Real-time positioning method, device and equipment for carotid artery blood vessel, medium and all-in-one machine

Abstract

The invention relates to the technical field of artificial intelligence, in particular to a carotid artery blood vessel real-time positioning method, device and equipment, a medium and an all-in-one machine. Acquiring a real-time carotid artery ultrasonic image; and performing feature extraction on the real-time carotid artery ultrasonic image to obtain real-time image features of the real-time carotid artery ultrasonic image, and performing real-time positioning on the carotid artery blood vessel in the real-time carotid artery ultrasonic image according to the real-time image features to obtain a real-time positioning result of the carotid artery blood vessel. In the application, the real-time carotid artery ultrasonic image is acquired, and the real-time carotid artery ultrasonic image is subjected to feature extraction, so that the carotid artery blood vessel in the real-time carotid artery ultrasonic image is positioned in real time, the real-time positioning result of the carotid artery blood vessel is obtained, and the flow velocity of the carotid artery blood vessel is monitored in real time according to the real-time positioning result. The real-time monitoring result is obtained, so that the corresponding monitoring result can be fed back in real time, the phenomenon of poor prognosis of the patient is prevented, and the cardio-pulmonary resuscitation treatment efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a real-time positioning method, device, equipment, medium and all-in-one machine for carotid artery blood vessels. Background Art

[0002] Cardiopulmonary resuscitation is the most effective method for rescuing patients with cardiac arrest. High-quality cardiopulmonary resuscitation is of great significance for the recovery of spontaneous circulation and survival of patients with cardiac arrest. At present, there is no means or indicators to monitor the resuscitation process in real time when clinicians perform cardiopulmonary resuscitation for patients with cardiac arrest. They can only perform chest compressions according to clinical guidelines and judge and evaluate the patient's condition during the pause of compressions. It is even more impossible to dynamically adjust the cardiopulmonary resuscitation operation based on the indicator data fed back by real-time monitoring. When cardiac arrest occurs, the patient's brain is the most susceptible part to ischemic and hypoxic damage. Even if cardiopulmonary resuscitation is successful, it may leave different degrees of neurological sequelae, leading to poor prognosis. Therefore, the ultimate goal of cardiopulmonary resuscitation is to achieve brain resuscitation, and the blood supply level of the patient's brain tissue during the resuscitation process can well reflect the quality of brain resuscitation. Therefore, how to locate the carotid artery in real time during cardiopulmonary resuscitation in order to monitor the carotid artery flow rate in real time has become an urgent problem to be solved. Summary of the invention

[0003] In view of this, the embodiments of the present invention provide a method, device, equipment, medium and all-in-one machine for real-time positioning of the carotid artery to solve the problem that the carotid artery cannot be positioned in real time during cardiopulmonary resuscitation.

[0004] In a first aspect, an embodiment of the present invention provides a real-time positioning method for a carotid artery, the real-time positioning method comprising: Acquire real-time carotid ultrasound images; Performing feature extraction on the real-time carotid artery ultrasound image to obtain real-time image features of the real-time carotid artery ultrasound image; The carotid artery blood vessel in the real-time carotid artery ultrasound image is positioned in real time according to the real-time image feature to obtain a real-time positioning result of the carotid artery blood vessel.

[0005] In a second aspect, an embodiment of the present invention provides a real-time positioning device for a carotid artery, the real-time positioning device comprising: An acquisition module, used for acquiring a real-time carotid artery ultrasound image; A feature extraction module, used for extracting features from the real-time carotid artery ultrasound image to obtain real-time image features of the real-time carotid artery ultrasound image; The positioning module is used to perform real-time positioning of the carotid artery in the real-time carotid artery ultrasound image according to the real-time image features to obtain a real-time positioning result of the carotid artery.

[0006] In a third aspect, an embodiment of the present invention provides a computer device, characterized in that the computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the real-time positioning method as described in the first aspect is implemented.

[0007] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and wherein the computer program, when executed by a processor, implements the real-time positioning method as described in the first aspect.

[0008] In a fifth aspect, an embodiment of the present invention provides an all-in-one machine, comprising a main body of a cardiopulmonary-cerebral resuscitation machine and a miniature patch-type ultrasound probe, wherein the main body of the cardiopulmonary-cerebral resuscitation machine is connected to the miniature patch-type ultrasound probe and is used to process a carotid artery ultrasound image scanned by the miniature patch-type ultrasound probe; The miniature patch type ultrasound probe is used for scanning the neck to obtain a real-time carotid artery ultrasound image.

[0009] Compared with the prior art, the present invention has the following beneficial effects: Acquire a real-time carotid ultrasound image; perform feature extraction on the real-time carotid ultrasound image to obtain real-time image features of the real-time carotid ultrasound image; perform real-time positioning of the carotid blood vessels in the real-time carotid ultrasound image based on the real-time image features to obtain real-time positioning results of the carotid blood vessels. In the present application, by acquiring a real-time carotid ultrasound image and performing feature extraction on the real-time carotid ultrasound image, the carotid blood vessels in the real-time carotid ultrasound image are positioned in real time to obtain real-time positioning results of the carotid blood vessels, thereby monitoring the flow velocity of the carotid blood vessels in real time based on the real-time positioning results to obtain real-time monitoring results, so that the corresponding monitoring results can be fed back in real time to prevent the patient from having a poor prognosis and improve the efficiency of cardiopulmonary resuscitation. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0011] Figure 1This is a schematic diagram of an application environment of a real-time positioning method for a carotid artery provided by an embodiment of the present invention; Figure 2 It is a flow chart of a real-time positioning method of a carotid artery provided by an embodiment of the present invention; Figure 3 It is a structural block diagram of a real-time positioning device for a carotid artery provided by an embodiment of the present invention; Figure 4 is a structural schematic diagram of a computer device provided by an embodiment of the present invention; Figure 5 It is a structural schematic diagram of an all-in-one machine provided by an embodiment of the present invention; Figure 6 1 is a view of a miniature patch-type ultrasound probe provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0014] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0016] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0017] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0018] The embodiments of the present invention can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0019] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0020] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0021] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0022] A real-time positioning method for a carotid artery provided by an embodiment of the present invention can be applied in Figure 1 In an application environment, a client communicates with a server. The client includes, but is not limited to, a PDA, a desktop computer, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), and other computer devices. The server can be implemented with an independent server or a server cluster consisting of multiple servers.

[0023] See also Figure 2, is a flow chart of a method for real-time positioning of a carotid artery provided by an embodiment of the present invention. The method for real-time positioning of a carotid artery can be applied to Figure 1 The server in the above connects to the corresponding client, such as Figure 2 As shown, the real-time positioning method of the carotid artery vessel may include the following steps.

[0024] S201: Acquire a real-time carotid artery ultrasound image.

[0025] In step S201, a real-time carotid artery ultrasound image is acquired, wherein the real-time carotid artery ultrasound image is a carotid artery ultrasound image obtained by scanning using a cardiopulmonary and brain resuscitation integrated machine during a cardiopulmonary resuscitation operation.

[0026] In this embodiment, during the cardiopulmonary resuscitation operation, a cardiopulmonary-cerebral resuscitation integrated machine is used to scan and obtain a real-time carotid artery ultrasound image, wherein the cardiopulmonary-cerebral resuscitation integrated machine includes a main body of the cardiopulmonary-cerebral resuscitation machine and a miniature patch-type ultrasound probe, and the main body of the cardiopulmonary-cerebral resuscitation machine is connected to the miniature patch-type ultrasound probe to process the real-time carotid artery ultrasound image scanned by the miniature patch-type ultrasound probe, and the miniature patch-type ultrasound probe is used to obtain a real-time carotid artery ultrasound image.

[0027] It should be noted that the micro-patch ultrasound probe in the cardiopulmonary-cerebral resuscitation machine includes at least 80 array elements. The shell of the micro-patch ultrasound probe is tilted at a certain angle and is fitted with a soft colloid with extremely low acoustic impedance. No coupling agent is required during use.

[0028] It should be noted that when acquiring the real-time carotid artery ultrasound image, a micro patch-type ultrasound probe is fixed on the neck of the individual being treated.

[0029] In this embodiment, a real-time carotid artery ultrasound image is acquired so as to perform real-time positioning of the carotid artery blood vessel in the real-time carotid artery ultrasound image, thereby obtaining a real-time positioning result of the carotid artery blood vessel.

[0030] S202: Extract features from the real-time carotid artery ultrasound image to obtain real-time image features of the real-time carotid artery ultrasound image.

[0031] In step S202, feature extraction is performed on the real-time carotid artery ultrasound image, that is, features in the real-time carotid artery ultrasound image are extracted, so as to identify and locate the carotid artery blood vessel in the real-time carotid artery ultrasound image according to the real-time image features.

[0032] In this embodiment, when extracting features from the real-time carotid ultrasound image, a convolutional neural network can be used for feature extraction. During the extraction, the real-time carotid ultrasound image is first dimensionally upgraded to increase the expressive power of the features in the real-time carotid ultrasound image, and then feature extraction is performed on the dimensionally upgraded real-time carotid ultrasound image to obtain a first image feature. The first image feature is a highly complex image feature, but its accuracy is relatively poor.

[0033] Therefore, in order to obtain high-precision image features, the real-time carotid ultrasound image is also subjected to dimensionality reduction processing to simplify the real-time carotid ultrasound image, and then feature extraction is performed on the reduced-dimensional real-time carotid ultrasound image to obtain a second image feature with high precision. Finally, the first image feature and the second image feature are fused using the h-swish function to obtain the feature information of the real-time carotid ultrasound image.

[0034] In another embodiment, a combined convolutional neural network can also be used to extract features from real-time carotid ultrasound images. The combined convolutional neural network here refers to a combined convolutional neural network obtained by combining 3DResnet (3D residual network) and FPN (feature pyramid network). By performing a convolution operation on the real-time carotid ultrasound image, the 3D network part can effectively use spatial information to extract features of the 3D image. The feature pyramid network (FPN) part is a feature extractor designed based on the concept of feature pyramid. The use of FPN can realize multi-scale feature extraction, and the use of FPN can realize global feature and local feature extraction. By combining the convolutional neural network, the spatial image features of multiple sizes corresponding to the real-time carotid ultrasound image are extracted, and then based on the obtained spatial image features of multiple sizes, the combined convolutional neural network is used to extract relevant feature maps to obtain the real-time image features of the real-time carotid ultrasound image with tissue abnormality. In practical applications, by using a combined convolutional neural network to extract relevant features, the 3D residual network in the combined convolutional neural network is first used to extract the features of the 3D image from the spatial information in the real-time carotid ultrasound image, and then the feature pyramid network in the combined convolutional neural network is used to extract multi-scale feature information from the feature information of the real-time carotid ultrasound image and its 3D image to obtain the real-time image features of the real-time carotid ultrasound image.

[0035] In another embodiment, a preset deep learning model may also be used to extract features from real-time carotid ultrasound images, such as a YOLOX model, and a deep learning model may be used to perform multi-scale feature extraction on real-time carotid ultrasound images to obtain N feature maps of different scales, wherein the N feature maps of different scales are feature maps of different scales, and N is an integer greater than 1. When obtaining N feature maps of different scales, a convolution model combination having multiple sampling rates may be used to perform convolution and parallel sampling on the real-time carotid ultrasound image at multiple sampling rates to obtain N feature maps of different scales. Here, the convolution model combination is used to enrich the semantic features of different levels in the semantic information of the real-time carotid ultrasound image. Specifically, the convolution model combination has multiple sampling rates, and the real-time carotid ultrasound image is convoluted and sampled in parallel at multiple sampling rates, which is equivalent to capturing the context of the real-time carotid ultrasound image at multiple scales. Feature fusion is performed on N feature maps of different scales to obtain a fused feature map, wherein the feature fusion can fuse features of different semantic information in the N feature maps of different scales together to obtain a feature map containing different deep semantic features, that is, a real-time image feature.

[0036] S203: Based on the real-time image features, the carotid artery in the real-time carotid artery ultrasound image is positioned in real time to obtain a real-time positioning result of the carotid artery.

[0037] In step S203, the carotid artery in the real-time carotid ultrasound image is identified according to the real-time image features, and is positioned according to the identification result to obtain a real-time positioning result of the carotid artery.

[0038] In this embodiment, when real-time positioning of the carotid artery in the real-time carotid ultrasound image is performed according to the real-time image features, the U-net network is used for positioning detection. The U-net network is a U-shaped network, and its network structure includes two main parts: the front-end encoder path and the back-end decoder path. The encoder part consists of 5 convolutional layers, each of which has two sizes of 3 convolution kernel. Each The fusion feature extraction module, BN (Batch normalization) layer, linear correction unit layer (ReLU) and a 2 maximum pooling operations for downsampling. Correspondingly, the encoder path is mainly composed of 5 consecutive feature extraction blocks consisting of convolutional layers, ReLu activation layers and pooling layers. 2 convolutions perform a 2x upsampling operation to restore the feature map, which can obtain more detailed information. At the same time, the high-level semantic features are integrated with the shallow features using skip links to retain more information. Softmax is used as the classification layer in the feature map output by the feature extraction block at the last layer of the decoder path. Convolution maps each component feature vector to the number of categories required for positioning detection, achieving pixel-by-pixel classification.

[0039] In another embodiment, a preset positioning model may be used to locate the carotid artery in the real-time carotid ultrasound image, wherein the preset positioning model is a pre-trained positioning model that can locate the carotid artery.

[0040] In another embodiment, the real-time image features can also be positioned by a bounding box regression algorithm to obtain a real-time positioning result of the carotid artery. The bounding box regression algorithm can be a bounding box regression algorithm of YOLOV3. In the positioning detection process, the bounding box regression algorithm of YOLOV3 first divides the input image into S×S cells of the same size, each cell is only responsible for predicting the target whose center is located in the cell, and one cell predicts B bounding boxes. The information contained in each bounding box is the offset x and y of the target center point relative to the upper left corner of the cell where the point is located, as well as the width w and height h of the predicted bounding box, and then the values ​​of x, y, w, and h are updated by back propagation calculation to obtain the predicted bounding box. This bounding box regression algorithm uses a pre-defined anchor box to directly train the entire image as input, eliminating the intermediate step of generating candidate areas, and can quickly distinguish between background areas and targets, thereby achieving rapid positioning of targets.

[0041] Optionally, based on the real-time image features, the carotid artery in the real-time carotid artery ultrasound image is positioned in real time to obtain a real-time positioning result of the carotid artery, including: Acquire preset image features, match the real-time image features with the preset image features, and obtain matched image features; According to the position of the carotid artery in the image corresponding to the matched image features, the carotid artery in the real-time carotid ultrasound image is located to obtain a positioning result of the carotid artery.

[0042] In this embodiment, a preset image is obtained, wherein the preset image is a carotid artery ultrasound image collected in multiple scenes, the carotid artery ultrasound image is equal in size to the real-time carotid artery ultrasound image, and the position where the micro-patch ultrasound probe is fixed is equal. Feature extraction is performed on the preset image to obtain preset image features, wherein the feature extraction method can refer to the above-mentioned feature extraction method, which is not limited in this embodiment.

[0043] After obtaining the preset image features, the real-time image features are matched with the preset image features to obtain matched image features. During the matching, the cosine value between the preset image features and the real-time image features can be calculated, the cosine value can be compared with the preset cosine threshold, a target cosine value greater than the preset cosine threshold and a matched image feature corresponding to the target cosine value can be obtained, the image corresponding to the matched image feature is used as the target image, and the carotid artery in the real-time carotid ultrasound image is located according to the position of the carotid artery in the target image to obtain the positioning result of the carotid artery, that is, the position of the carotid artery in the target image is determined as the position of the carotid artery in the real-time carotid ultrasound image.

[0044] It should be noted that the corresponding carotid artery position has been marked in the preset image.

[0045] It should be noted that when the image corresponding to the matched image features is used as the target image, there may be multiple target images. When there are multiple target images, the average value of the carotid artery position in the target image is calculated, and the average value is used as the position of the carotid artery in the final real-time carotid artery ultrasound image.

[0046] In this embodiment, the real-time image features are matched with the preset image features, and the target image corresponding to the matched image features can be quickly determined. The carotid artery in the real-time carotid ultrasound image is located according to the target image, thereby improving the efficiency of locating the carotid artery.

[0047] Optionally, before obtaining the preset image features, the following steps are also included: Acquire carotid artery ultrasound images in N scenes, where N is an integer greater than zero; For the carotid artery ultrasound image in any scenario, feature extraction is performed on the carotid artery ultrasound image to obtain preset image features; According to the preset image features, the carotid artery is identified and located to obtain the positioning result of the carotid artery.

[0048] In this embodiment, before obtaining the preset image features, carotid artery ultrasound images in N scenes are obtained, where N is an integer greater than zero, and the N scenes are scenes at different scanning depths or different frequencies.

[0049] For the carotid artery ultrasound image in any scenario, feature extraction is performed on the carotid artery ultrasound image to obtain preset image features, wherein the feature extraction method can refer to the above-mentioned feature extraction method, which is not limited in this embodiment.

[0050] According to the preset image features, the carotid artery is identified and located to obtain the location result of the carotid artery. That is, the location result of the carotid artery in the preset image is obtained in advance. The above-mentioned location method can be used to identify and locate the carotid artery, which is not limited in this embodiment.

[0051] Optionally, after real-time positioning of the carotid artery in the real-time carotid artery ultrasound image is performed according to the real-time image features and the real-time positioning result of the carotid artery is obtained, the method further includes: According to the real-time positioning result, the flow velocity of the carotid artery at the position corresponding to the real-time positioning result is monitored in real time to obtain the flow velocity result of the carotid artery.

[0052] In this embodiment, the carotid artery in the real-time carotid ultrasound image is positioned in real time. After the real-time positioning result of the carotid artery is obtained, the flow velocity of the carotid artery at the position corresponding to the real-time positioning result is monitored to obtain the flow velocity result of the carotid artery, wherein the monitoring result includes the flow velocity of the carotid artery at each time point, and is displayed on the corresponding terminal in the form of a waveform graph, wherein the horizontal axis of the waveform graph displayed on the terminal is time, and the vertical axis is flow velocity.

[0053] In this embodiment, the flow velocity of the carotid artery can be monitored in real time based on the real-time positioning results, so that during the cardiopulmonary resuscitation operation, the patient can be judged and evaluated in real time based on the real-time monitored flow velocity of the carotid artery to prevent ischemic and hypoxic injuries.

[0054] Optionally, after obtaining the flow velocity result of the carotid artery, the method further includes: According to the flow velocity results, the peak flow velocity, mean flow velocity, minimum flow velocity and vascular resistance index of the carotid artery were calculated; Displays peak flow rate, mean flow rate, minimum flow rate and vascular resistance index.

[0055] In the present embodiment, after obtaining the flow velocity result of the carotid artery, i.e., the waveform graph of the flow velocity of the carotid artery, the waveform graph of the flow velocity of the carotid artery calculates the monitoring parameters such as the peak flow velocity, mean flow velocity, minimum flow velocity and vascular resistance index of the carotid artery, and the monitoring parameters such as the peak flow velocity, mean flow velocity, minimum flow velocity and vascular resistance index are displayed on the corresponding terminal.

[0056] In this embodiment, monitoring parameters such as peak flow rate, average flow rate, minimum flow rate and vascular resistance index are displayed on the corresponding terminal, which can make real-time judgment and evaluation of the patient, prevent ischemic and hypoxic injuries, and improve the human-computer interaction experience.

[0057] Acquire a real-time carotid ultrasound image; perform feature extraction on the real-time carotid ultrasound image to obtain real-time image features of the real-time carotid ultrasound image; perform real-time positioning of the carotid blood vessels in the real-time carotid ultrasound image based on the real-time image features to obtain real-time positioning results of the carotid blood vessels. In the present application, by acquiring a real-time carotid ultrasound image and performing feature extraction on the real-time carotid ultrasound image, the carotid blood vessels in the real-time carotid ultrasound image are positioned in real time to obtain real-time positioning results of the carotid blood vessels, thereby monitoring the flow velocity of the carotid blood vessels in real time based on the real-time positioning results to obtain real-time monitoring results, so that the corresponding monitoring results can be fed back in real time to prevent the patient from having a poor prognosis and improve the efficiency of cardiopulmonary resuscitation.

[0058] See also Figure 3 , is a structural block diagram of a real-time positioning device for a carotid artery provided by an embodiment of the present invention. The above-mentioned quartz glass detection device is applied to the above-mentioned service end. For the convenience of explanation, only the part related to the embodiment of the present application is shown. Figure 3 The real-time positioning device 30 includes: an acquisition module 31, a feature extraction module 32, and a positioning module 33.

[0059] An acquisition module 31 is used to acquire a real-time carotid artery ultrasound image; A feature extraction module 32 is used to extract features from the real-time carotid artery ultrasound image to obtain real-time image features of the real-time carotid artery ultrasound image; The positioning module 33 is used to perform real-time positioning of the carotid artery in the real-time carotid artery ultrasound image according to the real-time image features, and obtain a real-time positioning result of the carotid artery.

[0060] Optionally, the positioning module 33 includes: A matching module is used to obtain preset image features, match the real-time image features with the preset image features, and obtain matched image features; The second positioning module is used to locate the carotid artery in the real-time carotid ultrasound image according to the position of the carotid artery in the image corresponding to the matched image features, and obtain the positioning result of the carotid artery.

[0061] Optionally, the real-time positioning device 30 further includes: A second acquisition module is used to acquire carotid artery ultrasound images in N scenes, where N is an integer greater than zero; A second feature extraction module is used to extract features of the carotid artery ultrasound image in any scene to obtain preset image features; The second positioning module is used to identify and locate the carotid artery according to preset image features to obtain a positioning result of the carotid artery.

[0062] Optionally, the real-time positioning device 30 further includes: The monitoring module is used to monitor the flow velocity of the carotid artery at the position corresponding to the real-time positioning result in real time according to the real-time positioning result, and obtain the flow velocity result of the carotid artery.

[0063] Optionally, the monitoring module includes: A calculation unit, used for calculating the peak flow velocity, mean flow velocity, minimum flow velocity and vascular resistance index of the carotid artery according to the flow velocity result; The display unit is used to display the peak flow rate, the average flow rate, the minimum flow rate and the vascular resistance index.

[0064] It should be noted that the information interaction, execution process and other contents between the above-mentioned modules are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0065] Figure 4 A schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Figure 4 As shown, the computer device of this embodiment includes: at least one processor ( Figure 4 Only one is shown), a memory, and a computer program stored in the memory and executable on at least one processor, wherein when the processor executes the computer program, the steps in any of the above-mentioned embodiments of the method for real-time positioning of the carotid artery are implemented.

[0066] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Figure 4 It is only an example of a computer device and does not constitute a limitation on the computer device. The terminal device may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include a network interface, a display screen, and an input device.

[0067] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0068] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments, it may also be an external storage device of the computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory may also include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of a computer program, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0069] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0070] The present application implements all or part of the processes in the above-mentioned embodiment method, and may also be completed through a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiment when executing the computer program product.

[0071] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0072] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0073] In the embodiments provided in the present application, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0074] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0075] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

[0076] See also Figure 5 , Figure 5 1 is a schematic diagram of the structure of an all-in-one machine 50 provided by an embodiment of the present invention, wherein the all-in-one machine is a cardiopulmonary cerebral resuscitation all-in-one machine, including a cardiopulmonary cerebral resuscitation machine body 51 and a micro-patch type ultrasonic probe 52, the cardiopulmonary cerebral resuscitation machine body 51 is connected to the micro-patch type ultrasonic probe 52, and is used to process the carotid artery ultrasonic image scanned by the micro-patch type ultrasonic probe 52; the micro-patch type ultrasonic probe 52 is used to scan the neck to obtain a real-time carotid artery ultrasonic image. The cardiopulmonary cerebral resuscitation machine body 51 has the same structure as the cardiopulmonary cerebral resuscitation machine body in the prior art.

[0077] The micro-patch ultrasound probe 52 includes at least 80 array elements, and the shell of the micro-patch ultrasound probe is tilted at a certain angle. The micro-patch ultrasound probe can be fixed to the neck of the individual being treated, and the soft colloid with extremely low acoustic impedance is used to achieve adhesion, and no coupling agent is required during use. Figure 6 , Figure 6 is a view of a miniature patch type ultrasound probe provided by one embodiment of the present invention, Figure 6 In the figure, Figure a is a top view of the miniature patch type ultrasound probe, Figure b is a bottom view of the miniature patch type ultrasound probe, Figure c is a front view of the miniature patch type ultrasound probe, and Figure d is a side view of the miniature patch type ultrasound probe.

[0078] In this embodiment, a miniature patch-type ultrasound probe can be fixed on the neck of the individual to be treated. The patch-type fixed ultrasound probe does not need to be held in hand. Due to its convenience, it can be operated by one or more clinicians. When it arrives at the individual to be treated, the compression installation can be quickly completed and the compression can be started. After being fixed on the neck of the individual to be treated, the real-time carotid artery ultrasound image obtained by scanning is positioned and monitored, and monitoring parameters such as flow rate peak, flow rate average, flow rate minimum and vascular resistance index are displayed on the corresponding terminal, which can be used as a reference for clinicians to adjust cardiopulmonary and cerebral resuscitation compression parameters and treatment strategies.

Claims

1. A real-time positioning method for a carotid artery, characterized in that: The real-time positioning method comprises: Acquire real-time carotid ultrasound images; Performing feature extraction on the real-time carotid artery ultrasound image to obtain real-time image features of the real-time carotid artery ultrasound image; The carotid artery blood vessel in the real-time carotid artery ultrasound image is positioned in real time according to the real-time image feature to obtain a real-time positioning result of the carotid artery blood vessel.

2. The real-time positioning method according to claim 1, characterized in that: The method of performing real-time positioning of the carotid artery in the real-time carotid artery ultrasound image according to the real-time image feature to obtain a real-time positioning result of the carotid artery includes: Acquire preset image features, and match the real-time image features with the preset image features to obtain matched image features; The carotid artery in the real-time carotid ultrasound image is located according to the position of the carotid artery in the image corresponding to the matched image features, and a positioning result of the carotid artery is obtained.

3. The real-time positioning method according to claim 2, characterized in that: Before obtaining the preset image features, the method further includes: Acquire carotid artery ultrasound images in N scenes, where N is an integer greater than zero; For the carotid artery ultrasound image in any scenario, extracting features of the carotid artery ultrasound image to obtain preset image features; The carotid artery is identified and located according to the preset image features to obtain a location result of the carotid artery.

4. The real-time positioning method according to claim 1, characterized in that: After real-time positioning of the carotid artery in the real-time carotid artery ultrasound image according to the real-time image feature and obtaining the real-time positioning result of the carotid artery, the method further includes: According to the real-time positioning result, the flow velocity of the carotid artery at the position corresponding to the real-time positioning result is monitored in real time to obtain the flow velocity result of the carotid artery.

5. The real-time positioning method according to claim 4, characterized in that: After obtaining the flow velocity result of the carotid artery, the method further comprises: According to the flow velocity results, the peak flow velocity, mean flow velocity, minimum flow velocity and vascular resistance index of the carotid artery are calculated; The flow velocity peak value, the flow velocity mean value, the flow velocity minimum value and the vascular resistance index are displayed.

6. A real-time positioning device for carotid artery, characterized in that: The real-time positioning device comprises: An acquisition module, used for acquiring a real-time carotid artery ultrasound image; A feature extraction module, used for extracting features from the real-time carotid artery ultrasound image to obtain real-time image features of the real-time carotid artery ultrasound image; The positioning module is used to perform real-time positioning of the carotid artery in the real-time carotid artery ultrasound image according to the real-time image features to obtain a real-time positioning result of the carotid artery.

7. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the real-time positioning method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the real-time positioning method according to any one of claims 1 to 5 is implemented.

9. An all-in-one machine, characterized in that: It includes a main body of a cardiopulmonary-cerebral resuscitation machine and a miniature patch-type ultrasonic probe, wherein the main body of the cardiopulmonary-cerebral resuscitation machine is connected to the miniature patch-type ultrasonic probe and is used to process the carotid artery ultrasonic image obtained by scanning the miniature patch-type ultrasonic probe; The miniature patch type ultrasound probe is used for scanning the neck to obtain a real-time carotid artery ultrasound image.

10. The all-in-one machine according to claim 9, characterized in that: The micro patch type ultrasonic probe comprises at least 80 array elements, and the shell of the micro patch type ultrasonic probe is inclined at a certain angle.

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