CN112819785A - Liver haemodynamic detection device - Google Patents

Liver haemodynamic detection device Download PDF

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CN112819785A
CN112819785A CN202110135823.5A CN202110135823A CN112819785A CN 112819785 A CN112819785 A CN 112819785A CN 202110135823 A CN202110135823 A CN 202110135823A CN 112819785 A CN112819785 A CN 112819785A
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CN112819785B (en
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陈皓
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Beijing Jingkang Technology Co ltd
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Abstract

The application provides a liver hemodynamic detection device, which comprises an image acquisition module, a data processing module and a data processing module, wherein the image acquisition module is used for acquiring a hepatic vein detection picture and a portal vein detection picture; the waveform boundary determining module is used for converting the pictures into binary images and taking the longest straight line in each picture as a waveform boundary; the waveform identification module cuts the hepatic vein detection picture and the portal vein detection picture, and determines the upper waveform or the lower waveform of the waveform picture by taking the average value of the gray levels of the pixel points of all straight lines lower than a threshold value as a judgment standard; a frequency shift calculation module, which determines the frequency shift of the oscillogram in the hepatic vein detection picture relative to the oscillogram in the portal vein detection picture in the upper waveform or the lower waveform; and the liver fibrosis degree determining module is used for determining the liver fibrosis degree according to the frequency shift and a preset liver fibrosis index comparison table, and the liver fibrosis index comparison table records the relationship between the frequency shift of different hepatic veins relative to portal veins and the liver fibrosis degree.

Description

Liver haemodynamic detection device
Technical Field
The application belongs to the technical field of data processing, in particular to a liver haemodynamic detection device.
Background
Liver fibrosis is caused by chronic injury to the liver, including hepatitis b and c, alcoholic liver disease, non-alcoholic fatty liver disease (NAFLD), and autoimmune hepatitis. With the progress of hepatic fibrosis, excessive accumulation of extracellular matrix proteins leads to increased liver hardness, resulting in liver cirrhosis, liver failure, and liver cancer.
The gold standard for detecting liver fibrosis has been the use of invasive needle biopsies and relies on the pathologist to visually inspect the tissue images. Problems with needle biopsies include: 1) low accuracy due to large sampling errors and pathologist interpretation variability; 2) pain and potential medical risks associated with invasive surgery (e.g., major bleeding). There is also a non-invasive technique, which compares various frequency patterns from different veins (portal vein, hepatic left vein, hepatic middle vein and hepatic right vein) in the liver, and determines the degree of liver fibrosis by the mapping relationship between the frequency shift and the liver fiber index.
Disclosure of Invention
In order to solve at least one of the above technical problems, the present application provides a liver hemodynamic detection apparatus, which is applied to a server side, and the detection apparatus includes:
the system comprises an image acquisition module, a database module and a database module, wherein the image acquisition module is used for acquiring a hepatic vein detection picture and a portal vein detection picture uploaded by a client, the hepatic vein detection picture comprises any one of a left hepatic vein, a middle hepatic vein or a right hepatic vein, and the hepatic vein detection picture and the portal vein detection picture are provided with oscillograms; the waveform boundary determining module is used for converting the hepatic vein detection picture and the portal vein detection picture into binary images and taking the longest straight line in each picture as a waveform boundary based on Hough straight line detection; the waveform identification module is used for cutting the hepatic vein detection picture and the portal vein detection picture by a plurality of straight lines parallel to the waveform boundary, and determining the upper waveform or the lower waveform of the waveform picture by taking the average value of the pixel point gray level of each straight line lower than a threshold value as a judgment standard; a frequency shift calculation module, configured to determine, in the upper waveform or the lower waveform, a frequency shift of a waveform pattern in the hepatic vein detection picture relative to a waveform pattern in the portal vein detection picture; and the liver fibrosis degree determining module is used for determining the liver fibrosis degree according to the frequency shift and a preset liver fibrosis index comparison table, and the liver fibrosis index comparison table records the relationship between the frequency shift of different hepatic veins relative to portal veins and the liver fibrosis degree.
Preferably, the image acquisition module further includes: the system comprises a patient history table acquisition unit, a picture analysis unit and a picture analysis unit, wherein the patient history table acquisition unit is used for acquiring a PACS system interface screenshot uploaded by a client before acquiring a detection picture, and the interface screenshot comprises a patient case table; a number identification unit for extracting a patient number or a detection number from the patient history table; and the image receiving unit is used for sending the patient number or the detection number to a client and receiving the hepatic vein detection picture and the portal vein detection picture which are searched by the client and are associated with the patient number or the detection number.
Preferably, the system further comprises a storage module, configured to feed back the liver fibrosis degree to the client after determining the liver fibrosis degree, and locally store the liver fibrosis degree data.
Preferably, the frequency shift calculation module includes: an offset acquisition unit, configured to determine a plurality of offsets of the positions of peaks or troughs of the waveform map in the hepatic vein detection picture with respect to the positions of corresponding peaks or corresponding troughs of the waveform map in the portal vein detection picture; and a frequency shift mean solving unit for calculating the frequency shift according to the plurality of offset amounts.
Preferably, calculating the frequency shift according to a plurality of the offset amounts includes: and calculating the average value of a plurality of offset values as the frequency shift.
Preferably, calculating the frequency shift from a plurality of the offsets comprises:
K＝(a1k1+a2k2+……+ankn)/n；
wherein k is1、k2、knIs n offsets, a1、a2、anThe method is characterized in that a plurality of parameters which are dispersed within a set data range, such as 0.9-1, are used for representing the weight of each offset according to the deviation value of the amplitude corresponding to each peak in the hepatic vein detection picture relative to the average amplitude.
Preferably, the system further comprises a hepatic vein type identification module, configured to determine a hepatic vein type of the hepatic vein detection picture uploaded by the client through text identification before determining the hepatic fibrosis degree, and call a hepatic fibrosis index comparison table corresponding to the hepatic vein type in the hepatic fibrosis degree determination module.
The method and the device can automatically acquire the ultrasonic image in the standard format transmitted by the medical image inspection equipment, automatically process waveforms in the image, give the hepatic fibrosis degree based on the frequency change value of the flow velocity of the liver blood, and assist doctors in diagnosing the hepatic fibrosis degree.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the liver hemodynamic detection method of the present application.
Figure 2 shows a schematic diagram comparing various frequency patterns from different veins in the liver.
Fig. 3 is a schematic structural diagram of a computer device suitable for implementing a terminal or a server according to an embodiment of the present application.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the accompanying drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all embodiments of the present application. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application, and should not be construed as limiting the present application. All other embodiments obtained by a person of ordinary skill in the art without any inventive work based on the embodiments in the present application are within the scope of protection of the present application. Embodiments of the present application will be described in detail below with reference to the drawings.
According to a first aspect of the present application, there is provided a liver hemodynamic detection method, comprising:
step S1, obtaining a hepatic vein detection picture and a portal vein detection picture uploaded by the client, wherein the hepatic vein detection picture comprises any one of a left hepatic vein, a middle hepatic vein or a right hepatic vein, and the hepatic vein detection picture and the portal vein detection picture are provided with oscillograms.
In this embodiment, the hepatic vein detection picture and the portal vein detection picture uploaded by the client are mainly stored in a temporary folder of the client, and are uploaded in advance by the doppler ultrasound apparatus and stored in the client.
The image uploaded by the doppler ultrasound apparatus into the client in this step S1 contains portal vein PV and any hepatic vein, such as left hepatic vein LHV, middle hepatic vein MHV, or right hepatic vein RHV. Any kind of comparison between hepatic vein and portal vein PV can be used as the basis for determining the degree of hepatic fibrosis.
In some optional embodiments, before obtaining the hepatic vein detection picture and the portal vein detection picture uploaded by the client, the method includes:
and step S11, before the detection picture is obtained, obtaining a PACS system interface screenshot uploaded by the client, wherein the interface screenshot comprises a case table.
And step S12, extracting the patient number or the detection number from the patient history table.
And step S13, sending the patient number or the detection number to a client, and receiving the hepatic vein detection picture and the portal vein detection picture which are searched by the client and are associated with the patient number or the detection number.
The client side of the method is connected with a PACS system, a program for carrying out screenshot on the interface of the PACS system is arranged in the client side, and in step S11, after the client side starts the corresponding program, screenshot is carried out on the current case interface of the PACS system.
The PACS system is an abbreviation of Picture Archiving and Communication Systems, meaning image Archiving and Communication Systems. The system is applied to a hospital image department, and mainly aims to store various medical images (including images generated by equipment such as nuclear magnetism, CT, ultrasound, various X-ray machines, various infrared instruments, microscopes and the like) generated in daily life in a digital mode through various interfaces (analog, DICOM and network), can be quickly called back for use under certain authorization when needed, and is added with auxiliary diagnosis management functions. It has important roles in transmitting data and organizing and storing data among various image devices.
The liver hemodynamic detection software is divided into a server side and a client side based on a standard DICOM 3.0 protocol. The liver hemodynamic detection method described above is executed at a server, where the server receives ultrasound images in a standard format or in a format such as JPG, PNG, etc. transmitted by other medical image examination devices complying with the standard DICOM 3.0 protocol (for example, in step S1 of the present application, an image complying with the standard is uploaded to a client by a doppler ultrasound apparatus, and then the image is uploaded to the server by the client), and stores the medical images in a designated directory. On the other hand, the client provides basic information of the patient to be uploaded based on the step S11, the server performs integration processing on the case and the image information, the processing result can be returned to the client, the client displays the processing result of the medical image file, and the doctor is assisted in diagnosing the liver fibrosis degree through the frequency change value of the liver blood flow rate.
In step S11, after the client opens the PACS system, the system automatically intercepts the current interface of the PACS system and uploads the screenshot to the server through the screenshot plug-in provided by the client, and after the server receives the screenshot uploaded by the client, in step S12, the server detects the text on the screenshot through software, identifies the patient number and the detection number, and returns the two numbers to the client, or calls a retrieval tool of the client to directly read the hepatic vein detection picture and portal vein detection picture associated with the patient number or the detection number. Prior to this embodiment, the method further comprises associating information such as patient number, name, etc. with the storage location of each vein picture detected.
Step S2, converting the hepatic vein detection picture and portal vein detection picture into binary images, and using the longest straight line in each picture as a waveform boundary based on hough straight line detection.
And step S3, cutting the hepatic vein detection picture and the portal vein detection picture by a plurality of straight lines parallel to the boundary of the waveform, and determining the upper waveform or the lower waveform of the waveform by taking the average value of the pixel point gray levels of all the straight lines lower than a threshold value as a judgment standard.
In this embodiment, the image is converted into a binary image, and then the longest straight line in the image is detected by hough line detection, which is the upper and lower boundary of the waveform that we will use finally. Step S3 is to determine the upper and lower bounds of the waveform. Taking the above boundary as an example, the waveform oscillates from left to right and up and down, and then the average value of the gray levels of the pixel points in each row is greater than a certain threshold, and obviously, the average value at the peak or the trough is a little smaller. While moving away from the peak, the average value becomes smaller (because no line crosses the row of pixels). Then, a threshold value n can be set, the average value of the pixel points is calculated from the straight line to each row, the position of the row with the average value smaller than n is taken, the image between the position and the straight line is the upper waveform, and the lower waveform can be determined in the same way.
Step S4, in the upper waveform or the lower waveform, determining a frequency shift of the waveform pattern in the hepatic vein detection picture relative to the waveform pattern in the portal vein detection picture.
In some optional embodiments, determining the frequency shift comprises:
step S41, determining a plurality of offsets of the positions of the peaks or the troughs of the waveform map in the hepatic vein detection picture relative to the positions of the corresponding peaks or the corresponding troughs of the waveform map in the portal vein detection picture.
Step S42 is to obtain an average value of the plurality of offset amounts as the frequency shift.
In an alternative embodiment, the plurality of offsets may be sorted, and the larger of the plurality of offsets may be averaged to obtain the frequency shift.
In alternative embodiments, the frequency shift amount may be calculated by using a plurality of continuous peaks, the offset amount may be calculated by using a plurality of continuous valleys, or the offset amount may be calculated by using a plurality of alternating peaks and valleys.
In this embodiment, the peak and the trough may be determined by using the relationship between the average value of the gray levels of the pixel points and the set threshold. For the hepatic vein waveform image with a plurality of peaks and the portal vein waveform image with a plurality of peaks, the corresponding relationship between the peaks can be determined by setting the interval range, as shown in fig. 2, in the conventional hepatic fiber waveform, the interval between the peaks is about 0.1Hz, and the frequency shift caused by hepatic fiber is about within 0.05Hz, therefore, by taking the frequency of a certain peak m in the portal vein waveform image as a reference, a peak n in the hepatic vein waveform image which is searched for in the interval of 0.05Hz around the reference is set, and the peak n is the peak corresponding to the peak m.
It should be further noted that, in step S42, the frequency shift may also be calculated according to the weight of each offset, and the weight may be determined by the magnitude of the deviation value between each peak and the average value of the peak in the waveform diagram, which is described in detail in the subsequent frequency shift calculation module.
And step S5, determining the degree of hepatic fibrosis according to the frequency shift and a preset hepatic fibrosis index comparison table, wherein the hepatic fibrosis index comparison table records the relationship between the frequency shift of different hepatic veins relative to portal veins and the degree of hepatic fibrosis.
As shown in fig. 2, a graph comparing various frequency patterns from different veins in the liver (portal vein PV, three hepatic veins LHV, MHV, RHV) is shown, and it can be seen that the hepatic veins are shifted from the portal vein, and the shift amount has a certain correspondence with the degree of liver fibrosis, which is described in the liver fibrosis index comparison table, for example, for the left hepatic vein LHV, the presence of a frequency shift between about 0.0068Hz and about 0.0200Hz is indicative of stage 1 liver fibrosis; the presence of a frequency shift between about 0.0200Hz to about 0.0251Hz is indicative of stage 2 liver fibrosis; the presence of a frequency shift between about 0.0251Hz to about 0.0401Hz is indicative of stage 3 liver fibrosis; the presence of a frequency shift greater than about 0.0401Hz is indicative of stage 4 liver fibrosis (stage 4 liver fibrosis is also known as cirrhosis). In some embodiments, a frequency shift of greater than 0.0401Hz in the frequency of blood flow in the hepatic vein compared to the frequency of blood flow in the portal vein is indicative of stage 4 liver fibrosis.
Wherein the term "stage 1" refers to fibrosis-dilated liver fibrosis that occurs in certain portal vein areas, but without the presence of a short fibrotic septum, as with stage 1 of Metavir fibrosis score; the term "stage 2" refers to fibrosis of the liver where fibrosis expansion occurs in most portal areas, with a short fibrotic septum present, as in stage 2 of Metavir fibrosis score; the term "stage 3" refers to liver fibrosis with fibrotic expansion in most portal vein areas, where there is bridging between portal veins, as in stage 3 of Metavir fibrosis score; the term "stage 4" refers to progression of liver fibrosis to cirrhosis, the same stage 4 as the Metavir fibrosis score.
In some alternative embodiments, determining the extent of liver fibrosis comprises, prior to: and determining the hepatic vein type of the hepatic vein detection picture uploaded by the client through character recognition, and calling a hepatic fibrosis index comparison table corresponding to the hepatic vein type when determining the hepatic fibrosis degree.
In some alternative embodiments, after determining the degree of liver fibrosis, further comprising: and step S6, feeding back the hepatic fibrosis degree to the client, displaying the result by the client, and locally storing the hepatic fibrosis degree data at the server.
In this embodiment, the server calls a program to calculate the detected image, stores the data in the local specified directory, and returns the calculation result to the client. The customer opens a web page to present the results of the analysis.
The method and the device can automatically acquire the ultrasonic image in the standard format transmitted by the medical image inspection equipment, automatically process waveforms in the image, give the hepatic fibrosis degree based on the frequency change value of the flow velocity of the liver blood, and assist doctors in diagnosing the hepatic fibrosis degree.
The second aspect of the present application provides a liver hemodynamic detection apparatus corresponding to the above method, which mainly includes:
the system comprises an image acquisition module, a database module and a database module, wherein the image acquisition module is used for acquiring a hepatic vein detection picture and a portal vein detection picture uploaded by a client, the hepatic vein detection picture comprises any one of a left hepatic vein, a middle hepatic vein or a right hepatic vein, and the hepatic vein detection picture and the portal vein detection picture are provided with oscillograms; the waveform boundary determining module is used for converting the hepatic vein detection picture and the portal vein detection picture into binary images and taking the longest straight line in each picture as a waveform boundary based on Hough straight line detection; the waveform identification module is used for cutting the hepatic vein detection picture and the portal vein detection picture by a plurality of straight lines parallel to the waveform boundary, and determining the upper waveform or the lower waveform of the waveform picture by taking the average value of the pixel point gray level of each straight line lower than a threshold value as a judgment standard; a frequency shift calculation module, configured to determine, in the upper waveform or the lower waveform, a frequency shift of a waveform pattern in the hepatic vein detection picture relative to a waveform pattern in the portal vein detection picture; and the liver fibrosis degree determining module is used for determining the liver fibrosis degree according to the frequency shift and a preset liver fibrosis index comparison table, and the liver fibrosis index comparison table records the relationship between the frequency shift of different hepatic veins relative to portal veins and the liver fibrosis degree.
In some optional embodiments, the image acquisition module further comprises: the system comprises a patient history table acquisition unit, a picture analysis unit and a picture analysis unit, wherein the patient history table acquisition unit is used for acquiring a PACS system interface screenshot uploaded by a client before acquiring a detection picture, and the interface screenshot comprises a patient case table; a number identification unit for extracting a patient number or a detection number from the patient history table; and the image receiving unit is used for sending the patient number or the detection number to a client and receiving the hepatic vein detection picture and the portal vein detection picture which are searched by the client and are associated with the patient number or the detection number.
In some optional embodiments, the system further comprises a storage module, configured to, after determining the liver fibrosis degree, feed back the liver fibrosis degree to the client, and locally store the liver fibrosis degree data.
In some optional embodiments, the frequency shift calculation module comprises: an offset acquisition unit, configured to determine a plurality of offsets of the positions of peaks or troughs of the waveform map in the hepatic vein detection picture with respect to the positions of corresponding peaks or corresponding troughs of the waveform map in the portal vein detection picture; and a frequency shift mean solving unit for calculating the frequency shift according to the plurality of offset amounts.
In some optional embodiments, calculating the frequency shift from a plurality of the offsets comprises: and calculating the average value of a plurality of offset values as the frequency shift.
In some optional embodiments, calculating the frequency shift from a plurality of the offsets comprises:
K＝(a1k1+a2k2+……+ankn)/n；
wherein k is1、k2、knIs n offsets, a1、a2、anThe plurality of parameters are discrete within a set data range according to the deviation value of the amplitude corresponding to each peak in the hepatic vein detection picture relative to the average amplitude, and are used for representing the weight of each deviation amount.
Taking fig. 2 as an example for illustration, the broken line in fig. 2 contains 16 peaks in total, the average value of the peaks is about 1500, the maximum value is about 6500, and the minimum value is about 1000, then the deviation value of each peak from the average value (the deviation value is different from the deviation value, the deviation value refers to the peak deviation in the same oscillogram, and the deviation value refers to the deviation of the corresponding peaks of the two oscillograms) is about 500-5000, and then data dispersion is performed in the set data range, such as 0.9-1, according to the deviation value, and if only containing 500, 1000, 1500 three deviation values, three corresponding weight values a dispersed in 0.9-1 are assumed to be contained1、a2、a3Is 0.975, 0.95 and 0.925. It should be noted that, in the present embodiment, the stability of the acquired waveform pattern is mainly considered, and when the waveform tends to be stable around the average value, the offset k should be set for each offset1、k2、knIn order to give approximately the same weight, when the deviation from the average value is likely to cause inaccurate frequency shift, the weight should be reduced, for example, the weight corresponding to the deviation 1500 is at least 0.925, and the weight corresponding to the deviation 500 is at most 0.975.
It should be further noted that the set data range is determined empirically or statistically so as to more accurately reflect the relationship between the frequency shift and the liver fibrosis, for example, the set data range may be 0.9-1 or 0.8-1, and this embodiment is only an example and is not limited thereto.
In some optional embodiments, the system further includes a hepatic vein type identification module, configured to determine, before determining the hepatic fibrosis degree, a hepatic vein type of a hepatic vein detection picture uploaded by the client through text recognition, and in the hepatic fibrosis degree determination module, call a hepatic fibrosis index comparison table corresponding to the hepatic vein type.
According to a third aspect of the present application, a computer system comprises a processor, a memory and a computer program stored on the memory and executable on the processor, the processor executing the computer program for implementing the liver hemodynamic detection method as described above.
According to a fourth aspect of the present application, a readable storage medium stores a computer program which, when executed by a processor, is adapted to carry out the liver hemodynamic detection method described above.
Referring now to FIG. 3, there is shown a schematic block diagram of a computer device 800 suitable for use in implementing embodiments of the present application. The computer device shown in fig. 3 is only an example, and should not bring any limitation to the function and the scope of use of the embodiments of the present application.
As shown in fig. 3, the computer apparatus 800 includes a Central Processing Unit (CPU)801 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data necessary for the operation of the apparatus 800 are also stored. The CPU801, ROM802, and RAM803 are connected to each other via a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, a mouse, and the like; an output section 807 including a signal such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. A drive 810 is also connected to the I/O interface 805 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted on the storage section 808 as necessary.
In particular, according to embodiments of the present application, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section 809 and/or installed from the removable medium 811. The computer program performs the above-described functions defined in the method of the present application when executed by the Central Processing Unit (CPU) 801. It should be noted that the computer storage media of the present application can be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules or units described in the embodiments of the present application may be implemented by software or hardware. The modules or units described may also be provided in a processor, the names of which in some cases do not constitute a limitation of the module or unit itself.
The computer-readable storage medium provided by the fourth aspect of the present application may be included in the apparatus described in the above embodiment; or may be present separately and not assembled into the device. The computer readable storage medium carries one or more programs which, when executed by the apparatus, process data in the manner described above.
The above description is only for the specific embodiments of the present application, but the scope of the present application 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 application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (7)

1. The utility model provides a liver haemodynamic detection device, is applied to the server side, its characterized in that includes:
the system comprises an image acquisition module, a database module and a database module, wherein the image acquisition module is used for acquiring a hepatic vein detection picture and a portal vein detection picture uploaded by a client, the hepatic vein detection picture comprises any one of a left hepatic vein, a middle hepatic vein or a right hepatic vein, and the hepatic vein detection picture and the portal vein detection picture are provided with oscillograms;
the waveform boundary determining module is used for converting the hepatic vein detection picture and the portal vein detection picture into binary images and taking the longest straight line in each picture as a waveform boundary based on Hough straight line detection;
the waveform identification module is used for cutting the hepatic vein detection picture and the portal vein detection picture by a plurality of straight lines parallel to the waveform boundary, and determining the upper waveform or the lower waveform of the waveform picture by taking the average value of the pixel point gray level of each straight line lower than a threshold value as a judgment standard;
a frequency shift calculation module, configured to determine, in the upper waveform or the lower waveform, a frequency shift of a waveform pattern in the hepatic vein detection picture relative to a waveform pattern in the portal vein detection picture;
and the liver fibrosis degree determining module is used for determining the liver fibrosis degree according to the frequency shift and a preset liver fibrosis index comparison table, and the liver fibrosis index comparison table records the relationship between the frequency shift of different hepatic veins relative to portal veins and the liver fibrosis degree.
2. The liver hemodynamic detection apparatus of claim 1, wherein the image acquisition module further comprises:
the system comprises a patient history table acquisition unit, a picture analysis unit and a picture analysis unit, wherein the patient history table acquisition unit is used for acquiring a PACS system interface screenshot uploaded by a client before acquiring a detection picture, and the interface screenshot comprises a patient case table;
a number identification unit for extracting a patient number or a detection number from the patient history table;
and the image receiving unit is used for sending the patient number or the detection number to a client and receiving the hepatic vein detection picture and the portal vein detection picture which are searched by the client and are associated with the patient number or the detection number.
3. The liver hemodynamic detection apparatus of claim 1, further comprising a storage module for feeding back the degree of liver fibrosis to the client and locally storing the liver fibrosis degree data after determining the degree of liver fibrosis.
4. The liver hemodynamic detection apparatus of claim 1, wherein the frequency shift computation module comprises:
an offset acquisition unit, configured to determine a plurality of offsets of the positions of peaks or troughs of the waveform map in the hepatic vein detection picture with respect to the positions of corresponding peaks or corresponding troughs of the waveform map in the portal vein detection picture;
and the frequency shift mean solving unit is used for calculating the frequency shift according to a plurality of offset quantities.
5. The liver hemodynamic detection device of claim 1, further comprising a hepatic vein type identification module, configured to determine a hepatic vein type of a hepatic vein detection picture uploaded by the client through text recognition before determining a hepatic fibrosis degree, and call a hepatic fibrosis index comparison table corresponding to the hepatic vein type in the hepatic fibrosis degree determination module.
6. The liver hemodynamic detection apparatus of claim 4, wherein calculating the frequency shift based on a plurality of the offsets comprises:
K＝(a1k1+a2k2+……+ankn)/n；
wherein k is1、k2、knIs n offsets, a1、a2、anThe plurality of parameters are discrete within a set data range according to the deviation value of the amplitude corresponding to each peak in the hepatic vein detection picture relative to the average amplitude, and are used for representing the weight of each deviation amount.
7. The liver hemodynamic detection apparatus of claim 6, wherein the set data range is 0.9-1.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101708123A (en) * 2009-10-28 2010-05-19 上海理工大学 Magnetic resonance elastography detection system of liver fibrosis classification research and method thereof
CN103796579A (en) * 2011-09-01 2014-05-14 微创医学科技有限公司 Method of detecting portal and/or hepatic pressure and a portal hypertension monitoring system
WO2015162200A1 (en) * 2014-04-23 2015-10-29 Vaiomer Method for diagnosing hepatic fibrosis
WO2019006248A1 (en) * 2017-06-30 2019-01-03 Georgia State University Research Foundation, Inc. Noninvasive methods for detecting liver fibrosis

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101708123A (en) * 2009-10-28 2010-05-19 上海理工大学 Magnetic resonance elastography detection system of liver fibrosis classification research and method thereof
CN103796579A (en) * 2011-09-01 2014-05-14 微创医学科技有限公司 Method of detecting portal and/or hepatic pressure and a portal hypertension monitoring system
WO2015162200A1 (en) * 2014-04-23 2015-10-29 Vaiomer Method for diagnosing hepatic fibrosis
WO2019006248A1 (en) * 2017-06-30 2019-01-03 Georgia State University Research Foundation, Inc. Noninvasive methods for detecting liver fibrosis
CN110769741A (en) * 2017-06-30 2020-02-07 佐治亚州立大学研究基金会 Non-invasive method for detecting liver fibrosis

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