CN112819786B - Liver hemodynamic detection device based on multiple hepatic vein wave patterns - Google Patents
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Abstract
The application provides a liver hemodynamic detection device based on a plurality of hepatic vein wave patterns, which comprises an image acquisition module, a detection module and a detection module, wherein the image acquisition module is used for acquiring hepatic vein detection pictures and portal vein detection pictures uploaded by a client; the waveform dividing line determining module is used for determining a waveform dividing line; the waveform identification module is used for cutting the hepatic vein detection picture and the portal vein detection picture, and determining an upper waveform or a lower waveform of the waveform picture by taking the average value of the pixel point gray levels of all the straight lines as a judgment standard, wherein the average value of the pixel point gray levels of all the straight lines is lower than a threshold value; the frequency shift calculation module is used for determining the frequency shift of the waveform diagram in each hepatic vein detection picture relative to the waveform diagram in the portal vein detection picture in the upper waveform or the lower waveform; the frequency shift calibration module is used for calculating a frequency shift calibration value based on the preset specific gravity of each hepatic vein; and the liver fibrosis degree determining module is used for determining the liver fibrosis degree according to the frequency shift calibration value and a preset liver fibrosis index comparison table.
Description
Technical Field
The application belongs to the technical field of data processing, and particularly relates to a liver hemodynamic detection device based on a plurality of hepatic vein wave patterns.
Background
Liver fibrosis is caused by chronic injury of the liver, including hepatitis b and c, alcoholic liver disease, non-alcoholic fatty liver disease (NAFLD), and autoimmune hepatitis. As liver fibrosis progresses, excessive accumulation of extracellular matrix proteins leads to an increase in liver hardness, resulting in cirrhosis, liver failure and liver cancer.
Gold standards that have been used to detect liver fibrosis are biopsy using invasive needles and rely on visual inspection of tissue images by pathologists. Problems with needle biopsies include: 1) Low accuracy due to large sampling errors and variability in interpretation results by pathologists; 2) Pain, and potential medical risks associated with invasive surgery (e.g., major bleeding). There is also a non-invasive technique for comparing various frequency patterns from different veins in the liver (portal vein, left hepatic vein, middle hepatic vein and right hepatic vein), and discriminating the degree of liver fibrosis by a mapping relationship between frequency shift and liver fibrosis index.
Disclosure of Invention
In order to solve at least one of the above technical problems, the present application provides a liver hemodynamic detection device based on a plurality of hepatic vein waveforms, which is applied to a server, and the device includes: the image acquisition module is used for acquiring a hepatic vein detection picture and a portal vein detection picture uploaded by the client, wherein the hepatic vein detection picture comprises at least two detection pictures in 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 waveform diagrams; the waveform dividing line 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 dividing line 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 dividing line, and determining an upper waveform or a lower waveform of the waveform graph by taking the pixel point gray average value of each straight line as a judgment standard, wherein the pixel point gray average value of each straight line is lower than a threshold value; a frequency shift calculation module, configured to determine a frequency shift of a waveform diagram in the hepatic vein detection picture relative to a waveform diagram in the portal vein detection picture in the upper waveform or the lower waveform; the frequency shift calibration module is used for calculating a frequency shift calibration value based on the preset specific gravity of each hepatic vein; and the liver fibrosis degree determining module is used for determining the liver fibrosis degree according to the frequency shift calibration value and a preset liver fibrosis index comparison table, wherein the liver fibrosis index comparison table records the relation between the frequency shift calibration value and the liver fibrosis degree.
Preferably, the image acquisition module further comprises: the medical record table acquisition unit is used for acquiring a PACS system interface screenshot uploaded by the client before acquiring the detection picture, wherein the interface screenshot comprises a case table; a number identification unit for extracting a patient number or a detection number from the medical record table; and the image receiving unit is used for sending the patient number or the detection number to the client and receiving the hepatic vein detection picture and the portal vein detection picture which are related to the patient number or the detection number and are retrieved by the client.
Preferably, the method further comprises a storage module, wherein the storage module is used for feeding back the liver fibrosis degree to the client after determining the liver fibrosis degree, and storing the liver fibrosis degree data locally.
Preferably, the frequency shift calculation module includes: an offset obtaining unit, configured to determine a plurality of offsets of positions of peaks or troughs of a waveform diagram in the hepatic vein detection picture relative to positions of corresponding peaks or corresponding troughs of the waveform diagram in the portal vein detection picture; and a frequency shift average solving unit for calculating the frequency shift according to a plurality of the offsets.
Preferably, calculating the frequency shift according to a plurality of the offsets includes: and calculating an average value of a plurality of offset values as the frequency shift.
Preferably, calculating the frequency shift from the plurality of the offsets includes:
K＝(a 1 k 1 +a 2 k 2 +……+a n k n )/n；
wherein k is 1 、k 2 、k n For n offsets, a 1 、a 2 、a n And a plurality of discrete parameters, such as 0.9-1, 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 in a set data range.
Preferably, the electronic device further comprises a hepatic vein type identification module for determining the hepatic vein type of the hepatic vein detection picture uploaded by the client through text recognition before calculating the frequency shift calibration value, and a specific gravity acquisition module for determining the specific gravity of each hepatic vein based on the hepatic vein type.
The application 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 liver fibrosis degree based on the frequency change value of the liver blood flow velocity, and assist doctors to diagnose the liver fibrosis degree.
Drawings
Fig. 1 is a flow chart of a preferred embodiment of the method for liver hemodynamic detection based on multiple hepatic vein waveforms 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 diagram of a computer device suitable for use in implementing an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application become more apparent, the technical solutions in the embodiments of the present application will be described in more detail 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 application. The embodiments described below by referring to the drawings are exemplary and intended to illustrate the present application and should not be construed as limiting the application. All other embodiments, based on the embodiments of the application, which are apparent to those of ordinary skill in the art without inventive faculty, are intended to be within the scope of the application. Embodiments of the present application will be described in detail below with reference to the accompanying drawings.
According to a first aspect of the present application, there is provided a method of liver hemodynamic detection based on a plurality of hepatic vein waveforms, comprising:
step S1, a hepatic vein detection picture and a portal vein detection picture uploaded by a client are obtained, wherein the hepatic vein detection picture comprises at least two detection pictures in 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 waveform diagrams.
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.
In the step S1, the image uploaded by the doppler ultrasound apparatus includes a portal vein PV detection image and at least two hepatic vein detection images, for example, two detection images of a left hepatic vein LHV and a middle hepatic vein MHV, two detection images of a middle hepatic vein MHV or a right hepatic vein RHV, two detection images of a left hepatic vein LHV and a right hepatic vein RHV, or three detection images of a left hepatic vein LHV, a middle hepatic vein MHV and a right hepatic vein RHV. Any comparison of hepatic vein with portal vein PV can result in a frequency shift, and a frequency shift calibration value is determined based on at least two frequency shifts, and can be used as a basis for determining the degree of hepatic fibrosis.
In some optional embodiments, before acquiring the hepatic vein detection picture and the portal vein detection picture uploaded by the client, the method includes:
and step S11, acquiring a PACS system interface screenshot uploaded by the client before acquiring the detection picture, wherein the interface screenshot comprises a case list.
And step S12, extracting a patient number or a detection number from the medical record table.
And step S13, the patient number or the detection number is sent to a client, and the hepatic vein detection picture and the portal vein detection picture which are related to the patient number or the detection number and retrieved by the client are received.
The client is connected with the PACS system, a program for capturing the picture of the PACS system interface is arranged in the client, and in step S11, the client captures the current case interface of the PACS system after starting the corresponding program.
PACS systems are an abbreviation for Picture Archiving and Communication Systems, meaning image archiving and communication systems. The system is applied to a hospital image department, and the main task is to store various medical images (including images generated by nuclear magnetism, CT, ultrasound, various X-ray machines, various infrared instruments, microscopes and other equipment) generated in daily life in a digital manner through various interfaces (simulation, DICOM, network), and can be quickly returned for use under a certain authorization when needed, and meanwhile, a plurality of auxiliary diagnosis management functions are added. It plays an important role in transmitting data and organizing and storing data among various imaging devices.
The liver hemodynamic detection software is divided into a server side and a client side based on the standard DICOM 3.0 protocol. The above-described liver hemodynamic detection method of the present application is executed at a server, and the server receives ultrasound images in standard format or JPG, PNG, etc. format transmitted by medical image inspection equipment compliant with the standard DICOM 3.0 protocol (for example, in step S1 of the present application, the doppler ultrasound apparatus uploads an image compliant with the standard to a client, and then the client uploads the image to the server), and the medical images are stored in a designated directory. On the other hand, the client side provides basic information uploading of the patient based on the step S11, the server side integrates the case and the image information, the processing result can be returned to the client side, the processing result of the medical image file is displayed by the client side, and the doctor is assisted to diagnose the liver fibrosis degree through the frequency change value of the liver blood flow velocity.
In step S11, after the client opens the PACS system, the system automatically intercepts the interface of the current 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 above the screenshot through software, identifies the patient number and the detection number, and returns the two numbers to the client, or invokes the retrieval tool of the client, and directly reads the hepatic vein detection picture and the portal vein detection picture associated with the patient number or the detection number. Prior to this embodiment, further comprising associating information such as patient number, name, etc. with the storage location of each vein picture detected.
And S2, 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 dividing line based on Hough straight line detection.
And S3, cutting the hepatic vein detection picture and the portal vein detection picture by a plurality of straight lines parallel to the waveform dividing line, and determining an upper waveform or a lower waveform of the waveform diagram by taking the pixel point gray average value of each straight line as a judgment standard, wherein the pixel point gray average value of each straight line is lower than a threshold value.
In this embodiment, the image is converted into a binary image, and then the hough line detection is used to detect the longest line in the image, where the line is the upper and lower boundaries of the waveform that we need to use finally. Step S3 is to determine the upper and lower bounds of the waveform. Taking the upper bound as an example, the waveform oscillates from left to right and up and down, and then the average value of the gray scale of the pixel point of each row is greater than a certain threshold value, and obviously, the average value is smaller at the peak or the trough. While the average value becomes smaller as it gets farther from the peak (because no line passes through the row of pixels). Then a threshold n can be set, an average value of pixel points is calculated from the straight line upwards in each row, and the position of the row with the average value smaller than n is taken, so that the image between the position and the straight line is the upper waveform, and the lower waveform can be determined in the same way.
And S4, determining the frequency shift of the waveform diagram in the hepatic vein detection picture relative to the waveform diagram in the portal vein detection picture in the upper waveform or the lower waveform.
In some alternative embodiments, determining the frequency shift includes:
step S41, determining a plurality of offsets of the positions of the peaks or the troughs of the waveform diagrams in the hepatic vein detection picture relative to the positions of the corresponding peaks or the corresponding troughs of the waveform diagrams in the portal vein detection picture.
Step S42, an average value of a plurality of offset amounts is obtained as the frequency shift.
In an alternative embodiment, the plurality of offsets may be sorted, and the average calculation may be performed by taking a larger value of the plurality of offsets, to obtain the frequency shift.
In an alternative embodiment, the frequency shift amount may be calculated by using a plurality of continuous wave crests, or the offset amount may be calculated by using a plurality of continuous wave troughs, or the offset amount may be calculated by using a plurality of wave crests and wave troughs that are spaced apart.
In this embodiment, the peaks and valleys can be similarly determined using the relationship between the average value of the pixel gray scale and the set threshold value. As shown in fig. 2, in a conventional hepatic fiber waveform, the intervals between peaks are approximately 0.1Hz, and the frequency shift due to hepatic fibers is approximately within 0.05Hz, so that a peak n in the hepatic vein waveform corresponding to a peak m is set to be searched for in a section of 0.05Hz around the reference, with respect to the frequency at which the peak m is located in the hepatic vein waveform.
It should be further noted that, in step S42, the frequency shift may also be calculated according to the weight of each offset, where the weight may be determined by the magnitude of the offset between each peak and the peak average value in the waveform chart, which is detailed in the subsequent frequency shift calculation module.
And S5, calculating a frequency shift calibration value based on the preset specific gravity of each hepatic vein.
In some alternative embodiments, prior to calculating the frequency shift calibration value, comprising: and determining the hepatic vein type of the hepatic vein detection picture uploaded by the client through text recognition, and determining the specific gravity of each hepatic vein based on the hepatic vein type.
For example, the preset specific gravity of the left hepatic vein LHV is m, the specific gravity of the middle hepatic vein MHV is n, and the specific gravity of the right hepatic vein RHV is p, where m+n+p=1.
If the acquired hepatic vein detection image includes the above LHV, MHV, RHV images, the frequency shift calibration values are calculated using m, n, and p as specific gravity, for example, LHV, MHV, RHV the waveform offset values of the three images and the portal vein PV detection image are a, b, and c, respectively, and the calculated frequency shift calibration value x=ma+nb+pc.
If the acquired hepatic vein detection image includes any two of the three images LHV, MHV, RHV, the corresponding specific gravities of the two hepatic vein detection images are normalized, for example, the specific gravity m' of LHV is normalized as follows: m' =m/(m+n); after processing, the frequency shift calibration value x is calculated in the manner described above.
And S6, determining the liver fibrosis degree according to the frequency shift calibration value and a preset liver fibrosis index comparison table, wherein the liver fibrosis index comparison table records the relation between the frequency shift of different hepatic veins relative to portal veins and the liver fibrosis degree.
As shown in fig. 2, which shows a graph comparing various frequency patterns from different veins (portal vein PV, three hepatic veins LHV, MHV, RHV) in the liver, it can be seen that hepatic veins are shifted from portal veins, and that frequency shift calibration values calculated based on two or three such shifts have a correspondence with the degree of liver fibrosis, which is described in a liver fibrosis index lookup table, for example, for calculated frequency shift calibration values, 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 blood flow frequency in the hepatic vein compared to the blood flow frequency in the portal vein is indicative of stage 4 liver fibrosis.
Wherein the term "stage 1" refers to liver fibrosis that undergoes fibrotic distension in certain portal vein areas, but without the presence of a short fibrotic membrane, as in stage 1 of Metavir fibrosis score; the term "stage 2" refers to liver fibrosis that undergoes fibrotic distension in most portal vein areas, wherein a short fibrotic membrane is present, the same as stage 2 Metavir fibrosis score; the term "stage 3" refers to liver fibrosis that undergoes fibrotic dilation in most portal vein areas, where there is bridging between portal veins, the same as stage 3 of Metavir fibrosis score; the term "stage 4" refers to the progression of liver fibrosis to cirrhosis, the same as stage 4 of Metavir fibrosis score.
In some alternative embodiments, after determining the degree of liver fibrosis, further comprising: and S6, feeding the liver fibrosis degree back to the client, and displaying the result by the client, and on the other hand, locally storing the liver fibrosis degree data at a server.
In this embodiment, the server calling program calculates the detected image, stores the data in the local designated directory, and returns the calculation result to the client. The customer opens the web page to present the analysis results.
The application 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 liver fibrosis degree based on the frequency change value of the liver blood flow velocity, and assist doctors to diagnose the liver fibrosis degree.
The second aspect of the present application provides a liver hemodynamic detection apparatus based on a plurality of hepatic vein waveforms corresponding to the above method, mainly comprising:
the image acquisition module is used for acquiring a hepatic vein detection picture and a portal vein detection picture uploaded by the client, wherein the hepatic vein detection picture comprises at least two detection pictures in 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 waveform diagrams; the waveform dividing line 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 dividing line 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 dividing line, and determining an upper waveform or a lower waveform of the waveform graph by taking the pixel point gray average value of each straight line as a judgment standard, wherein the pixel point gray average value of each straight line is lower than a threshold value; a frequency shift calculation module, configured to determine a frequency shift of a waveform diagram in the hepatic vein detection picture relative to a waveform diagram in the portal vein detection picture in the upper waveform or the lower waveform; the frequency shift calibration module is used for calculating a frequency shift calibration value based on the preset specific gravity of each hepatic vein; and the liver fibrosis degree determining module is used for determining the liver fibrosis degree according to the frequency shift calibration value and a preset liver fibrosis index comparison table, wherein the liver fibrosis index comparison table records the relation between the frequency shift calibration value and the liver fibrosis degree.
In some alternative embodiments, the image acquisition module further comprises: the medical record table acquisition unit is used for acquiring a PACS system interface screenshot uploaded by the client before acquiring the detection picture, wherein the interface screenshot comprises a case table; a number identification unit for extracting a patient number or a detection number from the medical record table; and the image receiving unit is used for sending the patient number or the detection number to the client and receiving the hepatic vein detection picture and the portal vein detection picture which are related to the patient number or the detection number and are retrieved by the client.
In some optional embodiments, the method further comprises a storage module for feeding back the liver fibrosis degree to the client after determining the liver fibrosis degree, and storing the liver fibrosis degree data locally.
In some alternative embodiments, the frequency shift calculation module includes: an offset obtaining unit, configured to determine a plurality of offsets of positions of peaks or troughs of a waveform diagram in the hepatic vein detection picture relative to positions of corresponding peaks or corresponding troughs of the waveform diagram in the portal vein detection picture; and a frequency shift average solving unit for calculating the frequency shift according to a plurality of the offsets.
In some alternative embodiments, calculating the frequency shift from a plurality of the offsets includes: and calculating an average value of a plurality of offset values as the frequency shift.
In some alternative embodiments, calculating the frequency shift from a plurality of the offsets includes:
K＝(a 1 k 1 +a 2 k 2 +……+a n k n )/n；
wherein k is 1 、k 2 、k n For n offsets, a 1 、a 2 、a n The method is characterized by comprising the steps of determining a plurality of parameters which 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 representing the weight of each offset.
Taking the example of fig. 2 as an illustration, the dashed line in fig. 2 contains a total of 16 peaks,the average value of the peaks is about 1500, the maximum value is about 6500, the minimum value is about 1000, the deviation value of each peak relative to the average value (the deviation value is different from the deviation amount, the deviation value refers to the peak deviation in the same waveform diagram, the deviation amount refers to the deviation of the corresponding peaks of two waveform diagrams) is about 500-5000, then the data dispersion is carried out according to the deviation value in a set data range such as 0.9-1, and the three corresponding weight values a dispersed in 0.9-1 are provided that the three deviation values of 500, 1000 and 1500 are only included 1 、a 2 、a 3 0.975,0.95,0.925. It should be noted that the stability of the acquired waveform pattern is mainly considered in this embodiment, and the deviation k should be calculated for each deviation k when the waveform tends to be stable near the average value 1 、k 2 、k n In order to give the same weight, the offset value of 1500 may be 0.925 at the lowest and the offset value of 500 may be 0.975 at the highest, for example, if the offset value is offset from the average value, which may be inaccurate.
It should be further noted that the setting data range is determined according to experience or statistical data, so that the relationship between the reaction frequency shift and the liver fibrosis can be more accurate, for example, 0.9-1 or 0.8-1, which is only an example and not limited thereto.
In some optional embodiments, the method further comprises a hepatic vein type identification module for determining a hepatic vein type of the hepatic vein detection picture uploaded by the client through text recognition before calculating the frequency shift calibration value, and a specific gravity acquisition module for determining a specific gravity of each hepatic vein based on 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 a hepatic hemodynamic detection method based on a plurality of hepatic vein waveform diagrams as above.
According to a fourth aspect of the present application, a readable storage medium stores a computer program for implementing the above-described hepatic hemodynamic detection method based on a plurality of hepatic vein waveform diagrams when the computer program is executed by a processor.
Referring now to FIG. 3, there is illustrated a schematic 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 be construed as limiting the functionality and scope of use of embodiments of the present application.
As shown in fig. 3, the computer device 800 includes a Central Processing Unit (CPU) 801, which 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 required for the operation of the device 800 are also stored. The CPU801, ROM802, and RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.
The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, mouse, etc.; an output portion 807 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 808 including a hard disk or 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. The drive 810 is also connected to the I/O interface 805 as needed. 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 needed so that a computer program read out therefrom is mounted into the storage section 808 as needed.
In particular, according to embodiments of the present application, the processes described above with reference to 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 shown in the flowcharts. 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 media 811. The above-described functions defined in the method of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 801. The computer storage medium of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 context of this document, 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 the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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 may be implemented by hardware. The modules or units described may also be provided in a processor, the names of which do not in some cases constitute a limitation of the module or unit itself.
The computer-readable storage medium provided in the fourth aspect of the present application may be contained in the apparatus described in the above embodiment; or may be present alone without being fitted into the device. The computer readable storage medium carries one or more programs which, when executed by the apparatus, process data as described above.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present application should be included in the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (7)
1. A hepatic hemodynamic detection device based on a plurality of hepatic vein waveforms, applied to a server, comprising:
the image acquisition module is used for acquiring a hepatic vein detection picture and a portal vein detection picture uploaded by the client, wherein the hepatic vein detection picture comprises at least two detection pictures in 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 waveform diagrams;
the waveform dividing line 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 dividing line 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 dividing line, and determining an upper waveform or a lower waveform of the waveform graph by taking the pixel point gray average value of each straight line as a judgment standard, wherein the pixel point gray average value of each straight line is lower than a threshold value;
a frequency shift calculation module, configured to determine a frequency shift of a waveform diagram in the hepatic vein detection picture relative to a waveform diagram in the portal vein detection picture in the upper waveform or the lower waveform;
the frequency shift calibration module is used for calculating a frequency shift calibration value based on the preset specific gravity of each hepatic vein;
the liver fibrosis degree determining module is used for determining the liver fibrosis degree according to the frequency shift calibration value and a preset liver fibrosis index comparison table, and the liver fibrosis index comparison table records the relation between the frequency shift calibration value and the liver fibrosis degree.
2. The liver hemodynamic detection apparatus of claim 1, wherein the image acquisition module further comprises:
the medical record table acquisition unit is used for acquiring a PACS system interface screenshot uploaded by the client before acquiring the detection picture, wherein the interface screenshot comprises a medical record table;
a number identification unit for extracting a patient number or a detection number from the medical record table;
and the image receiving unit is used for sending the patient number or the detection number to the client and receiving the hepatic vein detection picture and the portal vein detection picture which are related to the patient number or the detection number and are retrieved by the client.
3. The hepatic hemodynamic detection apparatus of claim 1, further comprising a storage module configured to feed back the liver fibrosis level to the client after determining the liver fibrosis level and to store the liver fibrosis level data locally.
4. The liver hemodynamic detection apparatus of claim 1, wherein the frequency shift calculation module includes:
an offset obtaining unit, configured to determine a plurality of offsets of positions of peaks or troughs of a waveform diagram in the hepatic vein detection picture relative to positions of corresponding peaks or corresponding troughs of the waveform diagram in the portal vein detection picture;
and the frequency shift average value solving unit is used for calculating the frequency shift according to a plurality of offset values.
5. The hepatic hemodynamic detection apparatus of claim 1, further comprising a hepatic vein type identification module for determining a hepatic vein type of a hepatic vein detection picture uploaded by the client by text recognition before calculating the frequency shift calibration value, and a specific gravity acquisition module for determining a specific gravity of each hepatic vein based on the hepatic vein type.
6. The liver hemodynamic detection apparatus of claim 4, wherein calculating the frequency shift from a plurality of the offsets includes:
K＝(a 1 k 1 +a 2 k 2 +……+a n k n )/n；
wherein k is 1 、k 2 、k n For n offsets, a 1 、a 2 、a n The method is characterized by comprising the steps of determining a plurality of parameters which 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 representing the weight of each offset.
7. The liver hemodynamic detection apparatus of claim 6, wherein the set data range is 0.9 to 1.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102406641A (en) * | 2010-09-20 | 2012-04-11 | 中国科学院上海生命科学研究院 | Application of sorafenib to treatment of fibrosis |
| CN102938027A (en) * | 2012-11-30 | 2013-02-20 | 河北大学 | Realization method of computer-assisted liver transplantation operation planning system |
| CN103830844A (en) * | 2013-03-08 | 2014-06-04 | 牛欣 | Infrared liver vital energy function blood flow conditioning instrument modulated through low-frequency electromagnetic vibration |
| EP3141181A1 (en) * | 2015-09-11 | 2017-03-15 | Bernard Boon Chye Lim | Ablation catheter apparatus with a basket comprising electrodes, an optical emitting element and an optical receiving element |
| 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 |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015162200A1 (en) * | 2014-04-23 | 2015-10-29 | Vaiomer | Method for diagnosing hepatic fibrosis |
| US11517287B2 (en) * | 2017-03-07 | 2022-12-06 | The University Of Tokyo | Tissue elasticity measurement device and measurement method |
-
2021
- 2021-02-01 CN CN202110135825.4A patent/CN112819786B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102406641A (en) * | 2010-09-20 | 2012-04-11 | 中国科学院上海生命科学研究院 | Application of sorafenib to treatment of fibrosis |
| CN102938027A (en) * | 2012-11-30 | 2013-02-20 | 河北大学 | Realization method of computer-assisted liver transplantation operation planning system |
| CN103830844A (en) * | 2013-03-08 | 2014-06-04 | 牛欣 | Infrared liver vital energy function blood flow conditioning instrument modulated through low-frequency electromagnetic vibration |
| EP3141181A1 (en) * | 2015-09-11 | 2017-03-15 | Bernard Boon Chye Lim | Ablation catheter apparatus with a basket comprising electrodes, an optical emitting element and an optical receiving element |
| 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 |
Non-Patent Citations (3)
| Title |
|---|
| 肝脏免疫微环境与肝纤维化的研究进展;王思奇;蒋炜;;复旦学报(医学版)(第05期);全文 * |
| 贮脂细胞在大鼠实验性肝纤维化中的作用及机理研究;周建国, 朱建思;南华大学学报(医学版)(第04期);全文 * |
| 非侵入性诊断肝纤维化模型研究进展;甘龙禹;李治国;郝文成;姚慧;蒋坤松;;肝脏(第01期);全文 * |
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