the missed diagnosis of tiny lesions to a certain extent.

[0134] Just as an example, Figure 6As shown, in some embodiments, another medical image scanning method may include: the user turns on the image quality control function, and the processor performs image quality screening on at least one scanned image. When there is a scanned image that is unqualified (i.e., does not meet the image quality requirements), the processor identifies it as an abnormal scanned image and gives the abnormal reason for the abnormal scanned image. After the scan is completed, the processor detects whether there is a scanned image with a lesion size smaller than the preset size. In response to no (i.e., there is no scanned image with a lesion size smaller than the preset size), the user selects whether to perform smart printing. In response to yes (i.e., there is a scanned image with a lesion size smaller than the preset size), the processor controls the imaging device to rescan the target object based on the second scanning sequence to obtain an updated scanned image. After the rescan is completed, the user selects whether to perform smart printing. If the user selects no (i.e., smart printing is not required), the user confirms whether to perform medical image printing. In response to the user confirming to perform medical image printing, the processor controls the printing device to print the medical image. If the user selects yes (i.e., smart printing is required), the processor can obtain the image swing mode. After confirming the swinging mode, the processor controls the printing interface of the printing device to display the abnormal scanned image after the mark, and reminds the user to verify the image quality of the abnormal scanned image. After the verification is completed, the user confirms whether to delete the abnormal scanned image. After the user confirms to print the medical image, the medical image is printed by the printing device. In this embodiment, when the user confirms that the abnormal scanned image needs to be deleted, the printing device can swing and print the part of the scanned image after the abnormal scanned image is deleted; when the user confirms that the abnormal scanned image does not need to be deleted, the printing device can swing and print all the scanned images.

[0135] It should be noted that the above description of the process 300 and the process 500 is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process 300 and the process 500 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0136] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0137] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment" 、 "One embodiment" 、and / or "some embodiments" means a certain feature, structure or characteristic related to at least one embodiment of the present specification. Therefore, it should be emphasized and noted that "an embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different places in the present specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of the present specification may be appropriately combined.

[0138] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0139] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0140] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0141] It should be noted that if there is any inconsistency or conflict between the descriptions, definitions, and / or usage of terms in the supplementary materials of this specification and the contents described in this specification, the descriptions, definitions, and / or usage of terms in this specification shall prevail.

[0142] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A medical image scanning method, characterized in that: The method comprises: Based on the first scanning sequence, acquiring at least one scanned image of the target object; Based on a preset algorithm, determining whether the image quality of the at least one scanned image meets the image quality requirement, where the image quality requirement is at least related to one or more of the following combinations: whether there is an artifact, the severity level of the artifact, whether there is a region of interest, and whether there is a lesion; Determining the scanned image that does not meet the image quality requirement as an abnormal scanned image, and marking the abnormal scanned image and then displaying it to the user; The processing operation of the user on the abnormal scan image is obtained, and the medical image is printed after the processing operation.

2. The method according to claim 1, characterized in that The determining, based on a preset algorithm, whether the image quality of the at least one scanned image meets the image quality requirement includes: The at least one scanned image is processed based on an image quality judgment model to determine whether the image quality of the at least one scanned image meets the image quality requirements, and to determine the abnormality level and abnormality cause of the at least one scanned image; the image quality judgment model is a machine learning model.

3. The method according to claim 1, characterized in that The method further comprises: Based on the user operation of the user, start automatic layout; The automatic drawing comprises: Based on a swaying method, the scanned images of interest are automatically swayed, the scanned images of interest being one or more scanned images after deleting the abnormal scanned images, and the swaying method includes at least one of swaying according to sequence, swaying according to orientation, and swaying according to organs.

4. The method according to claim 1, characterized in that: The method further comprises: Determine scan images that are not of interest, and display them to the user after marking them; the scan images that are not of interest include scan positioning images, one or more of a preset number of scan images that are acquired first and / or last in the at least one scan image.

5. The method according to claim 4, characterized in that The identification includes: The abnormal scan image and the scan image of no interest are displayed distinctively, and one or more of an abnormal area, an abnormal level and an abnormal cause in the abnormal scan image are highlighted.

6. The method according to claim 1, characterized in that The method further comprises: Detecting whether a lesion exists in each of the at least one scanned image; Determine that the scanned image in which the lesion exists and the size of the lesion is smaller than a preset size is a target scanned image; Based on the first scanning sequence corresponding to the target scanning image, a second scanning sequence is determined so that the updated lesion size in the updated scanning image corresponding to the second scanning sequence is not less than the preset size.

7. A medical image scanning system, characterized in that: The system comprises: An image acquisition module, configured to acquire at least one scanned image of the target object based on the first scan sequence; A quality analysis module, configured to determine, based on a preset algorithm, whether the image quality of the at least one scanned image meets an image quality requirement, wherein the image quality requirement is at least related to one or more of the following combinations: whether an artifact exists, the severity level of the artifact, whether a region of interest exists, and whether a lesion exists; A display module, used for determining the scanned image that does not meet the image quality requirement as an abnormal scanned image, and marking the abnormal scanned image and then displaying it to the user; The printing module is used to obtain the processing operation of the user on the abnormal scan image and print the medical image after the processing operation.

8. The system according to claim 7, characterized in that The quality analysis module is further used for: The at least one scanned image is processed based on an image quality judgment model to determine whether the image quality of the at least one scanned image meets the image quality requirements, and to determine the abnormality level and abnormality cause of the at least one scanned image, wherein the image quality judgment model is a machine learning model.

9. The system according to claim 7, characterized in that The printing module is further used for: Based on the user operation of the user, start automatic layout; The automatic drawing comprises: Based on a swaying method, the scanned images of interest are automatically swayed, the scanned images of interest being one or more scanned images after deleting the abnormal scanned images, and the swaying method includes at least one of swaying according to sequence, swaying according to orientation, and swaying according to organs.

10. A computer-readable storage medium storing computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the medical image scanning method according to any one of claims 1 to 6.

Images