CN110648328B - Aorta replacement surgery remote intelligent screening system based on 5G - Google Patents
Aorta replacement surgery remote intelligent screening system based on 5G Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/51—Indexing; Data structures therefor; Storage structures
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H30/00—ICT specially adapted for the handling or processing of medical images
- G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
- G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10132—Ultrasound image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30101—Blood vessel; Artery; Vein; Vascular
Abstract
The embodiment of the invention discloses a remote intelligent screening system for aortic replacement surgery based on 5G, which relates to the technical field of data processing, and comprises: the imaging device is used for acquiring image data of a target object; the 5G ultrasonic networking system is used for transmitting the image data to a remote cloud computing center through a 5G network; an aortic stenosis measurement algorithm module, which acquires image data acquired by the imaging device from the remote cloud computing center; a case database module storing image data of a plurality of target objects; and the cloud computing scheduling system is used for performing data scheduling on the 5G ultrasonic networking system, the aortic stenosis measuring algorithm module and the case database module. By the scheme of the embodiment of the invention, the efficiency of the aortic replacement operation can be effectively improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a remote intelligent screening system for aortic replacement surgery based on 5G.
Background
Transcatheter aortic valve replacement (TAVI), is now beginning to grow in popularity. Since the treatment by aortic valve replacement surgery requires multidisciplinary cooperation. But most of the primary doctors have insufficient technology and lack related training, and the aorta replacement cases are difficult to identify independently.
Aortic stenosis is a common structural heart disease, which occurs mostly in the elderly. Traditional treatments have dominated surgical replacement of prosthetic valves. However, due to the physical condition of the elderly, about 1/3 of the patients' physical conditions are not able to bear the risk of open chest surgery. In recent years, the technology TAVR for minimally invasive catheterization has been greatly developed, with less surgical trauma and low risk. However, the TAVI operation needs the cooperation of multiple subjects, the technology is complex, the operation is limited to the top hospitals at present, and the operation is difficult to be carried out in the primary hospitals. In addition, doctors in primary hospitals have little operation management, and the TAVI operation does not know that the patient is not in harmony with the primary operation and cannot correctly identify the patient in the TAVI operation. The patient can not be found in the operation center, and the patient can not know the problem that the patient can be cured.
Disclosure of Invention
In view of the above, embodiments of the present invention provide a remote intelligent screening system for aortic replacement surgery based on 5G, which at least partially solves the problems in the prior art.
The embodiment of the invention provides a remote intelligent screening system for aortic replacement surgery based on 5G, which comprises:
the imaging device is used for acquiring image data of a target object;
the 5G ultrasonic networking system is used for transmitting the image data to a remote cloud computing center through a 5G network;
the aortic stenosis measurement algorithm module acquires image data acquired by the imaging equipment from the remote cloud computing center, and performs target identification and calculation on the image data;
the case data base module stores image data of a plurality of target objects so as to provide data support when the aortic stenosis measurement algorithm module performs data calculation;
and the cloud computing scheduling system is used for performing data scheduling on the 5G ultrasonic networking system, the aortic stenosis measuring algorithm module and the case database module.
According to a specific implementation of the embodiments of the present invention, the imaging device comprises a phase array probe of at least 128 elements, capable of operating at a frequency of 2-5Mhz, and capable of using B-mode imaging.
According to a specific implementation manner of the embodiment of the invention, the image device transmits the image data out to the 5G ultrasonic networking system in a DICOM format.
According to a specific implementation manner of the embodiment of the invention, after being received, the image data is divided into two parts of meta information and image data through a 5G ultrasound networking system, wherein the meta information in the image data is transmitted through a 5G command channel, and the actual image data part in the image data is transmitted through a 5G data channel.
According to a specific implementation manner of the embodiment of the invention, the cloud computing platform receives information sent by the 5G ultrasonic networking system through two different endpoints, wherein metadata information in the image data flows into the Internet of things platform at the cloud end through an Internet of things protocol.
According to a specific implementation manner of the embodiment of the invention, the aortic stenosis measurement algorithm module automatically identifies the positions of the anterior valve and the posterior valve, and automatically measures the distance between the two points as the calculated size of the orifice.
According to a specific implementation mode of the embodiment of the invention, the case database module comprises a database, data in the database adopts a read-write separation structure, a storage engine is used for high-speed data writing, a read engine is used for high-performance data searching, original data is stored in an object storage part, and meta information is stored in an unstructured database.
According to a specific implementation manner of the embodiment of the invention, the database adopts a distributed engine and runs on a cloud platform, and the data storage engine is based on an unstructured distributed storage engine.
According to a specific implementation manner of the embodiment of the invention, the cloud computing scheduling system comprises a physical network IOT manager, an image data memory, a message queue for cancellation, a write-in database, a message queue for outflow and a message database, and is composed of a push service and a data synchronization service and used for performing image processing service.
The cloud computing system for aortic valve disease screening developed by the invention and popularized in primary hospitals utilizes 5G and artificial intelligence technology and automatically measures the aortic valve based on ultrasonic images. And grading the precise operation according to the measurement result, and screening the patient needing aortic valve replacement.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of a remote intelligent screening system for aortic replacement surgery based on 5G according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating a result of target detection performed on an ultrasound image according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the invention provides a 5G-based remote intelligent screening system for aortic replacement surgery, which comprises an imaging device, a 5G ultrasound networking system, an aortic stenosis measurement algorithm module, a case database module and a cloud computing scheduling system.
The invention adopts an ultrasonic imaging device as an imaging device for base layer screening. The ultrasonic is real-time, cheap, easy to move and safe, so that the ultrasonic is easier to popularize in the basic level compared with other equipment. The requirement for an ultrasound system is an array probe of at least 128 elements, at a frequency of 2-5Mhz, which can be imaged using the B-mode. The imaging depth is 17-20cm. Of course, a professional cardioscope or a portable ultrasound device with CMTU may also be used.
The 5G ultrasonic networking system carries out communication connection with each subassembly in the remote intelligent screening system of aortic replacement operation based on 5G through signal connection circuit, and 5G provides high-speed data transmission performance and high bandwidth performance, can reach the high in the clouds in real time with the ultrasonic examination of hospital. The cloud can send the examination result back to the ultrasonic examination terminal of the hospital in real time. As a specific example, the response time of the system based on 5G signal transmission can be 2-3 seconds, and the system is used for data processing and transmission, so that the patient data is immediately required
The ultrasonic machine equipment is connected with a remote cloud computing center through 5G equipment. The ultrasonic equipment transmits data in a DICOM format, and after the ultrasonic data (image data) is received, the ultrasonic data is divided into two parts of meta information and image data through 5G equipment. The meta information in the ultrasonic data is transmitted through a 5G command channel, the actual image data part in the ultrasonic data is transmitted through a 5G data channel, and the reliability of ultrasonic data transmission is ensured through a dual-channel signal transmission mechanism.
A remote cloud computing platform receives information at two different endpoints. The ultrasonic image metadata information flows into an Internet of things platform at the cloud end through an Internet of things protocol (MTQQ). And the rear end of the physical network platform is accessed into an unstructured database for storing data. Meanwhile, the metadata information is also pushed into a cancellation queue for triggering the image processing service at the back end. The data part of the ultrasonic data is accessed to the object storage part, and the object storage is directly connected with the CDN of the 5G network.
The data outflow part is also divided into two channels, the metadata part interacts metadata information with the client through the WebSocket, and the image data part is connected with the client in a CDN (content distribution network) mode.
The aortic stenosis measurement algorithm module stores an automatic measurement algorithm of the aortic valve, and can be an automatic analysis system running on a plurality of GPU servers. As one implementation, each GPU server is configured as a GPU graphics card of NVidia. The running video memory is 8G, and the system running memory is 16G. The system is based on tensorflow. The inside is isolated with docker.
The aortic valve measures more strongly than the (paracostal major axis PLAX) major axis, mainly the anterior and posterior aortic open valve roots. The automatic measurement algorithm automatically identifies the position of the anterior valve, the posterior valve and then automatically measures the distance between the two points as the size of the calculated valve orifice.
The automated measurement algorithm is divided into two parts, the first part is used for target detection in order to find a target region of the aortic orifice (see fig. 2), which is a square, for example, the size of the square may be: 128mm by 128mm. The second part of the automated measurement algorithm is used to perform keypoint detection to facilitate accurate positioning of the anterior and posterior valves within the target region.
As an embodiment, the target detection algorithm can be based on CenterNet, that is, the central line of the root line of the anterior valve and the posterior valve is found first by using the method of heatmap regression, and the range of the square of the detected target is 128 x 128mm.
The roots of the anterior and posterior lobes can be detected with high-precision marker points using the architecture of HeatMap regression. For each anatomical landmark point. The system outputs three values:
the first value is a distance value to the target;
the second value is the x component of the direction vector to the target;
the third value is the y-component of the direction vector to the target.
In this way, the two targets output data of a 6-layer structure in total.
The case database module is used for designing a case database, and ultrasonic image information and real-time image flow information of all patients in the cloud are stored in the case database.
The data in the database adopts a read-write separation structure, the storage engine is used for high-speed data writing, and the read engine is used for high-performance data searching. The original data is stored in the object storage part, and the meta information is stored in the unstructured database.
The data structure in which the ultrasound image can be searched comprises 4 parts: patient, exam, sequence and ID. The relationship is that multiple exams can be made for each patient, each exam can have multiple sequences, each sequence having multiple pictures. The writing engine takes the sequence and the ID as the Key of the database, and the database is not indexed.
The database also includes a search engine, which uses the patient as Key and the study + sequence + ID as index. Meanwhile, a plurality of secondary indexes can be arranged in the database, and data synchronization is carried out by the server before the read-write engine.
The database adopts a distributed engine and runs on a cloud platform. The data storage engine is based on an unstructured distributed storage engine. Such as BigTable, HBase, cassandra, or dynamodb. And the data synchronization self-establishes a service server or uses a serverless computing mode.
The cloud computing scheduling system is a scheduling platform at the cloud end, and the whole system comprises: IOT manager of physical network, image data memory, message queue, write-in database, message queue and message database. The push service and the data synchronization service. For performing image processing services.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on differences from other embodiments.
In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference may be made to the partial description of the method embodiment for relevant points.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Further, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof.
In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (2)
1. A remote intelligent screening system for aortic replacement surgery based on 5G is characterized by comprising:
the ultrasonic equipment is connected with a remote cloud computing center through 5G equipment, the ultrasonic equipment transmits data in a DICOM format, after the ultrasonic data are received, the ultrasonic data are divided into two parts of meta information and image data through the 5G equipment, the meta information in the ultrasonic data is transmitted through a 5G command channel, and the actual image data part in the ultrasonic data is transmitted through a 5G data channel;
the remote cloud computing platform receives information and ultrasonic image metadata information through two different endpoints, the information and the ultrasonic image metadata information flow into the Internet of things platform at the cloud end through an Internet of things protocol, the rear end of the physical network platform is connected into an unstructured database for storing data, meanwhile, the metadata information is also pushed into a cancellation queue for triggering image processing service at the rear end, the data part of the ultrasonic data is connected into an image storage part, and the image storage part is directly connected with a CDN (content distribution network) of a 5G network;
the data streaming module is divided into two channels, a metadata part interacts metadata information with the client through a WebSocket, and an image data part is connected with the client in a CDN (content distribution network) manner;
the aortic stenosis measurement algorithm module is an automatic analysis system which runs on a plurality of GPU servers and stores an automatic measurement algorithm of the aortic valve;
the measurement of the aortic valve is more than that of the long axis, the anterior valve root and the posterior valve root of the aortic opening are measured, the position of the anterior valve and the posterior valve is automatically identified by an automatic measurement algorithm, and then the distance between the two points is automatically measured to serve as the calculated size of the orifice;
the automatic measurement algorithm is divided into two parts, wherein the first part is used for carrying out target detection so as to find out a target area of an aortic valve orifice, the target area is a square, and the second part is used for carrying out key point detection so as to accurately position the positions of a front valve and a rear valve in the target area;
the target detection algorithm is based on CenterNet, the central line of the root connecting line of the front valve and the back valve is found firstly by utilizing a HeatMap regression method, the range of the square of the detected target is 128 multiplied by 128mm, the root of the front valve and the root of the back valve are subjected to high-precision marker point detection by using a HeatMap regression structure, and for each anatomical marker point, the system outputs three values: the first value is a distance value to the target; the second value is the x-component of the direction vector to the target; the third value is the y component of the direction vector to the target, and the two targets output data of a 6-layer structure in total;
the case database module is used for designing a case database, ultrasonic image information and real-time image flow information of all patients in a cloud are stored in the case database, data in the database adopt a read-write separation structure, a storage engine is used for high-speed data writing, the read engine is used for high-performance data searching, original data are stored in an object storage part, and meta information is stored in an unstructured database;
the data structure in which the ultrasound image can be searched comprises 4 parts: the system comprises patients, examinations, sequences and IDs, wherein the relations of the patients, the examinations, the sequences and the IDs are that each patient is subjected to a plurality of examinations, each examination has a plurality of sequences, each sequence has a plurality of pictures, a writing engine takes the sequences and the IDs as keys of a database, and the database is not indexed;
the database also comprises a search engine, the search engine takes a patient as Key and study + sequence + ID as index, meanwhile, the database has a plurality of secondary indexes, and a server carries out data synchronization before the read-write engine;
the database adopts a distributed engine and runs on a cloud platform, the data storage engine is based on an unstructured distributed storage engine, and a data synchronization self-built service server or a serverless service computing mode is used;
the cloud computing scheduling system is a scheduling platform of a cloud, and comprises: the system comprises a physical network IOT manager, an image data memory, an incoming message queue, a write-in database, an outgoing message queue, a message database, a push service and a data synchronization service, and is used for performing image processing service.
2. The system of claim 1, wherein:
the ultrasound machine apparatus includes an at least 128 element phased array probe capable of operating at a frequency of 2-5Mhz, and capable of using B-mode imaging.
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