Uterine muscle cell tension analysis method based on uterine muscle cell activity image

Abstract

The invention provides a uterine muscle cell tension analysis method based on a uterine muscle cell activity image, and relates to the technical field of image processing. The method comprises the steps of tension analysis mode obtaining, resting state analysis mode tension analysis, uterine contraction state analysis mode tension analysis and tension analysis result feedback. According to the method, a tension analysis mode is obtained, if the tension analysis mode is a resting state analysis mode, a resting uterine muscle cell image is obtained, and a resting uterine muscle cell tension analysis result is obtained, and if the tension analysis mode is a uterine contraction state analysis mode, a uterine contraction uterine muscle cell image is obtained, and a uterine contraction uterine muscle cell tension analysis result is obtained. Finally, the uterine muscle cell tension analysis result is transmitted to a preset medical staff terminal, the reliability of the uterine muscle cell tension analysis result is improved, and the problem that in the prior art, the reliability of the uterine muscle cell tension analysis result is unstable due to the difference of ultrasonic probe scanning is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a uterine muscle cell tension analysis method based on uterine muscle cell activity images. Background Art

[0002] With the continuous advancement of biomedical technology, the study of cell mechanics has become an important area of ​​research in cell biology. In particular, understanding changes in uterine muscle cell tension is crucial for revealing the mechanisms of uterine contraction and studying pregnancy-related diseases such as premature birth and uterine fibroids. Tension analysis methods based on images of uterine muscle cell activity, utilizing advanced image processing and mechanical modeling techniques, can accurately assess cell tension and promote the development of cell mechanics research and clinical applications.

[0003] Existing analysis methods primarily include traction force microscopy (TFM), optical tweezers, and image analysis algorithms. TFM assesses cell tension by detecting deformation caused by cell-matrix interactions; optical tweezers measure minute intercellular forces through light beam manipulation. Image analysis techniques, such as deep learning algorithms, can automatically process cell morphology and deformation data to provide accurate tension estimates. Despite progress, these technologies still face challenges such as image noise and cellular heterogeneity.

[0004] For example, the invention patent with announcement number CN118279912B discloses a method and system for assessing the degree of differentiation of stem cells based on image analysis, which includes: collecting initial stem cell image data to be processed and performing full convolutional network preprocessing to obtain enhanced stem cell image data; performing dense gated channel transformation and cell segmentation to obtain stem cell segmentation image data; performing feature extraction and direction-aware regression analysis to obtain a directional feature data set; inputting the directional feature data set into the initial stem cell differentiation degree assessment model for multi-scale feature fusion and stem cell differentiation degree assessment to obtain differentiation degree assessment information; performing global search and local optimal solution of model parameters based on the adaptive ephemera algorithm and differentiation degree assessment information to obtain a target stem cell differentiation degree assessment model.

[0005] For example, the invention patent announcement with announcement number: CN114241478B discloses a method and device for identifying abnormal cell images in cervical cell images, comprising: inputting a multi-scale cervical cell image into a pre-trained multi-scale fusion network, wherein the multi-scale cervical cells include different sizes of the same cervical cell image; extracting key node features of the cervical cell image at each scale in the multi-scale cervical cell image through the multi-scale fusion network; determining the grading results of the abnormal cell images in the cervical cell image corresponding to the key node features of the cervical cell image at each scale through the Berthesda grading system; fusing the grading results of the abnormal cell images in the cervical cell image at each scale, and outputting the abnormal cell images in the cervical cell image and the abnormal cell image with the highest level in the grading results.

[0006] However, in the process of implementing the technical solutions of the embodiments of the present invention, the present invention found that the above technology has at least the following technical problems:

[0007] In the prior art, corresponding ultrasonic elasticity images are obtained through ultrasonic testing. However, during the ultrasonic elasticity image scanning process, due to differences in the operation of the probe by medical staff, there is a problem of unstable reliability of the uterine muscle cell tension analysis results due to differences in ultrasonic probe scanning. Summary of the Invention

[0008] The embodiment of the present invention solves the problem in the prior art that the reliability of uterine muscle cell tension analysis results is unstable due to differences in ultrasound probe scanning by providing a uterine muscle cell tension analysis method based on uterine muscle cell activity images, thereby improving the reliability of the uterine muscle cell tension analysis results.

[0009] An embodiment of the present invention provides a uterine muscle cell tension analysis method based on a uterine muscle cell activity image, comprising the following steps: obtaining an initial judgment image through an initial scan, performing a tension analysis state judgment based on the initial judgment image, and selecting a tension analysis mode for an object to be analyzed, wherein the tension analysis mode includes a resting state analysis mode and a uterine contraction state analysis mode; if the tension analysis mode is a resting state analysis mode, performing an ultrasonic elastography scan to obtain a corresponding resting uterine muscle cell image, performing an ultrasonic scanning validity evaluation based on the resting uterine muscle cell image to determine whether to rescan, and if rescanning is performed, performing an ultrasonic scanning validity evaluation on the resting uterine muscle cell image obtained by the rescan, and determining whether to rescan based on the resting uterine muscle cell image that meets the scanning validity conditions. For example, the resting uterine muscle cell tension analysis result is obtained, otherwise the uterine muscle cell tension analysis result is directly obtained based on the resting uterine muscle cell image; if the tension analysis mode is the uterine contraction state analysis mode, an ultrasonic elastic imaging scan is performed to obtain the corresponding contracting uterine muscle cell image, the image tension performance is determined based on the contracting uterine muscle cell image, and corresponding tension analysis optimization measures are taken to obtain the corresponding contracting uterine muscle cell tension analysis result; the obtained uterine muscle cell tension analysis result is transmitted to the preset medical staff terminal, the uterine muscle cell tension analysis result includes the resting uterine muscle cell tension analysis result and the contracting uterine muscle cell tension analysis result, and the uterine muscle cell tension analysis result represents a collection of uterine muscle cell images, elasticity diagrams and uterine muscle cell tension analysis data.

[0010] Optionally, an ultrasound scan effectiveness evaluation is performed on the resting uterine muscle cell image to determine whether to rescan. The specific steps are as follows: K1, performing an ultrasound scan effectiveness evaluation on the acquired resting uterine muscle cell image to obtain an ultrasound detection effectiveness judgment value, and the ultrasound detection effectiveness judgment value is used to determine the effectiveness of the uterine muscle cell image currently imaged by ultrasound scanning; K2, if the ultrasound detection effectiveness judgment value is not less than the ultrasound detection evaluation threshold obtained from a preset database, it indicates that the resting uterine muscle cell image is qualified and K4 is executed; if the ultrasound detection effectiveness judgment value is less than the ultrasound detection evaluation threshold obtained from the preset database, the preset medical staff is prompted to change the scanning area for rescanning and execute K3; K3, based on the liquid interference judgment data, it is determined whether to optimize the ultrasound imaging parameters to obtain a qualified resting uterine muscle cell image and execute K4; K4, dividing the resting uterine muscle cell image according to the color identification of the qualified resting uterine muscle cell image to obtain a scar map domain and a non-scar map domain, and performing tension feedback on the scar map domain of the resting uterine muscle cell image, and the tension feedback means feeding back the tension condition of the scar map domain in the resting uterine muscle cell image to the preset medical staff.

[0011] Optionally, the specific process of obtaining the ultrasonic detection effectiveness judgment value is as follows: obtaining ultrasonic detection evaluation data of resting uterine muscle cell images, the ultrasonic detection evaluation data including echo intensity, image grayscale value, elastic modulus, attenuation coefficient and signal-to-noise ratio; obtaining ultrasonic evaluation parameters from a preset database, the ultrasonic evaluation parameters including ultrasonic evaluation reference values ​​and ultrasonic evaluation influence weights, the ultrasonic evaluation reference values ​​including echo intensity reference range, grayscale value reference range, minimum elastic modulus limit value, minimum attenuation coefficient critical value and minimum signal-to-noise ratio division value, the ultrasonic evaluation influence weights including echo intensity influence weight, grayscale value influence weight, elastic modulus influence weight, attenuation coefficient influence weight and signal-to-noise ratio influence weight; comparing the echo intensity with the corresponding echo intensity reference range A range deviation quantification operation is performed to obtain the echo intensity deviation; a range deviation quantification operation is performed on the image grayscale value and the corresponding grayscale value reference range to obtain the grayscale value deviation; a degree of deviation quantification operation is performed on the elastic modulus, attenuation coefficient and signal-to-noise ratio and the corresponding ultrasound assessment reference value to obtain the corresponding elastic modulus deviation degree value, attenuation coefficient deviation degree value and signal-to-noise ratio deviation degree value; the echo intensity deviation, grayscale value deviation, elastic modulus deviation degree value, attenuation coefficient deviation degree value and signal-to-noise ratio deviation degree value are coupled with the corresponding ultrasound assessment influence weight after weighting operation to obtain the ultrasound detection validity judgment value, which is used to determine the reliability of the resting uterine muscle cell image obtained by ultrasound elastography.

[0012] Optionally, whether to perform ultrasound imaging parameter optimization is determined based on the liquid interference judgment data, and the specific process is as follows: based on the resting uterine muscle cell image, the corresponding liquid interference judgment data is obtained, and the liquid interference judgment data includes the image average grayscale value and the image average echo intensity; the liquid interference judgment data is compared with the reference liquid judgment data obtained from the preset database, and the reference liquid judgment data includes the reference judgment grayscale value and the reference judgment echo intensity; if any liquid interference judgment data is not less than the corresponding reference liquid judgment data, ultrasound imaging parameter optimization is performed, otherwise no additional processing is performed; the specific steps of ultrasound imaging parameter optimization are as follows: the liquid interference judgment data and the corresponding reference liquid judgment data are quantified to obtain the corresponding liquid interference difference quantization value; based on the liquid interference difference quantization value mapping, an imaging parameter optimization ratio group is obtained, and the imaging parameters are optimized and compensated according to the imaging parameter optimization ratio group to obtain the corresponding parameters to be imaged, and re-scanning is performed based on the parameters to be imaged, and the imaging parameter optimization ratio group includes the probe frequency adjustment ratio and the echo gain adjustment ratio, and the imaging parameters include the probe frequency and the echo gain.

[0013] Optionally, the specific steps for performing tension feedback on the scar image domain of the resting uterine muscle cell image are as follows: Step 1, obtaining color identification data of the scar image domain of the resting uterine muscle cell image, inputting the color identification data into the image elasticity quantization value model, and outputting the corresponding image elasticity quantization value, recorded as the resting image elasticity quantization value; Step 2, transmitting the resting image elasticity quantization value of the scar image domain and the ultrasonic elasticity image of the resting uterine muscle cell image to a preset medical staff terminal.

[0014] Optionally, the specific method of obtaining the image elasticity quantization value model is as follows: obtaining the color identification data of the corresponding uterine muscle cell image and performing data normalization processing, the color identification data including color RGB value, color region area and elastic modulus; classifying and numbering the color of the uterine muscle cell image according to the color RGB value, and obtaining the corresponding color identification analysis parameters from a preset database at the same time, the color identification analysis parameters including color identification analysis ratio and color category analysis ratio, the color identification analysis ratio including RGB value analysis ratio, region area analysis ratio and elastic modulus analysis ratio; performing a weighted operation on the color identification data and the corresponding color identification analysis ratio and coupling them to obtain the initial quantization value of the image elasticity; performing a cumulative sum operation on the initial quantization value of the elasticity of each type of color RGB and the corresponding color category analysis ratio and then performing a mean operation to obtain the image elasticity quantization value model, the image elasticity quantization value model is used to quantify the tension characteristics of the corresponding image area through the elastic map of the ultrasonic elastic image.

[0015] Optionally, image tension performance is determined based on the image of contracting uterine muscle cells, and corresponding tension analysis optimization measures are taken to obtain corresponding uterine contracting uterine muscle cell tension analysis results. The specific steps are as follows: H1, when the tension analysis mode is obtained as the uterine contraction state analysis mode, the ultrasound imaging frequency is adjusted to the maximum imaging frequency; H2, the color identification data of the uterine contraction uterine muscle cell image within a preset time period is obtained and input into the image elasticity quantization value model, and the corresponding image elasticity quantization value is output, which is recorded as the uterine contraction image elasticity quantization value; H3, the uterine contraction image elasticity quantization value within the preset time period is averaged to obtain the corresponding average uterine contraction image elasticity quantization value; H4, the average uterine contraction image The image elasticity quantization value is compared with the reference uterine contraction image elasticity quantization value obtained from the preset database, and corresponding image monitoring and early warning measures are taken to obtain corresponding judgment results. If the average uterine contraction image elasticity quantization value is not less than the reference uterine contraction image elasticity quantization value, the real-time demand level of uterine contraction state tension monitoring is recorded as a first-level demand, otherwise it is a second-level demand; H5, according to the judgment result of the real-time demand level of uterine contraction state tension monitoring, corresponding imaging frequency adjustment measures are taken; H6, the uterine contraction uterine muscle cell image is divided to obtain the corresponding uterine contraction scar image domain, and scar priority feedback is performed on the uterine contraction scar image domain. Scar priority feedback means that priority feedback is given to the analysis results of the uterine contraction scar image domain.

[0016] Optionally, the specific process of taking corresponding image monitoring and early warning measures is as follows: when the quantified value of the elasticity of the contraction image is not less than the quantified value of the elasticity of the reference contraction image, a first-level warning is issued. The first-level warning indicates that the object to be analyzed and the preset medical staff are prompted by sound that the uterine contraction tension changes are abnormal; when the number of first-level warnings exceeds the preset number, a second-level warning is issued. The second-level warning includes a high-frequency sound warning and a display warning.

[0017] Optionally, corresponding imaging frequency adjustment measures are taken according to the determination result of the real-time demand level of uterine contraction state tension monitoring. The specific process is: if there are a continuous preset number of preset time periods in which the real-time demand level of uterine contraction state tension monitoring is a second-level demand, the difference quantization value of the average uterine contraction image elasticity of all preset time periods and the corresponding reference uterine contraction image elasticity, are quantified, and then the mean operation is performed to obtain the corresponding average difference degree value; the imaging frequency reduction multiple is obtained based on the average difference degree value mapping, and the ultrasound imaging frequency is adjusted by the imaging frequency reduction multiple; if the real-time demand level of uterine contraction state tension monitoring in the preset time period is a first-level demand, the ultrasound imaging frequency is adjusted back to the maximum imaging frequency.

[0018] Optionally, scar priority feedback is performed on the uterine contraction scar image domain, and the specific process is as follows: the color identification data of the uterine contraction scar image domain is input into the image elasticity quantization value model, and the corresponding image elasticity quantization value is output, which is recorded as the uterine contraction scar image elasticity quantization value; the uterine contraction scar elasticity quantization value sequence is obtained by statistically analyzing the uterine contraction scar image elasticity quantization values ​​output within a preset time period in chronological order; the uterine contraction scar elasticity quantization value sequence corresponding to the uterine contraction uterine muscle cell image within the preset time period is preferentially transmitted to the preset medical staff terminal.

[0019] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0020] 1. By obtaining the tension analysis mode of the object to be analyzed, if it is a resting state analysis mode, a resting uterine muscle cell image is obtained and the ultrasound scanning effectiveness evaluation is performed based on this to determine whether to rescan. Based on the judgment result, the corresponding uterine muscle cell tension analysis result is obtained. If it is a contraction state analysis mode, a contraction uterine muscle cell image is obtained and the image tension performance is determined based on this. Then, tension analysis optimization measures are taken to obtain the contraction uterine muscle cell tension analysis result. Finally, the uterine muscle cell tension analysis result is transmitted to the medical staff terminal, so as to provide the medical staff with more detailed tension analysis results, thereby improving the reliability of the uterine muscle cell tension analysis result, and effectively solving the problem of unstable reliability of uterine muscle cell tension analysis results due to differences in ultrasound probe scanning in the existing technology.

[0021] 2. By obtaining the ultrasonic detection evaluation data of the resting uterine muscle cell image and obtaining the ultrasonic evaluation parameters from the preset database, the echo intensity and image grayscale value are then quantified by the range deviation operation with the corresponding ultrasonic evaluation reference value to obtain the echo intensity deviation and grayscale value deviation. At the same time, the elastic modulus, attenuation coefficient and signal-to-noise ratio are quantified by the deviation degree with the corresponding ultrasonic evaluation reference value to obtain the elastic modulus deviation degree value, attenuation coefficient deviation degree value and signal-to-noise ratio deviation degree value. Finally, the above-mentioned data are weighted and coupled with the corresponding ultrasonic evaluation influence ratio to obtain the ultrasonic detection validity judgment value, thereby more accurately quantifying the reliability of the resting uterine muscle cell image, and thus achieving a more accurate judgment of the validity of the resting uterine muscle cell image.

[0022] 3. By obtaining the color identification data of the corresponding uterine muscle cell image and performing data normalization processing, the color of the uterine muscle cell image is classified and numbered according to the color RGB value, and the color identification analysis parameters are obtained from the preset database, and then the color identification data and the corresponding color identification analysis ratio are weighted and coupled to obtain the initial quantization value of the image elasticity. Finally, the initial quantization value of the elasticity of each type of color RGB and the corresponding color category analysis ratio are calculated to obtain the image elasticity quantization value model, thereby more accurately quantifying the tension characteristics of the corresponding image area represented by the elastic map of the ultrasound elastic image, thereby achieving more accurate feedback of the tension characteristics of the uterine muscle cell image. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flow chart of a method for analyzing uterine muscle cell tension based on uterine muscle cell activity images provided by an embodiment of the present invention;

[0024] Figure 2 A flowchart of scanning determination in a method for analyzing uterine muscle cell tension based on uterine muscle cell activity images provided by an embodiment of the present invention;

[0025] Figure 3 This is a flowchart of image tension performance determination in a uterine muscle cell tension analysis method based on uterine muscle cell activity images provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The embodiment of the present invention solves the problem in the prior art that the reliability of uterine muscle cell tension analysis results is unstable due to differences in ultrasonic probe scanning by providing a uterine muscle cell tension analysis method based on uterine muscle cell activity images. An initial judgment image is obtained through initial scanning, and a tension analysis state is determined based on the initial judgment image to select a tension analysis mode for the object to be analyzed. If the tension analysis mode is a resting state analysis mode, a resting uterine muscle cell image is obtained and an ultrasonic scanning effectiveness evaluation is performed based on this to determine whether to rescan. Based on the judgment result, a corresponding uterine muscle cell tension analysis result is obtained. If the tension analysis mode is a uterine contraction state analysis mode, the ultrasonic imaging frequency is adjusted to the maximum imaging frequency, and then the color identification data of the uterine contraction uterine muscle cell image within a preset time period is obtained and input into the image elasticity quantization value model, and the corresponding image elasticity quantization value is output, which is recorded as uterine The quantified value of the uterine contraction image elasticity is obtained, and then the quantified value of the uterine contraction image elasticity within the preset time period is averaged to obtain the corresponding average quantified value of the uterine contraction image elasticity. The average quantified value of the uterine contraction image elasticity is then compared with the reference quantified value of the uterine contraction image elasticity obtained from the preset database to take corresponding image monitoring and early warning measures and obtain corresponding judgment results. If the average quantified value of the uterine contraction image elasticity is not less than the reference quantified value of the uterine contraction image elasticity, the real-time demand level of the uterine contraction state tension monitoring is recorded as the first-level demand, otherwise it is the second-level demand. Then, according to the judgment result of the real-time demand level of the uterine contraction state tension monitoring, corresponding imaging frequency adjustment measures are taken, and the uterine contraction uterine muscle cell image is divided to obtain the corresponding uterine contraction scar map domain for scar priority feedback. Finally, the obtained uterine muscle cell tension analysis result is transmitted to the preset medical staff terminal, thereby improving the reliability of the uterine muscle cell tension analysis result.

[0027] The technical solution in the embodiment of the present invention is to solve the problem of unstable reliability of uterine muscle cell tension analysis results caused by differences in ultrasound probe scanning. The overall idea is as follows:

[0028] By obtaining the tension analysis mode, if the tension analysis mode is a resting state analysis mode, a resting uterine muscle cell image is obtained and the corresponding uterine muscle cell tension analysis result is obtained; if the tension analysis mode is a contraction state analysis mode, a contracting uterine muscle cell image is obtained to obtain the contracting uterine muscle cell tension analysis result, and finally the obtained uterine muscle cell tension analysis result is transmitted to the preset medical staff terminal, thereby achieving the effect of improving the reliability of the uterine muscle cell tension analysis result.

[0029] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] like Figure 1FIG. 1 is a flow chart of a method for analyzing uterine muscle cell tension based on uterine muscle cell activity images according to an embodiment of the present invention, the method comprising the following steps:

[0031] An initial determination image is obtained through an initial scan, and tension analysis state determination is performed based on the initial determination image to select a tension analysis mode for the object to be analyzed. The tension analysis mode includes a rest state analysis mode and a uterine contraction state analysis mode.

[0032] It should be added that the specific process of tension analysis state judgment based on the initial judgment image is: obtain the ultrasonic hardness value of the initial judgment image, compare the ultrasonic hardness value with the state boundary value obtained from the preset database, if the ultrasonic hardness value is greater than the state boundary value, then select the tension analysis mode of the object to be analyzed as the contraction state analysis mode, otherwise it is the rest state analysis mode; the state boundary value is pre-set by the preset staff and stored in the preset database, and the ultrasonic hardness value is usually expressed in elastic modulus (unit such as kPa).

[0033] If the tension analysis mode is the resting state analysis mode, an ultrasonic elastography scan is performed to obtain the corresponding resting uterine muscle cell image. The ultrasonic scanning validity evaluation is performed based on the resting uterine muscle cell image to determine whether to rescan. If a rescan is performed, the ultrasonic scanning validity evaluation is performed on the resting uterine muscle cell image obtained by the rescan, and the resting uterine muscle cell tension analysis result is obtained based on the resting uterine muscle cell image that meets the scan validity conditions. Otherwise, the uterine muscle cell tension analysis result is obtained directly based on the resting uterine muscle cell image.

[0034] If the tension analysis mode is the uterine contraction state analysis mode, an ultrasonic elastography scan is performed to obtain a corresponding uterine contraction myocyte image, image tension performance is determined based on the uterine contraction myocyte image, and corresponding tension analysis optimization measures are taken to obtain a corresponding uterine contraction myocyte tension analysis result.

[0035] The obtained uterine muscle cell tension analysis results are transmitted to the preset medical staff terminal. The uterine muscle cell tension analysis results include resting uterine muscle cell tension analysis results and uterine contraction uterine muscle cell tension analysis results. The uterine muscle cell tension analysis results represent a collection of uterine muscle cell images, elasticity diagrams, and uterine muscle cell tension analysis data, wherein the uterine muscle cell tension analysis data includes but is not limited to ultrasound detection evaluation data, ultrasound detection validity judgment value, liquid interference judgment data, color identification data, image elasticity quantification value, and uterine contraction scar elasticity quantification value sequence.

[0036] Specifically, the uterine muscle cell tension analysis results include resting image elasticity quantification values ​​of scar image domain, resting uterine muscle cell images and ultrasonic elasticity images thereof, or uterine contraction scar elasticity quantification value sequences, uterine contraction muscle cell images and ultrasonic elasticity images thereof.

[0037] In this embodiment, as the uterus of the subject being analyzed grows during pregnancy, the uterine muscle fibers stretch and experience increased pressure. Uterine scars are often unable to withstand high tension, leading to uterine rupture in late pregnancy. Cesarean section is typically performed at 39 weeks of gestation to prevent uterine muscle fiber tension from exceeding its maximum tolerance during contractions or further uterine enlargement, ultimately leading to uterine rupture. However, some women with uterine scars experience uterine rupture before elective cesarean sections due to thin myometrium or low uterine muscle fiber tension tolerance. Therefore, it is necessary to test the uterine muscle cells of the subject being analyzed. Currently, ultrasound testing is performed to obtain corresponding ultrasound elasticity images for tension analysis. However, during ultrasound elasticity imaging, variability in probe manipulation by medical personnel can reduce the reliability of the uterine muscle cell tension analysis results. By analyzing the tension of the subject's uterus under two different conditions and implementing different optimization measures, the accuracy of the uterine muscle cell images used for tension analysis is ensured, thereby improving the reliability of the uterine muscle cell tension analysis results.

[0038] It should be added that before designing the uterine muscle cell tension analysis method based on uterine muscle cell activity images, a preset database for storing various types of setting data was established by the preset personnel. The preset database includes but is not limited to ultrasound detection evaluation thresholds, ultrasound evaluation parameters, reference liquid judgment data, color identification analysis parameters and reference uterine contraction image elasticity quantification values, etc., among which various values ​​are directly set by preset professionals. For example, the ultrasound detection evaluation threshold is obtained by the preset staff based on the ultrasound detection evaluation data corresponding to the qualified ultrasound elasticity image generated in the historical database, and is substituted into the specific restriction expression of the ultrasound detection validity judgment value to obtain the corresponding data set. The data set is averaged to obtain the ultrasound detection evaluation threshold, which is pre-stored in the preset database.

[0039] It's important to explain that ultrasound elastography assesses tissue stiffness or elasticity by measuring its response to external forces. In detecting uterine muscle fiber tension, ultrasound elastography primarily utilizes the propagation properties of ultrasound waves to reflect changes in tissue stiffness. Ultrasound elastography images the tissue's response to external forces (such as vibration or pressure). Stiffer tissue (such as fibrotic scar tissue) typically exhibits less deformation under external forces, while softer tissue (such as normal uterine muscle fibers) experiences greater deformation. Ultrasound elastography utilizes this principle to assess tissue elasticity or stiffness. An ultrasound probe captures reflected waves, recording the time difference, wave velocity, and degree of deformation. This information is converted into an elastography image, showing the distribution of stiffness (or rigidity) across different regions. Based on these differences in wave propagation speed, image processing software generates an elastography map, typically displaying stiffer areas as brighter or redder, and softer areas as darker or bluer. This allows medical professionals to visualize the stiffness distribution of uterine muscle fibers in the image and assess their tension.

[0040] like Figure 2 As shown, it is a flowchart of scanning judgment in the uterine muscle cell tension analysis method based on uterine muscle cell activity image provided by an embodiment of the present invention. The specific logic is: the obtained resting uterine muscle cell image is evaluated for ultrasonic scanning validity to obtain an ultrasonic detection validity judgment value. If the ultrasonic detection validity judgment value is not less than the ultrasonic detection evaluation threshold obtained from the preset database, it indicates that the resting uterine muscle cell image is qualified and the resting uterine muscle cell image is divided. If the ultrasonic detection validity judgment value is less than the ultrasonic detection evaluation threshold obtained from the preset database, the preset medical staff is prompted to change the scanning area for rescanning and perform liquid interference judgment; based on the resting uterine muscle cell image, the corresponding liquid interference judgment data is obtained, and the liquid interference judgment data is compared with the reference liquid judgment data. If any liquid interference judgment data is not less than the corresponding reference liquid judgment data, the ultrasonic imaging parameters are optimized, otherwise no additional processing is performed.

[0041] It should be understood that the specific steps of ultrasound imaging parameter optimization are as follows: quantify the difference between the liquid interference judgment data and the corresponding reference liquid judgment data to obtain the corresponding liquid interference difference quantification value; obtain the imaging parameter optimization ratio group based on the liquid interference difference quantification value mapping, optimize the imaging parameters according to the imaging parameter optimization ratio group to obtain the corresponding parameters to be imaged, re-scan based on the parameters to be imaged, obtain a qualified resting uterine muscle cell image and divide the resting uterine muscle cell image; wherein, the resting uterine muscle cell image is divided to obtain a scar image domain and a non-scar image domain, and perform tension feedback on the scar image domain of the resting uterine muscle cell image; through the above process, not only the accuracy of ultrasound examination is improved, but also the accuracy of tension analysis of uterine muscle cell images is improved, and the reliability of uterine muscle cell tension analysis results is also improved.

[0042] Optionally, the effectiveness of ultrasound scanning is evaluated based on the resting uterine muscle cell image to determine whether to rescan. The specific steps are as follows:

[0043] K1, performing ultrasound scanning effectiveness evaluation on the acquired resting uterine muscle cell image to obtain an ultrasound detection effectiveness judgment value, which is used to judge the effectiveness of the current ultrasound scanning imaging uterine muscle cell image.

[0044] K2, compare the ultrasound detection validity judgment value with the ultrasound detection evaluation threshold obtained from the preset database: if the ultrasound detection validity judgment value is not less than the ultrasound detection evaluation threshold obtained from the preset database, it means that the resting uterine muscle cell image is qualified and K4 is executed; if the ultrasound detection validity judgment value is less than the ultrasound detection evaluation threshold obtained from the preset database, the preset medical staff is prompted to change the scanning area for rescanning and execute K3.

[0045] K3: Determine whether to optimize ultrasound imaging parameters based on the liquid interference determination data, obtain a qualified resting uterine muscle cell image, and execute K4.

[0046] K4, divides the resting uterine muscle cell image according to the color identification of the qualified resting uterine muscle cell image to obtain the scar map domain and the non-scar map domain, and performs tension feedback on the scar map domain of the resting uterine muscle cell image. The tension feedback means feeding back the tension condition of the scar map domain in the resting uterine muscle cell image to the preset medical staff.

[0047] It's important to note that in ultrasound elasticity images, scar area demarcation typically relies on color coding, with different color values ​​representing tissue areas of varying hardness. Ultrasound elasticity imaging (such as ultrasound elastography or strain imaging) can demonstrate tissue hardness, with areas of higher hardness (such as scar tissue) typically represented by brighter or darker colors, while areas of lower hardness (such as healthy tissue) are represented by darker or lighter colors.

[0048] In this embodiment, the effectiveness evaluation of ultrasound scanning ensures that the scanned image quality meets the expected standards, thereby providing reliable basic data for further analysis; through liquid interference determination, the images of uterine muscle cells that do not meet the expected standards are reduced to enter the subsequent analysis, ensuring the accuracy of the final results; and by optimizing the scanning parameters, the scanning quality and imaging clarity are improved, and the accuracy of the data is guaranteed, which not only helps medical staff to quickly identify scar areas, but also can provide specific tension feedback of the scar area, providing valuable information for subsequent treatment decisions; through the above steps of effectiveness evaluation of ultrasound images, parameter optimization, area division and tension feedback, not only the accuracy of ultrasound examination is improved, but also more accurate data support is provided for subsequent treatment.

[0049] Optionally, the specific process of obtaining the ultrasonic detection effectiveness determination value is as follows:

[0050] First, ultrasound detection and evaluation data of resting uterine muscle cell images are obtained, and the ultrasound detection and evaluation data include echo intensity, image grayscale value, elastic modulus, attenuation coefficient and signal-to-noise ratio.

[0051] It should be added that the echo intensity is usually calculated by the built-in processing system of the ultrasound device. The specific acquisition method is to convert the signal into a grayscale value after reflection and display it on the image; the image grayscale value is calculated in real time by the ultrasound device based on the intensity of the reflected echo signal and displayed on the display with different brightness. The higher the image grayscale value, the stronger the reflection, which usually represents hard tissue (such as bones and muscles); the lower the image grayscale value, the weaker the reflection, which usually represents soft tissue or liquid (such as amniotic fluid); through the elastic imaging mode of the ultrasound device (such as sound wave frequency changes, strain imaging, etc.), the degree of tissue deformation is measured and an image is generated. Hard tissue will show a higher elastic modulus, while soft tissue will show a lower elastic modulus; the attenuation coefficient is usually calculated by the ultrasound device by measuring the intensity change of the ultrasound wave passing through the tissue, which can be specifically calculated by transmitting and receiving data of ultrasound signals of different frequencies; the ratio of the effective signal to the noise signal is calculated by the ultrasound device.

[0052] At the same time, ultrasound evaluation parameters are obtained from a preset database. The ultrasound evaluation parameters include ultrasound evaluation reference values ​​and ultrasound evaluation influence ratios. The ultrasound evaluation reference values ​​include echo intensity reference range, grayscale value reference range, minimum elastic modulus limit value, minimum attenuation coefficient critical value and minimum signal-to-noise ratio division value. The echo intensity reference range represents the range between the maximum echo intensity and the minimum echo intensity. The grayscale value reference range represents the range between the minimum grayscale value and the maximum grayscale value. The ultrasound evaluation influence ratios include echo intensity influence ratio, grayscale value influence ratio, elastic modulus influence ratio, attenuation coefficient influence ratio and signal-to-noise ratio influence ratio.

[0053] Specifically, the ultrasonic assessment influence ratio represents the degree of influence of the ultrasonic testing assessment data on the ultrasonic testing validity determination value. Each ultrasonic testing assessment data has a unique mapping relationship with its corresponding ultrasonic assessment influence ratio, and the value range is between 0 and 1. For example, a mapping set of ultrasonic testing assessment data and preset ultrasonic assessment influence ratios is constructed, and the real-time echo intensity, image grayscale value, elastic modulus, attenuation coefficient, and signal-to-noise ratio are input into the mapping set to obtain the corresponding echo intensity influence ratio, grayscale value influence ratio, elastic modulus influence ratio, attenuation coefficient influence ratio, and signal-to-noise ratio influence ratio, which respectively represent the degree of influence of the echo intensity, image grayscale value, elastic modulus, attenuation coefficient, and signal-to-noise ratio on the ultrasonic testing validity determination value, and the sum of the five is 1.

[0054] Specifically, the ultrasound evaluation reference value is obtained from a preset database, which is specifically pre-set by a preset staff based on specific requirements of ultrasound imaging and stored in the preset database.

[0055] Next, a range deviation quantification operation is performed on the echo intensity and the corresponding echo intensity reference range to obtain an echo intensity deviation.

[0056] It should be added that the specific expression of the echo intensity deviation is as follows:

[0057]

[0058] Wherein, EI represents the echo intensity of the resting uterine muscle cell image, EI_max represents the maximum echo intensity, EI_min represents the minimum echo intensity, and EI_i represents the echo intensity deviation of the resting uterine muscle cell image.

[0059] Furthermore, a range deviation quantization operation is performed on the image grayscale value and the corresponding grayscale value reference range to obtain a grayscale value deviation.

[0060] It should be added that the specific expression of gray value deviation is as follows:

[0061]

[0062] Wherein, GV represents the image grayscale value of the resting uterine muscle cell image, GV_min represents the minimum grayscale value, GV_max represents the maximum grayscale value, and GV_i represents the grayscale value deviation of the resting uterine muscle cell image.

[0063] Then, a quantitative operation is performed on the deviation degree of the elastic modulus, attenuation coefficient and signal-to-noise ratio from the corresponding ultrasonic evaluation reference value to obtain the corresponding elastic modulus deviation degree value, attenuation coefficient deviation degree value and signal-to-noise ratio deviation degree value.

[0064] It should be added that the specific expression of the elastic modulus deviation value is as follows:

[0065]

[0066] Where EM represents the elastic modulus of the resting uterine muscle cell image, EM min represents the minimum elastic modulus limit value, and EM_i represents the elastic modulus deviation value of the resting uterine muscle cell image.

[0067] It should be added that the specific expression of the attenuation coefficient deviation value is as follows:

[0068]

[0069] Where AC represents the attenuation coefficient of the resting uterine muscle cell image, AC min represents the minimum attenuation coefficient critical value, and AC_i represents the attenuation coefficient deviation value of the resting uterine muscle cell image.

[0070] It should be added that the specific expression of the signal-to-noise ratio deviation value is as follows:

[0071]

[0072] Among them, SNR represents the signal-to-noise ratio of the resting uterine muscle cell image. min represents the minimum signal-to-noise ratio division value, and SNR_i represents the signal-to-noise ratio deviation value of the resting uterine muscle cell image.

[0073] Finally, by weighting the echo intensity deviation, grayscale value deviation, elastic modulus deviation, attenuation coefficient deviation, and signal-to-noise ratio deviation with the corresponding ultrasound assessment influence weights, the ultrasound detection validity judgment value was obtained. The ultrasound detection validity judgment value was used to determine the reliability of the resting uterine muscle cell image obtained by ultrasound elastography.

[0074] The specific limiting expression of the ultrasonic testing effectiveness judgment value is as follows:

[0075] EUT=EI_i×θ ei+GV_i×θ gv +EM_i×θ em +AC_i×θ ac +SNR_i×θ snr ;

[0076] Where θ ei Indicates the echo intensity influence ratio, θ gv Indicates the grayscale value influence ratio, θ em Indicates the elastic modulus affecting specific gravity, θ ac Indicates the attenuation coefficient affecting the specific gravity, θ snr represents the influence ratio of signal-to-noise ratio, and EUT represents the effectiveness judgment value of ultrasound detection of resting uterine muscle cell images.

[0077] In this embodiment, the algorithm combines the echo intensity deviation, grayscale value deviation, elastic modulus deviation, attenuation coefficient deviation and signal-to-noise ratio deviation with the corresponding ultrasound evaluation influence ratio to analyze and obtain the ultrasound detection validity judgment value, wherein, as the echo intensity deviation, grayscale value deviation, elastic modulus deviation, attenuation coefficient deviation and signal-to-noise ratio deviation increase, the corresponding ultrasound detection validity judgment value also increases, indicating that the detection validity of the current ultrasound scanning imaging uterine muscle cell image is lower, and vice versa, it indicates that the detection validity of the ultrasound scanning imaging uterine muscle cell image is higher; judging the validity of the uterine muscle cell image by the ultrasound detection validity judgment value helps to improve the quality of the ultrasound imaging uterine muscle cell image, thereby improving the reliability of the uterine muscle cell image and ensuring the stability of the quality of the generated uterine muscle cell image.

[0078] Optionally, whether to perform ultrasound imaging parameter optimization is determined based on the liquid interference determination data. The specific process is as follows:

[0079] J1. Acquire corresponding fluid interference determination data based on the resting uterine muscle cell image. The fluid interference determination data includes the image average grayscale value and the image average echo intensity.

[0080] J2, comparing the liquid interference determination data with reference liquid determination data obtained from a preset database, where the reference liquid determination data includes a reference determination grayscale value and a reference determination echo intensity.

[0081] It should be explained that the reference liquid determination data is preset by a preset staff and stored in a preset database.

[0082] J3: If any liquid interference determination data is not less than the corresponding reference liquid determination data, the ultrasonic imaging parameters are optimized; otherwise, no additional processing is performed.

[0083] The specific steps for ultrasound imaging parameter optimization are as follows:

[0084] First, the difference between the liquid interference determination data and the corresponding reference liquid determination data is quantified to obtain a corresponding liquid interference difference quantification value.

[0085] The liquid interference determination data that is not less than the corresponding reference liquid determination data is subjected to difference quantification. Difference quantification means first performing a difference operation between the liquid interference determination data and the reference liquid determination data and performing a ratio operation on the reference liquid determination data.

[0086] Next, an imaging parameter optimization ratio group is obtained based on the liquid interference difference quantization value mapping. The imaging parameters are optimized and compensated according to the imaging parameter optimization ratio group to obtain the corresponding parameters to be imaged. The scan is re-performed based on the parameters to be imaged. The imaging parameter optimization ratio group includes the probe frequency adjustment ratio and the echo gain adjustment ratio, and the imaging parameters include the probe frequency and the echo gain.

[0087] Specifically, a mapping set of liquid interference difference quantification values ​​and imaging parameter optimization ratio groups is pre-constructed in a preset database, the real-time liquid interference difference quantification values ​​are input into the mapping set, and the corresponding imaging parameter optimization ratio group is output. The mapping set represents a collection of mapping relationships between liquid interference difference quantification values ​​and imaging parameter optimization ratio groups.

[0088] In addition, the optimization compensation operation represents a product operation of the imaging parameter optimization ratio group and the corresponding imaging parameters.

[0089] In this embodiment, the quantification value of the liquid interference difference is helpful to more accurately reflect the degree of interference of the liquid in the uterus on ultrasound imaging and provide a basis for optimization decision-making; at the same time, by mapping the optimization ratio group, it is helpful to dynamically adjust the imaging parameters according to the degree of interference and improve the imaging quality; and the optimized imaging parameters are helpful to better eliminate liquid interference to improve the clarity and accuracy of uterine muscle cell images; through the above process, liquid interference can be more effectively reduced during ultrasound examination, thereby optimizing the quality of uterine muscle cell images and ensuring the accuracy of uterine muscle cell image information and the reliability of corresponding tension analysis.

[0090] Optionally, the specific steps of performing tension feedback on the scar image domain of the resting uterine muscle cell image are as follows:

[0091] Step 1: Obtain color identification data of the scar image domain of the resting uterine muscle cell image, input the color identification data into the image elasticity quantization value model, and output the corresponding image elasticity quantization value, which is recorded as the resting image elasticity quantization value.

[0092] Step 2: Transmitting the resting image elasticity quantification value of the scar image domain and the ultrasound elasticity image of the resting uterine muscle cell image to a preset medical staff terminal.

[0093] In this embodiment, by inputting the color identification data of the scar image domain of the resting uterine muscle cell image into the image elasticity quantification value model, it is beneficial to obtain more accurate resting image elasticity quantification values, thereby facilitating more accurate analysis and quantification of the elastic characteristics of uterine muscle cells in the resting state, and thus providing more accurate data support for subsequent feedback; and by combining the elasticity quantification values ​​of the scar image domain with the ultrasound image, it is beneficial to more comprehensively and accurately monitor the elasticity status of resting uterine muscle cells, thereby helping medical staff to more efficiently evaluate the corresponding tension characteristics.

[0094] The specific method of obtaining the image elasticity quantization value model is as follows:

[0095] The first step is to obtain the color identification data corresponding to the uterine muscle cell image and perform data normalization processing. The color identification data includes color RGB value, color region area and elastic modulus.

[0096] It should be added that the uterine muscle cell image is loaded and its RGB value is obtained to obtain the corresponding color RGB value. Usually, the image can be read through an image processing library such as OpenCV and stored as a NumPy array; by using a preset threshold to extract a specific RGB value, by specifying the range of RGB values ​​to find out which areas in the uterine muscle cell image match the target color, the corresponding RGB area is extracted, and the number of pixels in the uterine muscle cell image is used to calculate the corresponding area to obtain the color area.

[0097] In the second step, the colors of the uterine muscle cell images are classified and numbered according to the color RGB values, and the corresponding color identification analysis parameters are obtained from the preset database. The color identification analysis parameters include the color identification analysis ratio and the color category analysis ratio. The color identification analysis ratio includes the RGB value analysis ratio, the regional area analysis ratio and the elastic modulus analysis ratio.

[0098] Specifically, the color identification analysis percentage represents the degree of influence of the color identification data on the initial quantized value of the image's elasticity. Each color identification data item has a unique mapping relationship with its corresponding color category analysis percentage, and the value range is between 0 and 1. For example, a mapping set is constructed between color identification data and preset color category analysis percentages. Real-time color RGB values, color region area, and elastic modulus are input into the mapping set to obtain the corresponding RGB value analysis percentage, region area analysis percentage, and elastic modulus analysis percentage, respectively representing the degree of influence of the color RGB value, color region area, and elastic modulus on the initial quantized value of the image's elasticity, with the sum of these three being 1.

[0099] Specifically, the color category analysis weight represents the degree of influence of each color's RGB value on the image elasticity quantization model. Each color's RGB value has a unique mapping relationship with its corresponding color category analysis weight, and the value range is between 0 and 1. For example, a mapping set is constructed between each color's RGB value and a preset color category analysis weight. Real-time color RGB values ​​are input into the mapping set to obtain the corresponding color category analysis weight, representing the degree of influence of each color's RGB value on the image elasticity quantization model. The sum of the color category analysis weights corresponding to each color's RGB value is 1.

[0100] In the third step, the color identification data is weighted and coupled with the corresponding color identification analysis ratio to obtain the initial quantitative value of the image elasticity.

[0101] In the fourth step, the initial quantized elasticity values ​​of each color RGB and the corresponding color category analysis weights are cumulatively summed and averaged to obtain an image elasticity quantization value model. The image elasticity quantization value model is used to quantify the tension characteristics of the corresponding image area through the elastic map of the ultrasonic elastic image.

[0102] The specific restriction expression of the image elasticity quantization value model is as follows:

[0103]

[0104] Where n represents the number of the color RGB category, n = 1, 2, ..., N, N represents the total number of color RGB categories, C_RGB n Indicates the color RGB value of the nth color RGB, CA n Indicates the color area of ​​the nth color RGB, A_EM n Indicates the elastic modulus of the nth color RGB, α_rgb indicates the proportion of RGB value analysis, β_area indicates the proportion of regional area analysis, and γ_ela indicates the proportion of elastic modulus analysis. It represents the color category analysis weight of the nth color RGB, and EQV represents the image elasticity quantization value.

[0105] In this embodiment, the algorithm combines color identification data and color identification analysis parameters for analysis to obtain the corresponding image elasticity quantization value, where the larger the color RGB value, the higher the corresponding elasticity may be, and the larger the corresponding tension feature data may be; when the color area is larger, the higher the proportion of the corresponding color RGB in the image, the larger the corresponding tension range may be, and the larger the corresponding image elasticity quantization value; when the elastic modulus is larger, the higher the elasticity of the corresponding image area, and the larger the corresponding image elasticity quantization value; through the analysis of the image elasticity quantization value, not only the tension characteristics of the corresponding image area are comprehensively quantified, but also the tension data of the uterine muscle cell image is more accurately fed back to the preset medical staff, thereby ensuring the accuracy of the uterine muscle cell tension analysis.

[0106] like Figure 3 FIG. 1 is a flowchart illustrating image tension performance determination in a method for analyzing uterine myocyte tension based on uterine myocyte activity images according to an embodiment of the present invention. The specific logic is as follows: when the tension analysis mode is the uterine contraction state analysis mode, the ultrasound imaging frequency is adjusted to the maximum imaging frequency. Then, image elasticity quantization values ​​within a preset time period are obtained and recorded as uterine contraction image elasticity quantization values. Then, based on the uterine contraction image elasticity quantization values ​​within the preset time period, a corresponding average uterine contraction image elasticity quantization value is obtained. The average uterine contraction image elasticity quantization value is compared with a reference uterine contraction image elasticity quantization value. If the average uterine contraction image elasticity quantization value is not less than the reference uterine contraction image elasticity quantization value, the real-time requirement level for uterine contraction state tension monitoring is recorded as a first-level requirement; otherwise, a second-level requirement is recorded. Then, corresponding imaging frequency adjustment measures are taken based on the determination result of the real-time requirement level for uterine contraction state tension monitoring. The uterine contraction myocyte image is divided into corresponding uterine contraction scar image regions for scar priority feedback. The above process not only improves the real-time and sensitivity of uterine contraction monitoring, but also facilitates timely detection of potential scar issues by prioritizing feedback on scar regions, thereby helping pre-emptive medical personnel make more accurate decisions.

[0107] It should be understood that the image tension performance is determined based on the image of the contracting uterine myocytes, and corresponding tension analysis optimization measures are taken to obtain the corresponding contracting uterine myocyte tension analysis results. The specific steps are as follows:

[0108] H1, when the tension analysis mode is the uterine contraction state analysis mode, the ultrasound imaging frequency is adjusted to the maximum imaging frequency; the maximum imaging frequency represents the highest imaging frequency supported by the current ultrasound imaging device.

[0109] H2, obtaining the color identification data of the uterine contraction myocyte image within a preset time period and inputting it into the image elasticity quantization value model, and outputting the corresponding image elasticity quantization value, which is recorded as the uterine contraction image elasticity quantization value.

[0110] H3, perform mean calculation on the quantified values ​​of the uterine contraction image elasticity within the preset time period to obtain the corresponding average quantified value of the uterine contraction image elasticity.

[0111] H4, compare the average uterine contraction image elasticity quantification value with the reference uterine contraction image elasticity quantification value obtained from the preset database, take corresponding image monitoring and early warning measures and obtain the corresponding judgment result. If the average uterine contraction image elasticity quantification value is not less than the reference uterine contraction image elasticity quantification value, then the real-time demand level of uterine contraction state tension monitoring is recorded as a first-level demand, otherwise it is a second-level demand.

[0112] Specifically, the reference uterine contraction image elasticity quantification value is preset by a preset staff and stored in a preset database.

[0113] Secondly, the higher the level of real-time demand for uterine contraction state tension monitoring, the lower the real-time demand for uterine contraction state tension monitoring. For example, the first-level demand is higher than the second-level demand.

[0114] H5: Take corresponding imaging frequency adjustment measures based on the determination result of the real-time demand level of uterine contraction state tension monitoring.

[0115] H6, divide the uterine contraction myocyte image to obtain the corresponding uterine contraction scar image domain, and perform scar priority feedback on the uterine contraction scar image domain. Scar priority feedback means giving priority feedback to the analysis result of the uterine contraction scar image domain.

[0116] In this embodiment, by adjusting the ultrasound imaging frequency to the maximum imaging frequency, it is helpful to ensure that the imaging is more accurate under high tension conditions and can more clearly present the details of the uterine muscle cells; and through the combination of color identification and elasticity quantification value, it is helpful to more accurately quantify the tension of the muscle cells during uterine contraction, thereby providing a strong basis for analysis; at the same time, automated early warning measures can respond to different tension states in a timely manner, improve the real-time and sensitivity of uterine contraction monitoring, and give priority to feedback on scar areas to help timely detect possible scar problems, especially during uterine contraction. The timely feedback of scar formation or changes helps pre-set medical staff to make more accurate decisions based on this.

[0117] Optionally, the specific process of taking corresponding image monitoring and early warning measures is as follows: when the quantified value of the elasticity of the contraction image is not less than the quantified value of the elasticity of the reference contraction image, a first-level warning is issued. The first-level warning indicates that the object to be analyzed and the preset medical staff are prompted by sound that the uterine contraction tension changes are abnormal; when the number of first-level warnings exceeds the preset number, a second-level warning is issued. The second-level warning includes a high-frequency sound warning and a display warning.

[0118] In this embodiment, the preset medical staff can be quickly reminded to pay attention to the drastic changes in uterine contraction tension through sound prompts to ensure timely processing, and provide clear sound feedback to the preset medical staff so that they can quickly realize the changes in uterine muscle cell tension and make timely judgments; in addition, the dual reminders of high-frequency sound warnings and display warnings can more effectively attract the attention of the preset medical staff and avoid ignoring the drastic changes in uterine contraction status. At the same time, the combination of sound and visual prompts ensures that the preset medical staff can receive warnings through different senses and adapt to different working environments and medical scenarios.

[0119] Optionally, corresponding imaging frequency adjustment measures are taken according to the determination result of the real-time requirement level of uterine contraction state tension monitoring. The specific process is as follows:

[0120] V1. If there are a continuous preset number of preset time periods with a real-time demand level of uterine contraction state tension monitoring of the second-level demand, the difference between the average uterine contraction image elasticity quantization value of all preset time periods and the corresponding reference uterine contraction image elasticity quantization value is quantified, and then the mean operation is performed to obtain the corresponding average difference degree value.

[0121] The difference quantization indicates that the difference calculation result of the average uterine contraction image elasticity quantization value and the corresponding reference uterine contraction image elasticity quantization value is calculated by ratio calculation with the reference uterine contraction image elasticity quantization value.

[0122] V2, based on the average difference degree value mapping, obtains the imaging frequency reduction multiple, and adjusts the ultrasound imaging frequency according to the imaging frequency reduction multiple.

[0123] Specifically, adjusting the ultrasonic imaging frequency by reducing the imaging frequency multiple means performing ultrasonic imaging by multiplying the imaging frequency reduction multiple by the ultrasonic imaging frequency to obtain a corresponding ultrasonic imaging frequency.

[0124] V3: If the real-time demand level of uterine contraction state tension monitoring in the preset time period is level one, the ultrasound imaging frequency is adjusted back to the maximum imaging frequency.

[0125] In this embodiment, the highest imaging frequency can present the details of the uterine contraction status more clearly and in real time, avoiding missing any key pathological changes and improving the accuracy of diagnosis; and automatically adjusting the imaging parameters according to changes in different demand levels is conducive to adapting to different clinical scenarios; dynamically adjusting the imaging frequency according to the real-time demand level of uterine contraction tension monitoring can not only ensure the quality and real-time nature of the uterine contraction image, but also save computing resources to improve equipment utilization efficiency.

[0126] Optionally, scar priority feedback is performed on the uterine contraction scar image domain, and the specific process is as follows: the color identification data of the uterine contraction scar image domain is input into the image elasticity quantization value model, and the corresponding image elasticity quantization value is output, which is recorded as the uterine contraction scar image elasticity quantization value; the uterine contraction scar elasticity quantization value sequence is obtained by statistically analyzing the uterine contraction scar image elasticity quantization values ​​output within a preset time period in chronological order; the uterine contraction scar elasticity quantization value sequence corresponding to the uterine contraction uterine muscle cell image within the preset time period is preferentially transmitted to the preset medical staff terminal.

[0127] In this embodiment, the color identification data is first converted into corresponding elasticity quantization values, which can more accurately reflect the actual condition of the uterine contraction scar and ensure more accurate monitoring; and the feedback on the scar image domain is quantified, so that medical staff can more clearly see the changing trend of the uterine contraction scar and make timely judgments; at the same time, the sorted elasticity quantization value sequence helps to dynamically track the changes of the uterine contraction scar in the time series; by preferentially transmitting the elasticity quantization value sequence of the scar image, it is ensured that medical staff receive uterine contraction scar-related data first in real-time monitoring, thereby providing more real-time data support for the subsequent decision-making of the preset medical staff.

[0128] In summary, the embodiment of the present invention obtains the tension analysis mode of the object to be analyzed. If it is a resting state analysis mode, a resting uterine muscle cell image is obtained and the ultrasound scanning effectiveness evaluation is performed based on this to determine whether to rescan. The corresponding uterine muscle cell tension analysis result is obtained based on the judgment result. If it is a contraction state analysis mode, a contraction uterine muscle cell image is obtained and the image tension performance is determined based on this. Then, tension analysis optimization measures are taken to obtain the contraction uterine muscle cell tension analysis result. Finally, the uterine muscle cell tension analysis result is transmitted to the medical staff terminal, so as to provide the medical staff with more detailed tension analysis results, thereby improving the reliability of the uterine muscle cell tension analysis result, and effectively solving the problem of unstable reliability of the uterine muscle cell tension analysis result due to differences in ultrasound probe scanning in the prior art.

[0129] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0130] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0133] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0134] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for analyzing uterine muscle cell tension based on uterine muscle cell activity images, characterized in that: The following steps are involved: An initial determination image is obtained through an initial scan, and a tension analysis state determination is performed based on the initial determination image to select a tension analysis mode for the object to be analyzed, wherein the tension analysis mode includes a resting state analysis mode and a uterine contraction state analysis mode; If the tension analysis mode is the resting state analysis mode, an ultrasonic elastography scan is performed to obtain a corresponding resting uterine myocyte image, and an ultrasonic scan validity assessment is performed based on the resting uterine myocyte image to determine whether to rescan. If a rescan is performed, an ultrasonic scan validity assessment is performed on the resting uterine myocyte image obtained by the rescan, and a resting uterine myocyte tension analysis result is obtained based on the resting uterine myocyte image that meets the scan validity condition. Otherwise, the uterine myocyte tension analysis result is obtained directly based on the resting uterine myocyte image. If the tension analysis mode is the uterine contraction state analysis mode, an ultrasonic elastography scan is performed to obtain a corresponding uterine contraction myocyte image, image tension performance is determined based on the uterine contraction myocyte image, and corresponding tension analysis optimization measures are taken to obtain a corresponding uterine contraction myocyte tension analysis result; The obtained uterine muscle cell tension analysis results are transmitted to a preset medical staff terminal, wherein the uterine muscle cell tension analysis results include resting uterine muscle cell tension analysis results and contraction uterine muscle cell tension analysis results, and the uterine muscle cell tension analysis results represent a collection of uterine muscle cell images, elasticity diagrams, and uterine muscle cell tension analysis data.

2. The method for analyzing uterine muscle cell tension based on uterine muscle cell activity images according to claim 1, wherein: The specific steps of evaluating the effectiveness of ultrasound scanning based on the resting uterine muscle cell image to determine whether to rescan are as follows: K1, performing ultrasound scanning effectiveness evaluation on the acquired resting uterine muscle cell image to obtain an ultrasound detection effectiveness determination value, wherein the ultrasound detection effectiveness determination value is used to determine the effectiveness of the current ultrasound scan imaging uterine muscle cell image; K2: If the ultrasound test validity judgment value is not less than the ultrasound test evaluation threshold obtained from the preset database, it indicates that the resting uterine muscle cell image is qualified and K4 is executed. If the ultrasound test validity judgment value is less than the ultrasound test evaluation threshold obtained from the preset database, the preset medical staff is prompted to change the scanning area for rescanning and K3 is executed; K3, based on the liquid interference determination data, determines whether to optimize the ultrasound imaging parameters, obtains a qualified resting uterine muscle cell image and executes K4; K4, divides the resting uterine muscle cell image according to the color identification of the qualified resting uterine muscle cell image to obtain the scar map domain and the non-scar map domain, and performs tension feedback on the scar map domain of the resting uterine muscle cell image, wherein the tension feedback means feeding back the tension condition of the scar map domain in the resting uterine muscle cell image to the preset medical staff.

3. The method for analyzing uterine muscle cell tension based on uterine muscle cell activity images according to claim 2, wherein: The specific process of obtaining the ultrasonic detection validity judgment value is as follows: Acquiring ultrasound detection and evaluation data of resting uterine muscle cell images, wherein the ultrasound detection and evaluation data includes echo intensity, image grayscale value, elastic modulus, attenuation coefficient, and signal-to-noise ratio; Obtaining ultrasound evaluation parameters from a preset database, wherein the ultrasound evaluation parameters include ultrasound evaluation reference values ​​and ultrasound evaluation influence weights, wherein the ultrasound evaluation reference values ​​include an echo intensity reference range, a grayscale value reference range, a minimum elastic modulus limit value, a minimum attenuation coefficient critical value, and a minimum signal-to-noise ratio division value, and the ultrasound evaluation influence weights include an echo intensity influence weight, a grayscale value influence weight, an elastic modulus influence weight, an attenuation coefficient influence weight, and a signal-to-noise ratio influence weight; Performing a range deviation quantification operation on the echo intensity and the corresponding echo intensity reference range to obtain an echo intensity deviation; Perform range deviation quantization operation on the grayscale value of the image and the corresponding grayscale value reference range to obtain the grayscale value deviation; Performing a quantitative calculation on the degree of deviation between the elastic modulus, attenuation coefficient and signal-to-noise ratio and the corresponding ultrasonic evaluation reference value to obtain corresponding elastic modulus deviation degree value, attenuation coefficient deviation degree value and signal-to-noise ratio deviation degree value; By weighting and coupling the echo intensity deviation, grayscale value deviation, elastic modulus deviation, attenuation coefficient deviation, and signal-to-noise ratio deviation with the corresponding ultrasound assessment influence weights, an ultrasound detection validity judgment value is obtained. The ultrasound detection validity judgment value is used to determine the reliability of the resting uterine muscle cell image obtained by ultrasound elastography.

4. The method for analyzing uterine muscle cell tension based on uterine muscle cell activity images according to claim 2, wherein: The specific process of determining whether to optimize ultrasonic imaging parameters based on liquid interference determination data is as follows: Acquiring corresponding liquid interference determination data based on the resting uterine muscle cell image, wherein the liquid interference determination data includes an image average gray value and an image average echo intensity; Comparing the liquid interference determination data with reference liquid determination data obtained from a preset database, the reference liquid determination data including a reference determination grayscale value and a reference determination echo intensity; If any liquid interference determination data is not less than the corresponding reference liquid determination data, the ultrasonic imaging parameters are optimized, otherwise no additional processing is performed; The specific steps of optimizing the ultrasound imaging parameters are as follows: quantifying the difference between the liquid interference determination data and the corresponding reference liquid determination data to obtain a corresponding liquid interference difference quantification value; An imaging parameter optimization ratio group is obtained based on the mapping of the quantitative value of the liquid interference difference. The imaging parameters are optimized and compensated according to the imaging parameter optimization ratio group to obtain the corresponding parameters to be imaged. The scanning is re-performed based on the parameters to be imaged. The imaging parameter optimization ratio group includes a probe frequency adjustment ratio and an echo gain adjustment ratio. The imaging parameters include a probe frequency and an echo gain.

5. The method for analyzing uterine muscle cell tension based on uterine muscle cell activity images according to claim 2, wherein: The specific steps of performing tension feedback on the scar image domain of the resting uterine muscle cell image are as follows: Step 1: obtaining color identification data of the scar image domain of the resting uterine muscle cell image, inputting the color identification data into the image elasticity quantization value model, and outputting the corresponding image elasticity quantization value, which is recorded as the resting image elasticity quantization value; Step 2: Transmitting the resting image elasticity quantification value of the scar image domain and the ultrasound elasticity image of the resting uterine muscle cell image to a preset medical staff terminal.

6. The method for analyzing uterine muscle cell tension based on uterine muscle cell activity images according to claim 5, characterized in that: The specific method of obtaining the image elasticity quantization value model is as follows: Acquiring color identification data corresponding to the uterine muscle cell image and performing data normalization processing, wherein the color identification data includes color RGB value, color region area and elastic modulus; Classify and number the colors of the uterine muscle cell images according to the color RGB values, and obtain corresponding color identification analysis parameters from a preset database, wherein the color identification analysis parameters include a color identification analysis ratio and a color category analysis ratio, and the color identification analysis ratio includes an RGB value analysis ratio, a regional area analysis ratio, and an elastic modulus analysis ratio; The color identification data is weighted and coupled with the corresponding color identification analysis ratio to obtain the initial quantitative value of the image elasticity; The initial quantized elasticity values ​​of each color RGB and the corresponding color category analysis proportions are cumulatively summed and then averaged to obtain an image elasticity quantization value model. The image elasticity quantization value model is used to quantify the tension characteristics of the corresponding image area through the elastic map of the ultrasonic elastic image.

7. The method for analyzing uterine muscle cell tension based on uterine muscle cell activity images according to claim 1, wherein: The image tension performance determination is performed based on the uterine contraction myocyte image, and corresponding tension analysis optimization measures are taken to obtain corresponding uterine contraction myocyte tension analysis results. The specific steps are as follows: H1, when the tension analysis mode is the uterine contraction state analysis mode, adjust the ultrasound imaging frequency to the maximum imaging frequency; H2, obtaining the color identification data of the uterine contraction myocyte image within a preset time period and inputting it into the image elasticity quantization value model, and outputting the corresponding image elasticity quantization value, which is recorded as the uterine contraction image elasticity quantization value; H3, performing mean calculation on the quantified values ​​of the uterine contraction image elasticity within the preset time period to obtain the corresponding average quantified value of the uterine contraction image elasticity; H4: Compare the average uterine contraction image elasticity quantification value with the reference uterine contraction image elasticity quantification value obtained from the preset database, take corresponding image monitoring and early warning measures, and obtain the corresponding judgment result. If the average uterine contraction image elasticity quantification value is not less than the reference uterine contraction image elasticity quantification value, then the real-time requirement level of uterine contraction state tension monitoring is recorded as level one, otherwise it is recorded as level two; H5: Take corresponding imaging frequency adjustment measures based on the determination result of the real-time demand level of uterine contraction tension monitoring; H6, dividing the uterine contraction myocyte image to obtain the corresponding uterine contraction scar image domain, and performing scar priority feedback on the uterine contraction scar image domain, wherein the scar priority feedback indicates that the analysis result of the uterine contraction scar image domain is given priority feedback.

8. The method for analyzing uterine muscle cell tension based on uterine muscle cell activity images according to claim 7, wherein: The specific process of taking corresponding image monitoring and early warning measures is as follows: When the quantified value of the elasticity of the uterine contraction image is not less than the quantified value of the elasticity of the reference uterine contraction image, a first-level warning is issued, which means that the subject to be analyzed and the preset medical staff are notified of abnormal changes in uterine contraction tension through sound; When the number of first-level warnings exceeds a preset number, a second-level warning is issued, which includes a high-frequency sound warning and a display warning.

9. The method for analyzing uterine muscle cell tension based on uterine muscle cell activity images according to claim 7, wherein: The specific process of taking corresponding imaging frequency adjustment measures according to the result of determining the real-time requirement level of uterine contraction state tension monitoring is as follows: If there are a continuous number of preset time periods with a real-time demand level of uterine contraction state tension monitoring of the second level demand, the difference between the average uterine contraction image elasticity quantization value of all preset time periods and the corresponding reference uterine contraction image elasticity quantization value is quantified, and then the average operation is performed to obtain the corresponding average difference degree value; Based on the average difference degree value mapping, an imaging frequency reduction multiple is obtained, and the ultrasound imaging frequency is adjusted according to the imaging frequency reduction multiple; If the real-time demand level for uterine contraction state tension monitoring during the preset time period is level one, the ultrasound imaging frequency is adjusted back to the maximum imaging frequency.

10. The method for analyzing uterine muscle cell tension based on uterine muscle cell activity images according to claim 7, wherein: The specific process of performing scar-priority feedback on the uterine contraction scar image domain is as follows: Inputting the color identification data of the uterine contraction scar image domain into the image elasticity quantization value model, and outputting the corresponding image elasticity quantization value, which is recorded as the uterine contraction scar image elasticity quantization value; A sequence of uterine contraction scar elasticity quantification values ​​is obtained by statistically analyzing the uterine contraction scar image elasticity quantification values ​​output within a preset time period in chronological order; The uterine contraction scar elasticity quantification value sequence corresponding to the uterine contraction muscle cell image within the preset time period is preferentially transmitted to the preset medical staff terminal.

Images