Gravel strategy determination method and device, electronic equipment and readable storage medium

Abstract

The invention discloses a lithotripsy strategy determination method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining a medical image of a target region, and determining a target calculus region in the target region based on the medical image; the target calculus area is analyzed, and calculus parameters are determined; and determining a lithotripsy strategy according to the lithotripsy parameters. According to the method, the target calculus area in the medical image is firstly determined, then the calculus parameters are determined based on the target calculus area, and the lithotripsy strategy is specifically determined according to the calculus parameters, so that the accuracy of the lithotripsy strategy can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of medical image processing, and in particular to a lithotripsy strategy determination method, device, electronic device, and readable storage medium. Background Art

[0002] With the development of AI imaging technology, image scanning has become a common auxiliary diagnosis and treatment method in modern medical diagnosis and treatment. Medical imaging can provide doctors with more and more intuitive internal information of the human body, thereby speeding up the doctor's diagnosis of the patient's condition.

[0003] Urinary stones are a common urinary tract disease. Stones can be found anywhere in the kidneys, bladder, ureters, and urethra. Currently, stone removal relies on the physician's clinical experience to assess stone volume and density. This subjective experience, coupled with the physician's discretion in selecting the appropriate laser pulse energy for lithotripsy, can lead to inaccurate lithotripsy strategies. Summary of the Invention

[0004] The embodiments of the present application provide a lithotripsy strategy determination method, device, electronic device, and readable storage medium, which can improve the accuracy of the lithotripsy strategy.

[0005] In a first aspect, an embodiment of the present application provides a method for determining a stone crushing strategy, the method comprising:

[0006] Acquiring a medical image of a target area, and determining a target stone area in the target area based on the medical image;

[0007] Analyzing the target stone area to determine stone parameters;

[0008] According to the stone parameters, a lithotripsy strategy is determined.

[0009] Optionally, the stone parameters include stone density and stone volume, and the analyzing the stone area to determine the stone parameters includes:

[0010] determining the stone density according to the pixel density distribution corresponding to the target stone area;

[0011] The stone volume is determined according to the scale parameter corresponding to the medical image and the size parameter corresponding to the target stone area.

[0012] Optionally, determining a lithotripsy strategy according to the stone parameters includes:

[0013] determining a reference lithotripsy energy according to the stone density and the stone volume;

[0014] determining a target lithotripsy time based on the reference lithotripsy energy, the stone volume, and the stone density;

[0015] A lithotripsy strategy is determined according to the reference lithotripsy energy and the target lithotripsy duration.

[0016] Optionally, the method further includes:

[0017] According to preset constraints, the reference lithotripsy energy is adjusted to obtain a target lithotripsy energy;

[0018] A lithotripsy strategy is determined according to the target lithotripsy energy and the target lithotripsy duration.

[0019] Optionally, determining a target stone region in the target region based on the medical image includes:

[0020] Identifying the medical image to determine a reference stone region in the target region;

[0021] performing noise reduction processing on the reference stone region to obtain a noise-reduced stone region;

[0022] The pixel density of the noise-reduced stone area is corrected to obtain a target stone area.

[0023] Optionally, performing noise reduction processing on the reference stone region to obtain the noise-reduced stone region includes:

[0024] Acquire a target voxel point set whose pixel density in the reference stone area is less than a preset density threshold;

[0025] determining the region formed by the target voxel point set as the noise reduction region in the reference stone region;

[0026] The noise reduction area is subjected to noise reduction processing to obtain a noise reduction stone area.

[0027] Optionally, performing pixel density correction processing on the noise reduction stone area to obtain a target stone area includes:

[0028] Obtaining the pixel density corresponding to each voxel point in the denoised stone area, and calculating the pixel density change rate between adjacent voxel points;

[0029] Determine the region composed of voxel points whose pixel density change rate is greater than a preset change rate threshold as the target correction region in the noise reduction stone region;

[0030] Pixel density correction processing is performed on each voxel point in the target correction area to obtain a target stone area.

[0031] Optionally, performing pixel density correction processing on each voxel point in the target correction area to obtain a target stone area includes:

[0032] For each voxel point in the target correction area, obtaining a reference pixel density of each reference voxel point within a preset range of the voxel point;

[0033] Counting the distribution probability of the reference pixel density of each reference voxel point within the preset range of the voxel point;

[0034] Determine the reference pixel density with the maximum distribution probability as the target pixel density, and correct the pixel density corresponding to the voxel point to the target pixel density;

[0035] Until the pixel density correction is completed for each voxel point in the target correction area, the target stone area is obtained.

[0036] In a second aspect, an embodiment of the present application provides a device for determining a stone crushing strategy, the device comprising:

[0037] an acquiring unit, configured to acquire a medical image of a target area, and determine a target stone area in the target area based on the medical image;

[0038] a first determining unit, configured to analyze the target stone area and determine stone parameters;

[0039] The second determining unit is configured to determine a lithotripsy strategy according to the stone parameters.

[0040] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory storing a computer program; a processor loading the computer program from the memory to execute the steps of any one of the methods for determining a lithotripsy strategy provided in the embodiments of the present application.

[0041] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which is suitable for loading by a processor to execute the steps of any one of the methods for determining a lithotripsy strategy provided in the embodiments of the present application.

[0042] In a fifth aspect, an embodiment of the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any one of the methods for determining a stone crushing strategy provided in the embodiments of the present application.

[0043] Using the solution of the embodiment of the application, a medical image of the target area is acquired, and a target stone region within the target area is determined based on the medical image; the target stone region is analyzed to determine stone parameters; and a lithotripsy strategy is determined based on the stone parameters. By first determining the target stone region in the medical image, then determining stone parameters based on the target stone region, and then determining a lithotripsy strategy specifically based on the stone parameters, the accuracy of the lithotripsy strategy can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 This is a flowchart of the first embodiment of the method for determining a stone crushing strategy provided by the present application;

[0046] Figure 2 This is a flow chart of a second embodiment of the method for determining a stone crushing strategy provided by the present application;

[0047] Figure 3 This is a flowchart of the third embodiment of the method for determining a stone crushing strategy provided by the present application;

[0048] Figure 4 This is a flowchart of a fourth embodiment of the method for determining a lithotripsy strategy provided by the present application;

[0049] Figure 5 This is a flowchart of the fifth embodiment of the method for determining a stone crushing strategy provided by the present application;

[0050] Figure 6 is a schematic diagram of the noise reduction area in the reference stone area provided in this application;

[0051] Figure 7 This is a flowchart of a sixth embodiment of the method for determining a stone crushing strategy provided by the present application;

[0052] Figure 8 is a structural diagram of a device for determining a lithotripsy strategy provided in an embodiment of the present application;

[0053] Figure 9 It is a structural diagram of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application. At the same time, in the description of the embodiments of the present application, the terms "first", "second", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more features. In the description of the embodiments of the present application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0055] Embodiments of the present application provide a method, device, electronic device, and readable storage medium for determining a stone crushing strategy.

[0056] Specifically, this embodiment will be described from the perspective of an electronic device. The electronic device can be integrated into a lithotripsy strategy determination device, that is, the lithotripsy strategy determination method of the embodiment of the present application can be executed by the electronic device.

[0057] The method for determining a gravel strategy provided in the embodiments of the present application can be applied to electronic devices, which may be smart terminals, PC terminals, mobile terminals, and other devices.

[0058] The following is a detailed description of each step in conjunction with the accompanying drawings. In this embodiment, an electronic device is used as an example. It should be noted that the order in which the following embodiments are described does not limit the preferred order of the embodiments. Although the flowcharts illustrate a logical order, in some cases, the steps shown or described may be performed in a different order than that shown in the accompanying drawings.

[0059] Please refer to Figure 1 , a first embodiment of the method for determining a stone crushing strategy is proposed, and the first embodiment includes the following steps:

[0060] Step 101, obtaining a medical image of a target area, and determining a target stone area in the target area based on the medical image;

[0061] Step 102, analyzing the target stone area to determine stone parameters;

[0062] Step 103: Determine a lithotripsy strategy based on the stone parameters.

[0063] In this embodiment, when performing a medical imaging examination on a patient, the electronic device obtains a medical image of the patient's target area, and determines a reference stone area in the target area based on the medical image of the target area; since the reference stone area is obtained based on the pixel density of each voxel point in the medical image, the pixel density of a voxel point is the average density of all pixels in the voxel point, so the reference stone area may include some areas that are unclear in the medical image, which may be physiological tissues or stones. Therefore, the electronic device performs pixel density correction processing on the reference stone area, determines the physiological tissue area contained in the reference stone area, and then eliminates the physiological tissue area in the reference stone area to determine the target stone area in the target physiological tissue. The electronic device analyzes the target stone area, determines the stone parameters, and determines the lithotripsy strategy based on the stone parameters.

[0064] Specifically, each step is described in detail below:

[0065] Step 101, obtaining a medical image of a target area, and determining a target stone area in the target area based on the medical image;

[0066] In this step, when performing a medical imaging examination on a patient, the electronic device obtains a medical image of the patient's target area and determines a reference stone region within the target area based on the medical image of the target area. Optionally, the electronic device determines the reference stone region based on the CT value of each voxel point in the medical image, comparing the CT value of each voxel point with a preset CT threshold. Optionally, the electronic device inputs the medical image into a pre-created recognition model and uses the recognition model to identify the reference stone region within the medical image; wherein the recognition model is pre-trained using training samples of multiple lesions in multiple different physiological tissues.

[0067] Since the reference stone area is a preliminarily determined stone area, it may include some areas that are unclear in medical images. These areas may be physiological tissues or stones. Therefore, the electronic device performs pixel density correction processing on the reference stone area, determines the physiological tissue area contained in the reference stone area, removes the physiological tissue area from the reference stone area, and then determines the target stone area in the target physiological tissue.

[0068] Step 102: Analyze the target stone area to determine stone parameters.

[0069] In this step, after determining the target stone area, the electronic device determines the stone volume based on the target stone area. Specifically, the electronic device can assume that the stone shape is a regular geometric body (such as a sphere, ellipsoid, cube, etc.) to determine the stone's size parameters. For stones with complex shapes, three-dimensional image processing software (such as ITK, 3DSlicer, etc.) can be used to calculate the stone's size parameters by performing three-dimensional reconstruction and calculating the number of voxels within the stone area. The electronic device can automatically identify the stone's density parameters based on a deep learning model of medical imaging.

[0070] Step 102: Determine a lithotripsy strategy based on the stone parameters.

[0071] In this step, the electronic device determines the lithotripsy strategy corresponding to the stone parameters based on the stone parameters and a preset mapping relationship. The lithotripsy strategy includes lithotripsy energy and lithotripsy time.

[0072] The electronic device of this embodiment acquires a medical image of a target area, determines a target stone region within the target area based on the medical image, analyzes the target stone region to determine stone parameters, and determines a lithotripsy strategy based on the stone parameters. By first determining the target stone region in the medical image, then determining stone parameters based on the target stone region, and then determining a lithotripsy strategy specifically based on the stone parameters, the accuracy of the lithotripsy strategy can be improved.

[0073] Further, refer to Figure 2 A second embodiment of the lithotripsy strategy determination method is proposed. The difference between the second embodiment and the first embodiment is that the stone parameters include stone density and stone volume. The analysis of the stone area to determine the stone parameters includes:

[0074] Step 1021, determining the stone density according to the pixel density distribution corresponding to the target stone area;

[0075] In this step, the electronic device determines the stone density based on the pixel density distribution corresponding to the target stone area.

[0076] Optionally, the electronic device determines the pixel density distribution corresponding to the target stone area by counting the pixel density of all voxel points in the target stone area, and determines the pixel density with the highest distribution probability as the target pixel density, and then determines the stone density corresponding to the target stone area based on the target pixel density and the mapping relationship between the preset pixel density and the stone density; for example: the target stone area includes 100 voxel points, of which 30 voxel points have a pixel density of 15, and 70 voxel points have a pixel density of 20, that is, the pixel density distribution is: the voxel points with a pixel density of 15 account for 30%, and the voxel points with a pixel density of 20 account for 70%. At this time, the pixel density of 20 is determined as the target pixel density, and the stone density corresponding to the pixel density of 20 is found in the mapping relationship between the preset pixel density and the stone density, and then the stone density corresponding to the target stone area is determined.

[0077] Optionally, the electronic device determines the pixel density distribution corresponding to the target stone area by counting the pixel densities of all voxels in the target stone area, averages the pixel densities of all voxels to determine the target pixel density, and then determines the stone density corresponding to the target stone area based on the target pixel density and a mapping relationship between a preset pixel density and stone density. For example, the target stone area includes 100 voxels, of which 30 have a pixel density of 15 and 70 have a pixel density of 20. At this time, the pixel densities of all voxels are averaged, that is, target pixel density = 1 / 100*(20*70+15*30) = 18.5. The stone density corresponding to the target pixel density of 18.5 is found in the mapping relationship between the preset pixel density and stone density, thereby determining the stone density corresponding to the target stone area.

[0078] Step 1022: Determine the stone volume according to the scale parameter corresponding to the medical image and the size parameter corresponding to the target stone area.

[0079] In this step, the electronic device obtains the scale parameters corresponding to the medical image, and determines the stone volume based on the scale parameters and the size parameters corresponding to the target stone area; specifically, the target stone area represents a stone, and the size parameters corresponding to the target stone area are the reference volume in the medical image. By combining the reference volume and the scale parameters corresponding to the medical image, the actual volume of the stone can be calculated.

[0080] The electronic device of this embodiment determines the stone density based on the pixel density distribution corresponding to the target stone region, and determines the stone volume based on the scale parameter corresponding to the medical image and the size parameter corresponding to the target stone region. This can improve the accuracy of the determined stone density and stone volume, thereby helping to improve the accuracy of the subsequent lithotripsy strategy.

[0081] Further, refer to Figure 3 A third embodiment of the lithotripsy strategy determination method is proposed. The third embodiment differs from the first embodiment and the second embodiment in that the lithotripsy strategy is determined according to the stone parameters, including:

[0082] Step 1031, determining a reference lithotripsy energy according to the stone density and the stone volume;

[0083] In this step, the electronic device determines the reference lithotripsy energy based on the stone density and stone volume; specifically, the electronic device stores in advance the mapping relationship between stone density and lithotripsy energy, as well as the mapping relationship between stone volume and lithotripsy energy. After determining the stone density and stone volume, the electronic device can determine the first lithotripsy energy based on the mapping relationship between stone density and lithotripsy energy, and determine the second lithotripsy energy based on the mapping relationship between stone volume and lithotripsy energy, and then select the larger of the first lithotripsy energy and the second lithotripsy energy as the reference lithotripsy energy.

[0084] Among them, when the electronic device determines the stone density based on the pixel density distribution corresponding to the target stone area, it divides the target stone area into three areas of high density, medium density and low density according to preset rules, or divides the target stone area into two areas of high density and low density, and at the same time determines the stone volume corresponding to each density area, and determines the reference stone crushing energy corresponding to each divided density area respectively.

[0085] Step 1032: determining a target lithotripsy time based on the reference lithotripsy energy, the stone volume, and the stone density;

[0086] In this step, the electronic device determines a target lithotripsy time based on the reference lithotripsy energy, stone volume, and stone density. Specifically, the electronic device divides the target stone area into three areas: high-density area, medium-density area, and low-density area, according to a preset rule and the stone density of the target stone area. Alternatively, the electronic device divides the target stone area into two areas: high-density area and low-density area. For each density area, the electronic device determines the target lithotripsy time based on the reference lithotripsy energy and stone volume corresponding to that density area.

[0087] Step 1033: Determine a lithotripsy strategy according to the reference lithotripsy energy and the target lithotripsy duration.

[0088] In this step, the electronic device determines the lithotripsy strategy based on the reference lithotripsy energy and the target lithotripsy duration. Specifically, if the high-density area is large, the high-density area will be treated with high power for a short time (in divided time periods); if the low-density area is directly treated with low power. If the high-density area is small, the high-density area will be treated with high power; if the low-density area is low power. If the stone is small and not high-density, high power for a short time will be used. If the stone is large and not high-density, low power for a long time will be used.

[0089] Furthermore, the method further comprises:

[0090] Step a, adjusting the reference lithotripsy energy according to preset constraints to obtain a target lithotripsy energy;

[0091] Step b: determining a lithotripsy strategy according to the target lithotripsy energy and the target lithotripsy duration.

[0092] In step a and step b, the electronic device adjusts the reference lithotripsy energy according to preset constraints to obtain a target lithotripsy energy, and then determines a lithotripsy strategy according to the target lithotripsy energy and target lithotripsy duration.

[0093] Specifically, the preset constraint is the lithotripsy energy with the lowest energy or least harmful effect on the human body. The electronic device adjusts the reference lithotripsy energy based on this constraint to obtain a target lithotripsy energy. Adjusting the reference lithotripsy energy based on the lowest energy or least harmful effect on the human body can reduce the damage caused by the lithotripsy energy.

[0094] Specifically, the preset constraints are the lithotripsy energy that minimizes the energy or damage to different physiological tissues in the human body. Physiological tissues that may develop stones in a patient include the kidneys, gallbladder, urethra, bladder, and pancreas. Different physiological tissues have different tolerances to lithotripsy energy. Therefore, the electronic device first determines the physiological tissue corresponding to the medical image and then adjusts the reference lithotripsy energy based on the lithotripsy energy that minimizes the energy or damage to the physiological tissue to obtain the target lithotripsy energy. This can improve the accuracy of the determined lithotripsy energy while reducing the damage to physiological tissues caused by the lithotripsy energy.

[0095] The electronic device of this embodiment determines a reference lithotripsy energy based on the stone density and the stone volume; determines a target lithotripsy duration based on the reference lithotripsy energy, the stone volume, and the stone density; and determines a lithotripsy strategy based on the reference lithotripsy energy and the target lithotripsy duration. The lithotripsy strategy can be determined based on accurate stone density and stone volume, thereby improving the accuracy of the determined lithotripsy strategy.

[0096] Further, refer to Figure 4A fourth embodiment of the lithotripsy strategy determination method is proposed. The fourth embodiment differs from the first to third embodiments in that determining the target stone region in the target region based on the medical image includes:

[0097] Step 1011, identifying the medical image to determine a reference stone region in the target region;

[0098] In this step, the electronic device segments the medical image using a preset segmentation model to obtain segmented medical images of each physiological tissue corresponding to the target region. Specifically, the segmentation model is pre-trained and stored in the electronic device. The electronic device inputs the medical image into the segmentation model, which segments the medical image and outputs segmented medical images of each physiological tissue corresponding to the target region.

[0099] Exemplarily, the medical image is an image of the patient's kidney region, and the segmentation model segments the medical image to obtain segmented medical images of various physiological tissues corresponding to the kidney region, wherein the various physiological tissues corresponding to the kidney region include: renal cortex, renal medulla, renal pelvis, renal corpuscle, renal tubule, proximal convoluted tubule, loop of Henle, distal convoluted tubule, collecting duct, renal artery and renal vein, etc.

[0100] Exemplarily, the medical image is an image of the patient's urinary system region, and the segmentation model segments the medical image to obtain segmented medical images of various physiological tissues corresponding to the urinary system region, wherein the various physiological tissues corresponding to the urinary system region include: kidneys, ureters, bladder, urethra, etc.

[0101] After obtaining the segmented medical images of each physiological tissue corresponding to the target area, the electronic device determines the reference stone area in the target area based on the density characteristic value corresponding to each voxel point in each segmented medical image. The density characteristic value corresponding to each voxel point is represented by a CT value. In the field of medical imaging, the CT value (or Hounsfield unit, abbreviated as HU) is a quantitative standard for the density of different tissues in a computed tomography (CT) image. The CT value reflects the degree of absorption of X-rays by different substances during the CT scan process, thereby helping doctors identify and diagnose various lesions, diseases and tissue types. The CT value of a voxel point refers to the density information corresponding to a voxel (volume element) in a three-dimensional CT scan image, which is usually used to help doctors determine different tissue types, diagnose diseases, or evaluate treatment effects.

[0102] Specifically, for each of the segmented medical images, the electronic device compares the density characteristic value corresponding to each voxel point in the segmented medical image with a preset threshold value. The electronic device determines the area composed of voxel points in the segmented medical image whose density characteristic value is greater than the preset threshold value as a stone area. After the electronic device completes the recognition of each segmented medical image, it obtains the stone area corresponding to each segmented medical image, and then determines the reference stone area in the target area based on the stone area corresponding to each segmented medical image. By segmenting the medical image, the segmented medical images of each physiological tissue corresponding to the target area are obtained, and the density characteristic values corresponding to the voxel points in the segmented medical image of each physiological tissue can be analyzed respectively, thereby improving the accuracy of determining the reference stone area in the segmented medical image of each physiological tissue, which helps to improve the accuracy of determining the reference stone area in the target area.

[0103] Step 1012, performing noise reduction processing on the reference stone region to obtain a noise-reduced stone region;

[0104] In this step, after determining the reference stone region within the target area, the electronic device performs noise reduction on the reference stone region to obtain a noise-reduced stone region. It should be noted that scanned images often contain unevenly distributed image noise, such as jagged edges and anchor points. This image noise significantly impacts subsequent density correction, so noise reduction is necessary.

[0105] Optionally, the electronic device may perform noise reduction processing on the entire reference stone area to obtain a noise-reduced stone area; optionally, the electronic device may determine the edge area of the reference stone area as the noise reduction area, perform noise reduction processing on the noise reduction area, and obtain a noise-reduced stone area.

[0106] Step 1013: Perform pixel density correction processing on the noise-reduced stone area to obtain a target stone area.

[0107] In this step, the electronic device performs pixel density correction processing on the noise reduction stone area to obtain the target stone area. It should be noted that the reference stone area is obtained based on the pixel density of each voxel point in the medical image. The pixel density of a voxel point is the average density of all pixels within the voxel point. For example, a voxel point is 1cm 3 , which contains many pixels, each of which has a corresponding pixel density characteristic value. The pixel density value of this voxel is the average value of the corresponding pixel density characteristic values of all the pixels in it, which will lead to deviations in the attribution of this voxel (inaccurate classification of whether it belongs to the stone area). Therefore, it is necessary to perform pixel density correction processing on the denoised stone area to correctly divide the voxels belonging to the stone area in the reference stone area, and then obtain the target stone area.

[0108] Optionally, the electronic device can perform pixel density correction processing on all voxel points in the entire reference stone area to obtain a target stone area; optionally, the electronic device can determine the edge area of the reference stone area as the target area, perform pixel density correction processing on all voxel points in the target area to obtain a target stone area.

[0109] The electronic device of this embodiment identifies the medical image and determines a reference stone region within the target region; performs noise reduction processing on the reference stone region to obtain a noise-reduced stone region; and performs pixel density correction processing on the noise-reduced stone region to obtain a target stone region. Performing noise reduction processing first can reduce the impact of image noise on subsequent pixel density correction processing, thereby improving the accuracy of the pixel density correction processing. Then, performing pixel density correction processing to obtain the target stone region can improve the accuracy of the stone region identified in the medical image.

[0110] Further, refer to Figure 5 A fifth embodiment of the lithotripsy strategy determination method is proposed. The difference between the fifth embodiment and the first to fourth embodiments is that the noise reduction process is performed on the reference stone region to obtain the noise-reduced stone region, including:

[0111] Step 10121: determining, based on the comparison result, a target voxel point set whose pixel density is less than a preset density threshold;

[0112] Step 10122: determine the region consisting of the target voxel point set as the noise reduction region in the reference stone region.

[0113] In steps 10121 to 10122, the electronic device determines a target voxel point set in the reference stone region whose pixel density is less than the preset density threshold based on the comparison result of the pixel density corresponding to each voxel point and the preset density threshold, and determines the region composed of the target voxel point set as the noise reduction region in the reference stone region. It should be noted that in the reference stone region, the density threshold of the voxel points closer to the center of the region is larger, and the density threshold of the voxel points farther from the center of the region is smaller. Therefore, the voxel points with pixel density less than the preset density threshold are generally voxel points in the edge region of the reference stone region. In other words, it can be understood that the noise reduction region in the reference stone region is generally the edge region of the reference stone region.

[0114] For example, Figure 6 As shown, the circles represent the reference stone area, wherein the area between the two circles belongs to the noise reduction area in the reference stone area.

[0115] Step 10123: Perform noise reduction processing on the noise reduction area to obtain a noise reduction stone area.

[0116] In this step, the electronic device performs noise reduction processing on the noise reduction area in the reference stone area to obtain a noise reduction stone area. Among them, there are many methods of noise reduction processing, including: spatial domain noise reduction method, frequency domain noise reduction method, deep learning noise reduction method, and other noise reduction methods, among which the spatial domain noise reduction method includes: mean filtering, median filtering, Gaussian filtering, bilateral filtering, and adaptive filtering. The frequency domain noise reduction method includes: Fourier transform noise reduction and linear filtering. The deep learning noise reduction method includes: denoising autoencoder, convolutional neural network, and generative adversarial network. Other noise reduction methods include: total variation (TV) denoising and non-local mean denoising. The specific noise reduction method is not limited here.

[0117] The electronic device of this embodiment obtains a target voxel point set having a pixel density less than a preset density threshold in the reference stone region; determines the region formed by the target voxel point set as a noise reduction region in the reference stone region; and performs noise reduction processing on the noise reduction region to obtain a noise-reduced stone region. This can reduce the impact of image noise on subsequent pixel density correction processing and improve the accuracy of the pixel density correction processing.

[0118] Further, refer to Figure 7 A sixth embodiment of the stone crushing strategy determination method is proposed. The difference between the sixth embodiment and the first to fifth embodiments is that the pixel density correction processing is performed on the noise reduction stone area to obtain the target stone area, including:

[0119] Step 10131, obtaining the pixel density corresponding to each voxel point in the denoised stone area, and calculating the pixel density change rate between adjacent voxel points;

[0120] In this step, the electronic device obtains the pixel density corresponding to each voxel point in the noise reduction stone area and calculates the pixel density change rate between adjacent voxels. Specifically, for each voxel point, the pixel density of multiple adjacent voxels is obtained, and then the pixel density change rate between the multiple adjacent voxels is calculated. The calculated pixel density change rate is bound to the voxel point.

[0121] Step 10132: determining the region composed of voxel points whose pixel density change rate is greater than a preset change rate threshold as the target correction region in the denoised stone region;

[0122] In this step, after determining the pixel density change rate corresponding to each voxel point, the electronic device determines the area composed of voxel points whose pixel density change rate is greater than a preset change rate threshold as the target correction area in the noise reduction stone area.

[0123] Step 10133: Perform pixel density correction processing on each voxel point in the target correction area to obtain the target stone area.

[0124] In this step, when determining the target correction area, the electronic device performs pixel density correction processing on each voxel point in the target correction area to obtain the target stone area.

[0125] Specifically, step 10133 includes:

[0126] Step 101331: for each voxel point in the target correction area, obtain a reference pixel density of each reference voxel point within a preset range of the voxel point;

[0127] In this step, for each voxel point in the target correction area, the electronic device obtains the reference pixel density of each reference voxel point within a preset range of the voxel point. Preferably, the electronic device uses each voxel point adjacent to the voxel point as a reference voxel point and obtains the reference pixel density of each reference voxel point. Alternatively, the electronic device uses each voxel point within a range of two voxels from the voxel point as a reference voxel point and obtains the reference pixel density of each reference voxel point. The specific preset range is not limited here.

[0128] Step 101332, counting the distribution probability of the reference pixel density of the reference voxel points within the preset range of the voxel points;

[0129] In this step, after determining the reference pixel density of each reference voxel point within the preset voxel point range, the electronic device calculates the distribution probability of the reference pixel density of each reference voxel point. For example, if there are 10 reference voxels within the preset voxel point range, of which 3 have a reference pixel density of 10 and 7 have a reference pixel density of 20, then the distribution probability is: the reference voxels with a reference pixel density of 10 account for 30%, and the reference voxels with a reference pixel density of 20 account for 70%.

[0130] Step 101333: Determine the reference pixel density with the maximum distribution probability as the target pixel density, and correct the pixel density corresponding to the voxel point to the target pixel density;

[0131] In this step, the electronic device determines the reference pixel density with the largest distribution probability as the target pixel density, and corrects the pixel density corresponding to the voxel point to the target pixel density; for example: the pixel density of the voxel point is 12, and there are 10 reference voxel points within the preset range, of which 3 reference voxel points have a reference pixel density of 10, and 7 reference voxel points have a reference pixel density of 20, that is, the distribution probability is: the reference voxel points with a reference pixel density of 10 account for 30%, and the reference voxel points with a reference pixel density of 20 account for 70%. At this time, the pixel density of the voxel point 12 is corrected to the reference pixel density of 20.

[0132] Furthermore, if the reference pixel densities of the reference voxels within the preset range of the voxel point have the same distribution probability, a weighted averaging process is performed to determine the target pixel density, and the pixel density corresponding to the voxel point is then corrected to the target pixel density. For example, if the pixel density of the voxel point is 12, and there are 10 reference voxels within the preset range, of which 5 have a reference pixel density of 10 and 5 have a reference pixel density of 20, then the distribution probability is: the reference voxels with a reference pixel density of 10 account for 50%, and the reference voxels with a reference pixel density of 20 account for 50%. In this case, the distribution probabilities are the same, and a weighted averaging process is performed to determine the target pixel density of 15, and the pixel density of the voxel point 12 is then corrected to the reference pixel density of 15.

[0133] Step 101334, until each voxel point in the target correction area completes pixel density correction, and the target stone area is obtained.

[0134] In this step, the electronic device performs the above-mentioned correction processing on the pixel density of each voxel point in the target correction area until the pixel density correction of each voxel point in the target correction area is completed; the electronic device compares the corrected pixel density corresponding to each voxel point in the target correction area with the preset pixel density threshold, and determines the area composed of voxel points whose corrected pixel density is greater than or equal to the preset pixel density threshold as the stone area, and determines the area composed of the stone area and the non-target correction area in the denoised stone area as the target stone area.

[0135] The electronic device of this embodiment obtains the pixel density corresponding to each voxel point in the denoised stone region and calculates the pixel density change rate between adjacent voxels; determines the region consisting of voxels whose pixel density change rate is greater than a preset change rate threshold as a target correction region in the denoised stone region; and performs pixel density correction processing on each voxel point in the target correction region to obtain a target stone region. The pixel density correction processing improves the accuracy of the pixel density of each voxel point, thereby improving the accuracy of determining the stone region in the medical image.

[0136] This embodiment also provides a device for determining a stone-crushing strategy, which can be integrated into electronic devices such as smart terminals, PC terminals, and mobile terminals. Figure 8 As shown, the lithotripsy strategy determination device may include:

[0137] An acquiring unit 1001 is configured to acquire a medical image of a target area and determine a target stone area in the target area based on the medical image;

[0138] A first determining unit 1002 is configured to analyze the target stone area and determine stone parameters;

[0139] The second determining unit 1003 is configured to determine a lithotripsy strategy according to the stone parameters.

[0140] In an optional example, the first determining unit is further configured to:

[0141] determining the stone density according to the pixel density distribution corresponding to the target stone area;

[0142] The stone volume is determined according to the scale parameter corresponding to the medical image and the size parameter corresponding to the target stone area.

[0143] In an optional example, the second determining unit is further configured to:

[0144] determining a reference lithotripsy energy according to the stone density and the stone volume;

[0145] determining a target lithotripsy time based on the reference lithotripsy energy, the stone volume, and the stone density;

[0146] A lithotripsy strategy is determined according to the reference lithotripsy energy and the target lithotripsy duration.

[0147] In an optional example, the second determining unit is further configured to:

[0148] According to preset constraints, the reference lithotripsy energy is adjusted to obtain a target lithotripsy energy;

[0149] A lithotripsy strategy is determined according to the target lithotripsy energy and the target lithotripsy duration.

[0150] In an optional example, the acquisition unit is further used to:

[0151] Identifying the medical image to determine a reference stone region in the target region;

[0152] performing noise reduction processing on the reference stone region to obtain a noise-reduced stone region;

[0153] The pixel density of the noise-reduced stone area is corrected to obtain a target stone area.

[0154] In an optional example, the acquisition unit is further used to:

[0155] Acquire a target voxel point set whose pixel density in the reference stone area is less than a preset density threshold;

[0156] determining the region formed by the target voxel point set as the noise reduction region in the reference stone region;

[0157] The noise reduction area is subjected to noise reduction processing to obtain a noise reduction stone area.

[0158] In an optional example, the acquisition unit is further used to:

[0159] Obtaining the pixel density corresponding to each voxel point in the denoised stone area, and calculating the pixel density change rate between adjacent voxel points;

[0160] Determine the region composed of voxel points whose pixel density change rate is greater than a preset change rate threshold as the target correction region in the noise reduction stone region;

[0161] Pixel density correction processing is performed on each voxel point in the target correction area to obtain a target stone area.

[0162] In an optional example, the acquisition unit is further used to:

[0163] For each voxel point in the target correction area, obtaining a reference pixel density of each reference voxel point within a preset range of the voxel point;

[0164] Counting the distribution probability of the reference pixel density of each reference voxel point within the preset range of the voxel point;

[0165] Determine the reference pixel density with the maximum distribution probability as the target pixel density, and correct the pixel density corresponding to the voxel point to the target pixel density;

[0166] Until the pixel density correction is completed for each voxel point in the target correction area, the target stone area is obtained.

[0167] Using the solution of this embodiment, a medical image of a target area is acquired, and a target stone region within the target area is determined based on the medical image; the target stone region is analyzed to determine stone parameters; and a lithotripsy strategy is determined based on the stone parameters. By first determining the target stone region in the medical image, then determining stone parameters based on the target stone region, and then determining a lithotripsy strategy specifically based on the stone parameters, the accuracy of the lithotripsy strategy can be improved.

[0168] Accordingly, the embodiment of the present application further provides an electronic device, such as Figure 9 As shown, Figure 9 Schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 1100 includes a processor 1101 having one or more processing cores, a memory 1102 having one or more computer-readable storage media, and a computer program stored in the memory 1102 and executable on the processor. The processor 1101 is electrically connected to the memory 1102. Those skilled in the art will appreciate that the electronic device structure shown in the figure does not constitute a limitation of the electronic device, and may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0169] The processor 1101 is the control center of the electronic device 1100. It connects the various parts of the entire electronic device 1100 using various interfaces and lines. By running or loading software programs and / or units stored in the memory 1102 and calling data stored in the memory 1102, it executes various functions of the electronic device 1100 and processes data, thereby monitoring the entire electronic device 1100. The processor 1101 can be a processor CPU, a graphics processor GPU, a network processor (NP), etc., and can implement or execute the various methods, steps, and logic blocks disclosed in the embodiments of this application.

[0170] In an embodiment of the present application, the processor 1101 in the electronic device 1100 will load the instructions corresponding to the processes of one or more applications into the memory 1102 in accordance with the following steps, and the processor 1101 will run the applications stored in the memory 1102 to implement various functions. For specific implementation, please refer to the previous embodiments and will not be repeated here.

[0171] Optional, such as Figure 9 As shown, the electronic device 1100 further includes: a touch screen 1103, a radio frequency circuit 1104, an audio circuit 1105, an input unit 1106, and a power supply 1107. Among them, the processor 1101 is electrically connected to the touch screen 1103, the radio frequency circuit 1104, the audio circuit 1105, the input unit 1106, and the power supply 1107 respectively. Those skilled in the art will understand that Figure 9 The electronic device structure shown in the figure does not constitute a limitation to the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0172] The touch display screen 1103 can be used to display a graphical user interface and receive user actions on the operation instructions generated by the graphical user interface. The touch display screen 1103 may include a display panel and a touch panel. Among them, the display panel can be used to display the information input by the user or the information provided to the user and various graphical user interfaces of the electronic device, and these graphical user interfaces can be composed of graphics, text, icons, videos and any combination thereof. Optionally, a liquid crystal display (LCD), an organic light emitting diode (OLED, Organic Light-Emitting Diode) and the like can be used to configure the display panel. The touch panel can be used to collect the user's touch operations on or near it (such as the user uses any suitable object or accessory such as a finger, a stylus on the touch panel or near the touch panel) and generate corresponding operation instructions, and the operation instructions execute corresponding programs. Optionally, the touch panel may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch direction, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the touch point coordinates, and then sends it to the processor 1101, and can receive the command sent by the processor 1101 and execute it. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it is transmitted to the processor 1101 to determine the type of touch event. Then the processor 1101 provides a corresponding visual output on the display panel according to the type of touch event. In an embodiment of the present application, the touch panel and the display panel can be integrated into the touch display screen 1103 to realize the input and output functions. However, in some embodiments, the touch panel and the touch panel can be used as two independent components to realize the input and output functions. That is, the touch display screen 1103 can also be used as part of the input unit 1106 to realize the input function.

[0173] The radio frequency circuit 1104 may be used to transmit and receive radio frequency signals, so as to establish wireless communication with a network device or other electronic devices through wireless communication, and to transmit and receive signals with the network device or other electronic devices.

[0174] The audio circuit 1105 can be used to provide an audio interface between the user and the electronic device through a speaker and microphone. The audio circuit 1105 can convert the received audio data into an electrical signal and transmit it to the speaker, which then converts it into a sound signal for output. On the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 1105 and converted into audio data. The audio data is then output to the processor 1101 for processing, and then sent to another electronic device through the radio frequency circuit 1104, or the audio data is output to the memory 1102 for further processing. The audio circuit 1105 may also include an earphone jack to provide communication between external headphones and the electronic device.

[0175] The input unit 1106 may be configured to receive input digital, character information, or user feature information (such as fingerprint, iris, or facial information), and to generate keyboard, mouse, joystick, optical, or trackball signal input related to user settings and function control.

[0176] Power supply 1107 is used to supply power to various components of electronic device 1100. Optionally, power supply 1107 can be logically connected to processor 1101 via a power management device, thereby enabling the power management device to manage charging, discharging, and power consumption. Power supply 1107 can also include one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0177] although Figure 9 Not shown, the electronic device 1100 may further include a camera, a sensor, a wireless fidelity module, a Bluetooth module, etc., which will not be described in detail here.

[0178] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0179] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0180] To this end, embodiments of the present application provide a computer-readable storage medium storing multiple computer programs. These computer programs can be loaded by a processor to execute any of the methods for determining a lithotripsy strategy provided in embodiments of the present application. The computer programs can execute the lithotripsy strategy determination methods. Specific implementations can be found in the previous embodiments and will not be further described here.

[0181] The computer-readable storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0182] Since the computer program stored in the computer-readable storage medium can execute any of the lithotripsy strategy determination methods provided in the embodiments of the present application, the beneficial effects that can be achieved by any of the lithotripsy strategy determination methods provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0183] According to one aspect of the present application, a computer program product or computer program is also provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the methods provided in various optional implementations of the above embodiments.

[0184] In the above-described embodiments of the lithotripsy strategy determination device, computer-readable storage medium, electronic device, and computer program product, the descriptions of each embodiment have different focuses. For portions not described in detail in one embodiment, reference can be made to the relevant descriptions of other embodiments. Those skilled in the art will clearly understand that, for ease and brevity of description, the specific working processes and beneficial effects of the above-described lithotripsy strategy determination device, computer-readable storage medium, computer program product, electronic device, and their corresponding units can be referred to in the description of the lithotripsy strategy determination method in the above-described embodiments, and will not be further elaborated upon here.

[0185] The above describes in detail a method, device, electronic device, readable storage medium, and computer program product for determining a stone crushing strategy provided by the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is intended only to help understand the method and core concept of the present application. Furthermore, those skilled in the art may vary the specific implementation methods and scope of application based on the concept of the present application. In summary, the contents of this specification should not be construed as limiting the present application.

Claims

1. A method for determining a gravel strategy, characterized in that: The method for determining the stone crushing strategy includes: Acquiring a medical image of a target area, and determining a target stone area in the target area based on the medical image; Analyzing the target stone area to determine stone parameters; According to the stone parameters, a lithotripsy strategy is determined.

2. The method for determining a stone crushing strategy according to claim 1, wherein: The stone parameters include stone density and stone volume. The analyzing the stone area to determine the stone parameters includes: determining the stone density according to the pixel density distribution corresponding to the target stone area; The stone volume is determined according to the scale parameter corresponding to the medical image and the size parameter corresponding to the target stone area.

3. The method for determining a stone crushing strategy according to claim 2, wherein: Determining a lithotripsy strategy according to the stone parameters includes: determining a reference lithotripsy energy according to the stone density and the stone volume; determining a target lithotripsy time based on the reference lithotripsy energy, the stone volume, and the stone density; A lithotripsy strategy is determined according to the reference lithotripsy energy and the target lithotripsy duration.

4. The method for determining a stone crushing strategy according to claim 3, wherein: The method further comprises: According to preset constraints, the reference lithotripsy energy is adjusted to obtain a target lithotripsy energy; A lithotripsy strategy is determined according to the target lithotripsy energy and the target lithotripsy duration.

5. The method for determining a stone crushing strategy according to claim 1, wherein: Determining the target stone area in the target area based on the medical image includes: Identifying the medical image to determine a reference stone region in the target region; performing noise reduction processing on the reference stone region to obtain a noise-reduced stone region; The pixel density of the noise-reduced stone area is corrected to obtain a target stone area.

6. The method for determining a stone crushing strategy according to claim 5, characterized in that: The performing noise reduction processing on the reference stone region to obtain the noise-reduced stone region includes: Acquire a target voxel point set whose pixel density in the reference stone area is less than a preset density threshold; determining the region formed by the target voxel point set as the noise reduction region in the reference stone region; The noise reduction area is subjected to noise reduction processing to obtain a noise reduction stone area.

7. The method for determining a stone crushing strategy according to claim 5, characterized in that: The pixel density correction processing is performed on the noise reduction stone area to obtain a target stone area, including: Obtaining the pixel density corresponding to each voxel point in the denoised stone area, and calculating the pixel density change rate between adjacent voxel points; Determine the region composed of voxel points whose pixel density change rate is greater than a preset change rate threshold as the target correction region in the noise reduction stone region; Pixel density correction processing is performed on each voxel point in the target correction area to obtain a target stone area.

8. The method for determining a stone crushing strategy according to claim 7, characterized in that: The pixel density correction processing is performed on each voxel point in the target correction area to obtain the target stone area, including: For each voxel point in the target correction area, obtaining a reference pixel density of each reference voxel point within a preset range of the voxel point; Counting the distribution probability of the reference pixel density of each reference voxel point within the preset range of the voxel point; Determine the reference pixel density with the maximum distribution probability as the target pixel density, and correct the pixel density corresponding to the voxel point to the target pixel density; Until the pixel density correction is completed for each voxel point in the target correction area, the target stone area is obtained.

9. A device for determining a crushing strategy, characterized in that: The lithotripsy strategy determination device comprises: an acquiring unit, configured to acquire a medical image of a target area, and determine a target stone area in the target area based on the medical image; a first determining unit, configured to analyze the target stone area and determine stone parameters; The second determining unit is configured to determine a lithotripsy strategy according to the stone parameters.

10. An electronic device, characterized in that: The system comprises a processor and a memory, wherein the memory stores a computer program; the processor loads the computer program from the memory to execute the steps of the lithotripsy strategy determination method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor to execute the steps of the lithotripsy strategy determination method according to any one of claims 1 to 8.

Images