Intelligent analysis method and system for urinary calculus components

Abstract

The invention relates to a urinary calculus component intelligent analysis method and system, and relates to the technical field of urinary calculus component analysis. The intelligent urinary calculus component analysis method is applied to calculus type identification and comprises the following steps: acquiring a urinary calculus atomic number image corresponding to a dual-energy abdomen image; extracting a plurality of component percentages of peak values corresponding to a plurality of bar diagrams corresponding to effective atomic numbers in the urinary calculus atomic number image; and based on a plurality of component percentages corresponding to the plurality of bar maps and a plurality of set stone type component percentage intervals, identifying a stone type corresponding to the dual-energy abdomen image. According to the embodiment of the invention, intelligent stone type identification can be realized.

Description

Technical Field

[0001] The present disclosure relates to the technical field of urinary stone composition analysis, and in particular to an intelligent analysis method and system for urinary stone composition. Background Art

[0002] Urinary stones are a common disease, with a prevalence of approximately 5.6% in China, with kidney stones being the most common. Urinary stones can cause clinical symptoms such as pain, dysuria, infection, and hematuria. Left untreated, they can easily lead to renal impairment and even renal failure, and can also increase the incidence of many chronic diseases, including osteoporosis and cardiovascular disease. Furthermore, urinary stones frequently recur, with approximately 40% of patients experiencing one or more recurring episodes. Understanding the composition of urinary stones is crucial for selecting appropriate treatments for each component and guiding interventions to prevent recurrence. For example, uric acid stones can initially be treated with urine alkalinization, followed by surgery if necessary. Struvite / magnesium ammonium phosphate stones typically require preoperative antibiotic therapy, while calcium oxalate, carbonate apatite, and calcium carbonate phosphate stones often require surgery. Preventing urinary stone recurrence often requires dietary restrictions and modifications based on stone composition, as well as identifying associated metabolic diseases and administering medications.

[0003] At present, the diagnosis of the composition of urinary stones after surgery mainly relies on infrared spectroscopy analysis of stone samples. This is of certain significance for preventing the recurrence of urinary stones, but it does not help to select the appropriate stone treatment method before surgery. At present, the diagnosis of the composition of urinary stones before surgery mainly relies on the CT value of conventional CT examination. The high or low CT value can roughly distinguish uric acid from non-uric acid stones. For example, a CT value below 800HU is often a uric acid stone, but it is impossible to further distinguish non-uric acid stones (calcium oxalate monohydrate, calcium oxalate dihydrate, carbonate apatite, struvite, calcium carbonate phosphate, etc.). In addition, after the average atomic number value of spectral CT is applied in clinical practice, it can accurately distinguish uric acid stones from non-uric acid stones. However, further differentiation and subclassification of non-uric acid stones remain difficult.

[0004] Conventional CT scans cannot further differentiate non-uric acid stones. This is because even for single-component non-uric acid stones, the CT values of stones composed of calcium oxalate monohydrate, calcium oxalate dihydrate, carbonated apatite, struvite, and calcium carbonate phosphate still overlap, making it impossible to distinguish and identify them solely based on the CT value. Furthermore, mixed-component stones are far more common in clinical practice than single-component stones. For stones formed by a mixture of two components, if the proportions of the two components differ, the final value calculated using the average CT value formula will be different, making it impossible to accurately distinguish mixed stones of different compositions.

[0005] The average atomic number value of spectral CT can not only accurately identify uric acid stones and non-uric acid stones, but also has a certain diagnostic specificity for the components of non-uric acid stones with a single component. However, the average atomic number value of spectral CT is not applicable to the mixed-component stones that account for the largest proportion clinically. The reason is that the calculation method of the average atomic number value of the mixed components is also closely related to the composition percentage and component types of different components. Therefore, the average atomic number value cannot accurately identify the mixed urinary tract stones of different components. Summary of the Invention

[0006] The present disclosure proposes a technical solution corresponding to an intelligent analysis method and system for the components of urinary tract stones.

[0007] According to one aspect of the present disclosure, there is provided an intelligent analysis method for the components of urinary tract stones, which is applied to stone type identification and includes: obtaining an atomic number image of a urinary tract stone corresponding to a dual-energy abdominal image; wherein the dual-energy abdominal image is configured as a dual-energy abdominal CT image; extracting multiple component percentages corresponding to the peaks of multiple strip charts corresponding to the effective atomic numbers in the atomic number image of the urinary tract stone; and identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple strip charts and multiple set component percentage intervals of the set stone types.

[0008] Preferably, the method for extracting multiple component percentages corresponding to the peaks of multiple strip charts corresponding to the effective atomic numbers in the atomic number image of the urinary tract stone includes: extracting multiple strip charts corresponding to the effective atomic numbers in the atomic number image of the urinary tract stone; and determining multiple component percentages corresponding to the peaks of the multiple strip charts according to the multiple strip charts corresponding to the atomic number image of the urinary tract stone.

[0009] Preferably, the method for determining multiple component percentages corresponding to the peaks of multiple strip charts according to the multiple strip charts corresponding to the atomic number image of the urinary tract stone includes: obtaining the set strip chart color and / or the set strip chart width; extracting multiple strip charts corresponding to the effective atomic numbers in the atomic number image of the urinary tract stone that meet the set strip chart color and / or the set strip chart width according to the set strip chart color and / or the set strip chart width; and determining multiple component percentages corresponding to the peaks of the multiple strip charts according to the multiple strip charts corresponding to the atomic number image of the urinary tract stone that meet the set strip chart color and / or the set strip chart width.

[0010] Preferably, the method for identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and the multiple set stone type component percentage ranges includes: obtaining a set component percentage less than the minimum value of the set stone type component percentage range; if the component percentages corresponding to the multiple bar graphs are less than the set component percentage, deleting the component percentages less than the set component percentage from the multiple component percentages corresponding to the multiple bar graphs; otherwise, retaining the component percentages greater than or equal to the set component percentage; and identifying the stone type corresponding to the dual-energy abdominal image based on the retained multiple component percentages and the multiple set stone type component percentage ranges.

[0011] Preferably, the method for identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and the multiple set stone type component percentage ranges includes: if the component percentage in the multiple component percentages is within the range of a certain set stone type component percentage range among the multiple set stone type component percentage ranges; configuring the stone type corresponding to the certain set stone type component percentage range as the stone type corresponding to the component percentage within the range.

[0012] Preferably, the method for identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and the multiple set stone type component percentage ranges further includes: if there is an overlapping range in the set stone type component percentage ranges corresponding to at least two of the multiple component percentages corresponding to the multiple bar graphs, obtaining the iodine-based image corresponding to the dual-energy abdominal image; and identifying the stone type corresponding to the overlapping range based on the iodine-based image corresponding to the dual-energy abdominal image.

[0013] Preferably, the method for identifying the stone type corresponding to the overlapping range based on the iodine-based image corresponding to the dual-energy abdominal image includes: determining the stone gray value of the iodine-based image corresponding to the dual-energy abdominal image; respectively calculating the average gray value and uniformity corresponding to the stone gray value; if the average gray value is greater than the set average gray value and the uniformity is greater than the set uniformity, configuring the stone type as calcium oxalate dihydrate stone; otherwise, configuring the stone type as struvite stone or carbonate apatite stone.

[0014] Preferably, the multiple set stone type component percentage ranges include one or several of: uric acid stone component percentage range, calcium oxalate monohydrate stone component percentage range, calcium oxalate dihydrate stone component percentage range, carbonate apatite stone component percentage range, calcium carbonate phosphate stone component percentage range, struvite stone component percentage range.

[0015] Preferably, the percentage range of uric acid stone components is configured to be 6.5 - 10.5, the percentage range of calcium oxalate monohydrate stone components is configured to be 13.3 - 14.0, the percentage range of calcium oxalate dihydrate stone components is configured to be 12.0 - 13.3, the percentage range of carbonate apatite stone components is configured to be 14.0 - 15.0, the percentage range of calcium carbonate phosphate stone components is configured to be greater than 12.5, and the percentage range of struvite stone components is configured to be less than 12.5.

[0016] According to one aspect of the present disclosure, there is provided an intelligent analysis method for urinary stone components, which is applied to stone information extraction and includes: the intelligent analysis method for urinary stone components applied to stone type recognition as described above; and segmenting the stones in the kidneys, ureters, and bladder from the dual - energy abdominal image to obtain a stone mask image; based on the stone mask image and / or the dual - energy abdominal image, extracting one or several pieces of information including the stone position, stone shape, stone gray value, major axis and minor axis corresponding to the stone, and stone volume corresponding to each stone type.

[0017] Preferably, the method of extracting one or several pieces of information including the stone position, stone shape, stone gray value, major axis and minor axis corresponding to the stone, and stone volume corresponding to each stone type based on the stone mask image and / or the dual - energy abdominal image includes: respectively based on each two - dimensional slice of the dual - energy abdominal image in the three - dimensional dual - energy abdominal image and the two - dimensional slice stone mask image of the corresponding slice of the stone mask image, extracting a three - dimensional stone image with the original gray value from the three - dimensional dual - energy abdominal image; respectively calculating the average gray value corresponding to each stone type in the three - dimensional stone mask image to determine the stone gray value corresponding to each stone type.

[0018] Preferably, the method of extracting a three - dimensional stone image with the original gray value from the three - dimensional dual - energy abdominal image respectively based on each two - dimensional slice of the dual - energy abdominal image in the three - dimensional dual - energy abdominal image and the two - dimensional slice stone mask image of the corresponding slice of the stone mask image includes: performing a multiplication operation on each two - dimensional slice of the dual - energy abdominal image in the three - dimensional dual - energy abdominal image and the two - dimensional slice stone mask image of the corresponding slice of the stone mask image to obtain a three - dimensional stone gray value image; based on the mask values and stone spatial positions corresponding to each stone type in the two - dimensional slice stone mask image of the three - dimensional stone gray value image and its corresponding slice of the stone mask image, restoring the gray value of the three - dimensional stone gray value image to obtain a stone image with the original gray value.

[0019] Preferably, the method for restoring the gray value of the three-dimensional stone gray value image to obtain a stone image with the original gray value based on the mask values and stone spatial positions corresponding to each stone type in the two-dimensional slice stone mask image of the stone mask image corresponding to the three-dimensional stone gray value image and its corresponding slices includes: respectively extracting the mask values and stone spatial positions corresponding to each stone type in the two-dimensional slice stone mask image of the stone mask image; based on the stone spatial positions corresponding to each stone type, respectively performing stone type spatial position localization on the three-dimensional stone gray value image to obtain spatial position localization images corresponding to each stone type in the two-dimensional slice stone gray value image of the three-dimensional stone gray value image; dividing the spatial position localization images corresponding to each stone type in the two-dimensional slice stone gray value image of the three-dimensional stone gray value image by the mask values corresponding to the stone spatial positions to obtain a stone image with the original gray value.

[0020] Preferably, calculate the stone area corresponding to each stone type in each two-dimensional slice stone mask image of the three-dimensional stone mask image respectively; obtain a plurality of stone areas; calculate the major axis and minor axis corresponding to the largest stone area among the plurality of stone areas corresponding to each stone type respectively to obtain the major axis and minor axis corresponding to each stone type; perform edge detection on the stones corresponding to each stone type on the stone mask image respectively to obtain a three-dimensional stone edge mask line; fit the three-dimensional stone edge mask lines corresponding to each stone type respectively to obtain the stone shape corresponding to each stone type; perform three-dimensional reconstruction on the mask images corresponding to each stone type in the stone mask image respectively to obtain the stone volume corresponding to each stone type.

[0021] Preferably, segment the medical image of the kidney and / or ureter and / or bladder to obtain a three-dimensional kidney and / or ureter and / or bladder mask image; according to the three-dimensional kidney and / or ureter and / or bladder mask image, configure the kidney and / or ureter and / or bladder mask in the three-dimensional kidney and / or ureter and / or bladder mask image as the left kidney and / or ureter and / or bladder mask and the right kidney and / or ureter and / or bladder mask; based on the spatial position relationship between the stone mask in the stone mask image and the left kidney and / or ureter and / or bladder mask and the right kidney and / or ureter and / or bladder mask in the three-dimensional kidney and / or ureter and / or bladder mask image, determine the stone position corresponding to each stone type.

[0022] Preferably, the method for determining the stone position corresponding to each stone type based on the spatial position relationship between the stone mask in the stone mask image and the left kidney and / or ureter and / or bladder mask and the right kidney and / or ureter and / or bladder mask in the three-dimensional kidney and / or ureter and / or bladder mask image includes: extracting a plurality of two-dimensional slice stone mask images corresponding to each stone type from the three-dimensional stone mask image; if the spatial position of the stone mask corresponding to the plurality of two-dimensional slice stone mask images is within the spatial position of the left kidney and / or ureter and / or bladder mask in the two-dimensional slice stone mask image corresponding to the corresponding slice of the three-dimensional kidney and / or ureter and / or bladder mask image, the stone corresponding to the spatial position of the stone mask is configured as a left kidney and / or ureter and / or bladder stone; if the spatial position of the stone mask corresponding to the plurality of two-dimensional slice stone mask images is within the spatial position of the right kidney and / or ureter and / or bladder mask in the two-dimensional slice stone mask image corresponding to the corresponding slice of the three-dimensional kidney and / or ureter and / or bladder mask image, the stone corresponding to the spatial position of the stone mask is configured as a right kidney and / or ureter and / or bladder stone.

[0023] Preferably, the method for segmenting stones in the kidney, ureter, and bladder from the dual-energy abdominal image to obtain a stone mask image includes: obtaining a preset segmentation network, dual-energy abdominal training images for training the preset segmentation network, and a multi-mask label fusion image corresponding to the dual-energy abdominal training image and representing the kidney, ureter, and stones; training the preset segmentation network with the dual-energy abdominal training image and its corresponding multi-mask label fusion image to obtain a corresponding stone segmentation model; based on the stone segmentation model, segmenting stones in the kidney, ureter, and bladder from the dual-energy abdominal image to obtain a stone mask image.

[0024] Preferably, the method for constructing the multi-mask label image fusion image respectively representing the kidney, bladder, ureter, and stones includes: obtaining a kidney mask label image, a ureter mask label image, and a stone mask image corresponding to the dual-energy abdominal training image; respectively assigning a first mask value, a second mask value, and a third mask value to the kidney mask label image, the ureter mask label image, and the stone mask image; fusing the kidney mask label image corresponding to the first mask value, the ureter mask label image corresponding to the second mask value, and the stone mask image corresponding to the third mask value to obtain the multi-mask label fusion image corresponding to the dual-energy abdominal training image and respectively representing the kidney, bladder, ureter, and stones.

[0025] Preferably, the method of fusing the kidney mask label image corresponding to the first mask value, the ureter mask label image corresponding to the second mask value, and the calculus mask image corresponding to the third mask value to obtain the multi-mask label image fusion image representing the kidney, bladder, ureter, and calculus respectively corresponding to the dual-energy abdominal training image includes: respectively determining a first spatial position corresponding to the first mask value in the kidney mask label image, a second spatial position corresponding to the second mask value in the ureter mask label image, and a third spatial position corresponding to the third mask value in the calculus mask image; if the first spatial position and the second spatial position overlap with the third spatial position, updating the mask value corresponding to the overlapping spatial position to the third mask value corresponding to the third spatial position; otherwise, retaining the first mask value corresponding to the first spatial position and the second mask value corresponding to the second spatial position to obtain the multi-mask label fusion image representing the kidney, bladder, ureter, and calculus respectively corresponding to the dual-energy abdominal training image.

[0026] According to one aspect of the present disclosure, there is provided a urinary calculus composition intelligent analysis device / system, applied to calculus type recognition, including:

[0027] An acquisition unit, configured to acquire a urinary calculus atomic number image corresponding to a dual-energy abdominal image;

[0028] An extraction unit, configured to extract multiple component percentages corresponding to peaks of multiple bar graphs corresponding to effective atomic numbers in the urinary calculus atomic number image;

[0029] An identification unit, configured to identify the calculus type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and multiple set calculus type component percentage ranges.

[0030] According to one aspect of the present disclosure, there is provided a urinary calculus composition intelligent analysis device / system, applied to calculus information extraction, including:

[0031] An acquisition unit, configured to acquire a urinary calculus atomic number image corresponding to a dual-energy abdominal image;

[0032] A component percentage extraction unit, configured to extract multiple component percentages corresponding to peaks of multiple bar graphs corresponding to effective atomic numbers in the urinary calculus atomic number image;

[0033] An identification unit, configured to identify the calculus type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and multiple set calculus type component percentage ranges;

[0034] A segmentation unit for segmenting stones in the kidneys, ureters, and bladder from the dual-energy abdominal image to obtain a stone mask image;

[0035] A stone information extraction unit for extracting one or several pieces of information including the stone position, stone shape, stone gray value, major axis and minor axis corresponding to the stone, and stone volume corresponding to each stone type based on the stone mask image and / or the dual-energy abdominal image.

[0036] According to one aspect of the present disclosure, there is provided a smart analysis device / system for urinary tract stone components, including: an electronic device configured with a processor and a memory for storing instructions executable by the processor; wherein, the processor is configured to call the instructions stored in the memory to execute the above-mentioned smart analysis method for urinary tract stone components.

[0037] According to one aspect of the present disclosure, there is provided a smart analysis device / system for urinary tract stone components, including: a processor; a memory for storing instructions executable by the processor; wherein, the processor is configured to call the instructions stored in the memory to execute the above-mentioned smart analysis method for urinary tract stone components.

[0038] According to one aspect of the present disclosure, there is provided a smart analysis device / system for urinary tract stone components, including: a computer-readable storage medium storing computer program instructions, and when the computer program instructions are executed by a processor, the above-mentioned smart analysis method for urinary tract stone components is implemented.

[0039] According to one aspect of the present disclosure, there is provided a smart analysis device / system for urinary tract stone components, including: a computer program product including a computer program / instructions, and when the computer program / instructions are executed by a processor, the above-mentioned smart analysis method for urinary tract stone components is implemented.

[0040] In the embodiments of the present disclosure, a smart analysis method and system for urinary tract stone components are proposed to solve the technical problem that it is difficult to identify or distinguish mixed urinary tract stones of different components, so that the diagnostic requirements of mixed-component stones being more than single-component stones in clinical practice cannot be met.

[0041] It should be understood that the above general description and the following detailed description are exemplary and explanatory, and do not limit the present disclosure.

[0042] According to the following detailed description of exemplary embodiments with reference to the accompanying drawings, other features and aspects of the present disclosure will become clear. Description of the Drawings

[0043] The accompanying drawings here are incorporated into the specification and form a part of this specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used to explain the technical solutions of the present disclosure.

[0044] Figure 1 A flowchart showing an intelligent analysis method for urinary calculus components according to an embodiment of the present disclosure;

[0045] Figure 2 A block diagram of an electronic device 800 shown according to an exemplary embodiment;

[0046] Figure 3 A block diagram of an electronic device 1900 shown according to an exemplary embodiment. Detailed implementation manners

[0047] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0048] The special term "exemplary" herein means "serving as an example, embodiment, or illustration". Any embodiment described as "exemplary" here does not necessarily have to be construed as superior to or better than other embodiments.

[0049] The term "and / or" herein merely describes an association relationship between associated objects and indicates that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality. For example, including at least one of A, B, and C may represent including any one or more elements selected from the set composed of A, B, and C.

[0050] In addition, for a better description of the present disclosure, numerous specific details are given in the following detailed implementation manners. Those skilled in the art should understand that the present disclosure can also be implemented without some specific details. In some instances, methods, means, elements, and circuits well-known to those skilled in the art are not described in detail so as to highlight the gist of the present disclosure.

[0051] It can be understood that the above-mentioned various method embodiments of the intelligent analysis of urinary calculus components mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not elaborate further.

[0052] In addition, the present disclosure also provides an intelligent analysis device / system for urinary calculus components, an electronic device, a computer-readable storage medium, a program, and a program product, all of which can be used to implement any of the intelligent analysis methods for urinary calculus components provided by the present disclosure. For the corresponding technical solutions and descriptions, reference can be made to the corresponding records in the method section, which will not be elaborated herein.

[0053] Figure 1 The flowchart showing the intelligent analysis method for urinary calculus components according to an embodiment of the present disclosure is as follows. Figure 1 As shown, the intelligent analysis method for urinary calculus components, which is applied to stone type identification, includes: Step S101: Obtain an atomic number image of the urinary calculus corresponding to the dual-energy abdominal image; wherein, the dual-energy abdominal image is configured as a dual-energy abdominal CT image; Step S102: Extract multiple component percentages corresponding to the peaks of multiple strip charts of the effective atomic numbers in the atomic number image of the urinary calculus; Step S103: Based on the multiple component percentages corresponding to the multiple strip charts and multiple set component percentage intervals of stone types, identify the stone type corresponding to the dual-energy abdominal image. This is to solve the technical problem that it is difficult to distinguish or identify mixed urinary calculi with different components, so that the diagnostic requirements for mixed-component stones, which are more than single-component stones clinically, cannot be met.

[0054] Step S101: Obtain an atomic number image of the urinary calculus corresponding to the dual-energy abdominal image; wherein, the dual-energy abdominal image is configured as a dual-energy abdominal CT image.

[0055] In the embodiments of the present disclosure and other possible embodiments, an energy spectrum CT device (dual-energy CT device, energy spectrum CT device) is used to scan the abdomen of a patient to obtain a three-dimensional dual-energy abdominal image (dual-energy abdominal image / dual-energy spectrum abdominal image / dual-energy abdominal CT image / dual-energy spectrum abdominal CT image / energy spectrum abdominal image / energy spectrum abdominal CT image).

[0056] In the embodiments of the present disclosure and other possible embodiments, the energy spectrum CT device (dual-energy CT device, energy spectrum CT device), that is, a fast tube voltage switching dual-source CT device, adopts an instantaneous kVp switching technology to complete the switching of high and low single energies within an extremely short time (<0.25 ms), achieving the triple identity (simultaneous, in the same direction, and from the same source) of dual energies. It overcomes the defects of the dual-tube dual-source CT, thereby avoiding the slight angular difference between the scanning planes of the two tubes, improving the accuracy of the acquired data, and providing more excellent CT scan images.

[0057] In the embodiments of the present disclosure and other possible embodiments, spectral CT scanning and three-dimensional reconstruction can not only clearly display the density and morphology of the renal parenchyma, the orientation and contour of the renal pelvis and calyces, the course and lumen of the ureter, the bladder wall and the conditions inside the bladder, but also display the morphology and position of renal calculi and renal pelvis and calyces, and further measure the volumes of the calculi and renal pelvis and calyces. The imaging quality is far superior to that of traditional CT three-dimensional multi-planar reconstruction. In particular, the effective atomic number (Zeff value) of spectral CT can be used to analyze the compositional characteristics of renal calculi, display the numerical differences in different regions, and clarify the specific spatial distribution patterns of different components in mixed calculi based on these differences.

[0058] Step S102: Extract multiple component percentages corresponding to the peaks of multiple strip charts corresponding to the effective atomic number in the atomic number image of the urinary system calculi.

[0059] In the embodiments of the present disclosure and other possible embodiments, the method for extracting multiple component percentages corresponding to the peaks of multiple strip charts corresponding to the effective atomic number in the atomic number image of the urinary system calculi includes: extracting multiple strip charts corresponding to the effective atomic number in the atomic number image of the urinary system calculi; and determining multiple component percentages corresponding to the peaks of the multiple strip charts according to the multiple strip charts corresponding to the atomic number image of the urinary system calculi.

[0060] In the embodiments of the present disclosure, the method for determining multiple component percentages corresponding to the peaks of multiple strip charts according to the multiple strip charts corresponding to the atomic number image of the urinary system calculi includes: obtaining the set strip chart color and / or the set strip chart width; extracting multiple strip charts corresponding to the effective atomic number in the atomic number image of the urinary system calculi that meet the set strip chart color and / or the set strip chart width according to the set strip chart color and / or the set strip chart width; and determining multiple component percentages corresponding to the peaks of the multiple strip charts according to the multiple strip charts corresponding to the atomic number image of the urinary system calculi that meet the set strip chart color and / or the set strip chart width.

[0061] In the embodiments of the present disclosure and other possible embodiments, those skilled in the art can configure the set strip chart color configuration and / or the set strip chart width according to actual needs. For example, the set strip chart color is configured as yellow.

[0062] In embodiments of the present disclosure and other possible embodiments, before extracting multiple bar graphs corresponding to the effective atomic number in the atomic number image of the urinary system stones or extracting multiple bar graphs corresponding to the effective atomic number that meet the set bar graph color and / or set bar graph width in the atomic number image of the urinary system stones, the atomic number image of the urinary system stones is corrected to obtain a corrected atomic number image of the urinary system stones; multiple component percentages corresponding to the peaks of multiple bar graphs corresponding to the effective atomic number in the corrected atomic number image of the urinary system stones are extracted, or multiple component percentages corresponding to the peaks of multiple bar graphs corresponding to the effective atomic number that meet the set bar graph color and / or set bar graph width in the corrected atomic number image of the urinary system stones are extracted.

[0063] In embodiments of the present disclosure and other possible embodiments, the method for correcting the atomic number image of the urinary system stones to obtain a corrected atomic number image of the urinary system stones includes: performing at least one correction processing operation such as angle adjustment, contrast enhancement, and scaling on the atomic number image of the urinary system stones to obtain a corrected atomic number image of the urinary system stones for the subsequent extraction of multiple bar graphs corresponding to the effective atomic number.

[0064] In embodiments of the present disclosure and other possible embodiments, multiple component percentages corresponding to the peaks of multiple bar graphs corresponding to the effective atomic number in the atomic number image of the urinary system stones are extracted using optical character recognition technology; or multiple component percentages corresponding to the peaks of multiple bar graphs corresponding to the effective atomic number that meet the set bar graph color and / or set bar graph width in the corrected atomic number image of the urinary system stones are extracted using optical character recognition technology.

[0065] In embodiments of the present disclosure and other possible embodiments, optical character recognition technology (OCR) is a computer vision technology that uses image processing and machine learning algorithms to recognize and extract text content in an image and convert it into a machine-readable and editable text format.

[0066] In an embodiment of the present disclosure, the method for identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and the multiple preset stone type component percentage ranges includes: obtaining a preset component percentage less than the minimum value of the interval in the preset stone type component percentage range; if the component percentages corresponding to the multiple bar graphs are less than the preset component percentage, deleting the component percentages less than the preset component percentage from the multiple component percentages corresponding to the multiple bar graphs; otherwise, retaining the component percentages greater than or equal to the preset component percentage; and identifying the stone type corresponding to the dual-energy abdominal image based on the retained multiple component percentages and the multiple preset stone type component percentage ranges.

[0067] In the embodiments of the present disclosure and other possible embodiments, those skilled in the art can configure the preset component percentage less than the minimum value of the interval in the preset stone type component percentage range according to actual needs. For example, the preset component percentage less than the minimum value of the interval in the preset stone type component percentage range is configured to 5%.

[0068] In the embodiments of the present disclosure and other possible embodiments, the implementation of OCR requires calling the API of the GOT_OCR2 tool and setting its parameters such as language and text box positioning; the atomic number peak recognition and morphological calculation need to use the NumPy, SciPy or OpenCV libraries of Python to perform the extraction of the multiple component percentages corresponding to the peaks of the multiple bar graphs corresponding to the effective atomic numbers in the urinary tract stone atomic number image or the multiple component percentages corresponding to the peaks of the multiple bar graphs corresponding to the effective atomic numbers that meet the preset bar graph color and / or preset bar graph width in the urinary tract stone atomic number image.

[0069] Step S103: Identify the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and the multiple preset stone type component percentage ranges.

[0070] In the embodiments of the present disclosure and other possible embodiments, the method for identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and the multiple preset stone type component percentage ranges includes: if the component percentage in the multiple component percentages is within the range of a certain preset stone type component percentage range in the multiple preset stone type component percentage ranges; configuring the stone type corresponding to the certain preset stone type component percentage range as the stone type corresponding to the component percentage within the range.

[0071] In an embodiment of the present disclosure, if there is an overlapping interval in the set stone type component percentage intervals corresponding to at least two component percentages among the multiple component percentages corresponding to the multiple bar graphs, obtain the iodine-based image corresponding to the dual-energy abdominal image; based on the iodine-based image corresponding to the dual-energy abdominal image, identify the stone type corresponding to the overlapping interval.

[0072] In an embodiment of the present disclosure, the method for identifying the stone type corresponding to the overlapping interval based on the iodine-based image corresponding to the dual-energy abdominal image includes: determining the stone gray value of the iodine-based image corresponding to the dual-energy abdominal image; respectively calculating the average gray value and the uniformity corresponding to the stone gray value; if the average gray value is greater than the set average gray value and the uniformity is greater than the set uniformity, configure the stone type as calcium oxalate dihydrate stone; otherwise, configure the stone type as struvite stone or carbonate apatite stone.

[0073] In an embodiment of the present disclosure and other possible embodiments, those skilled in the art can configure the set average gray value and the set uniformity according to actual needs.

[0074] In an embodiment of the present disclosure and other possible embodiments, uniformity = (maximum value - minimum value) / (2 * average value) × 100%. Wherein, the maximum value represents the maximum value corresponding to the stone gray value, the minimum value represents the minimum value corresponding to the stone gray value, and the average value represents the average gray value corresponding to the stone gray value.

[0075] In an embodiment of the present disclosure, the multiple set stone type component percentage intervals include one or several of: uric acid stone component percentage interval, calcium oxalate monohydrate stone component percentage interval, calcium oxalate dihydrate stone component percentage interval, carbonate apatite stone component percentage interval, calcium carbonate phosphate stone component percentage interval, struvite stone component percentage interval.

[0076] In an embodiment of the present disclosure, the uric acid stone component percentage interval is configured as 6.5 - 10.5, the calcium oxalate monohydrate stone component percentage interval is configured as 13.3 - 14.0, the calcium oxalate dihydrate stone component percentage interval is configured as 12.0 - 13.3, the carbonate apatite stone component percentage interval is configured as 14.0 - 15.0, the calcium carbonate phosphate stone component percentage interval is configured as greater than 12.5, and the struvite stone component percentage interval is configured as less than 12.5.

[0077] In embodiments of the present disclosure and other possible embodiments, the Zeff peak value (component percentage range) of uric acid stones is between 6.5 and 10.5; the Zeff peak value of calcium oxalate monohydrate stones is between 13.3 and 14.0; the Zeff peak value of calcium oxalate dihydrate stones is between 12.0 and 13.3; the Zeff peak value of carbonate apatite stones is between 14.0 and 15.0; the Zeff peak value of calcium carbonate phosphate stones is greater than 12.5 and all have CT value images with non-uniform density; the Zeff peak of struvite stones is less than 12.5 and all have CT value images with non-uniform density.

[0078] In embodiments of the present disclosure and other possible embodiments, the method for identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and the multiple set stone type component percentage ranges includes: constructing a stone component comparison table according to the multiple set stone type component percentage ranges; and performing a look-up and comparison operation on the multiple component percentages corresponding to the multiple bar graphs based on the stone component comparison table to identify the stone type corresponding to the dual-energy abdominal image.

[0079] In an embodiment of the present disclosure, a method for intelligent analysis of urinary stone components is also proposed, which is applied to stone information extraction and includes: the method for intelligent analysis of urinary stone components applied to stone type identification as described above; and using a preset segmentation model (kidney / bladder / ureter / stone segmentation model) to segment the stones in the kidneys, ureters, and bladder in the dual-energy abdominal image to obtain a stone mask image; and extracting one or several pieces of information from the stone position, stone shape, stone gray value, major axis and minor axis corresponding to the stone, and stone volume corresponding to each stone type based on the stone mask image and / or the dual-energy abdominal image.

[0080] In an embodiment of the present disclosure, the method for extracting one or several pieces of information from the stone position, stone shape, stone gray value, major axis and minor axis corresponding to the stone, and stone volume corresponding to each stone type based on the stone mask image and / or the dual-energy abdominal image includes: respectively extracting a three-dimensional stone image with the original gray value from the three-dimensional dual-energy abdominal image based on each two-dimensional slice dual-energy abdominal image in the three-dimensional dual-energy abdominal image and the two-dimensional slice stone mask image corresponding to the corresponding slice; and respectively calculating the average gray value corresponding to each stone type in the three-dimensional stone mask image to determine the stone gray value corresponding to each stone type.

[0081] In embodiments of the present disclosure and other possible embodiments, the method for extracting a three-dimensional stone image with original gray values from the three-dimensional dual-energy abdominal image respectively based on each two-dimensional slice dual-energy abdominal image in the three-dimensional dual-energy abdominal image and the two-dimensional slice stone mask image in the corresponding slice of the stone mask image includes: performing a multiplication operation on each two-dimensional slice dual-energy abdominal image in the three-dimensional dual-energy abdominal image and the two-dimensional slice stone mask image in the corresponding slice of the stone mask image to obtain a three-dimensional stone gray value image; based on the mask values and stone spatial positions corresponding to each stone type in the two-dimensional slice stone mask image of the three-dimensional stone gray value image and the corresponding slice of the stone mask image, performing gray value restoration on the three-dimensional stone gray value image to obtain a stone image with original gray values.

[0082] In embodiments of the present disclosure and other possible embodiments, the method for performing gray value restoration on the three-dimensional stone gray value image based on the mask values and stone spatial positions corresponding to each stone type in the two-dimensional slice stone mask image of the three-dimensional stone gray value image and the corresponding slice of the stone mask image to obtain a stone image with original gray values includes: respectively extracting the mask values and stone spatial positions corresponding to each stone type in the two-dimensional slice stone mask image of the stone mask image; based on the stone spatial positions corresponding to each stone type, respectively performing stone type spatial position localization on the three-dimensional stone gray value image to obtain spatial position localization images corresponding to each stone type in the two-dimensional slice stone gray value image of the three-dimensional stone gray value image; dividing the spatial position localization images corresponding to each stone type in the two-dimensional slice stone gray value image of the three-dimensional stone gray value image by the mask values corresponding to the stone spatial positions to obtain a stone image with original gray values.

[0083] In embodiments of the present disclosure, the stone areas corresponding to each stone type in each two-dimensional slice stone mask image of the three-dimensional stone mask image are respectively calculated; a plurality of stone areas are obtained; the major axis and minor axis corresponding to the largest stone area among the plurality of stone areas corresponding to each stone type are respectively calculated to obtain the major axis and minor axis corresponding to each stone type; edge detection is respectively performed on the stones corresponding to each stone type on the stone mask image to obtain three-dimensional stone edge mask lines; fitting is respectively performed on the three-dimensional stone edge mask lines corresponding to each stone type to obtain the stone shapes corresponding to each stone type; three-dimensional reconstruction is respectively performed on the mask images corresponding to each stone type in the stone mask image to obtain the stone volumes corresponding to each stone type.

[0084] In an embodiment of the present disclosure, using a preset segmentation model (kidney / bladder / ureter / stone segmentation model), a medical image is segmented for the kidney and / or ureter and / or bladder, obtaining a three-dimensional kidney and / or ureter and / or bladder mask image; according to the three-dimensional kidney and / or ureter and / or bladder mask image, the kidney and / or ureter and / or bladder masks in the three-dimensional kidney and / or ureter and / or bladder mask image are configured as a left kidney and / or ureter and / or bladder mask and a right kidney and / or ureter and / or bladder mask; based on the spatial position relationship between the stone mask in the stone mask image and the left and right kidney and / or ureter and / or bladder masks in the three-dimensional kidney and / or ureter and / or bladder mask image, the stone positions corresponding to each of the stone types are determined.

[0085] In the embodiment of the present disclosure and other possible embodiments, the method for determining the stone positions corresponding to each of the stone types based on the spatial position relationship between the stone mask in the stone mask image and the left and right kidney and / or ureter and / or bladder masks in the three-dimensional kidney and / or ureter and / or bladder mask image includes: extracting a plurality of two-dimensional slice stone mask images corresponding to each of the stone types from the three-dimensional stone mask image; if the spatial position of the stone mask corresponding to the plurality of two-dimensional slice stone mask images is within the spatial position of the left kidney and / or ureter and / or bladder mask in the two-dimensional slice stone mask image corresponding to the corresponding slice of the three-dimensional kidney and / or ureter and / or bladder mask image, the stone corresponding to the spatial position of the stone mask is configured as a left kidney and / or ureter and / or bladder stone; if the spatial position of the stone mask corresponding to the plurality of two-dimensional slice stone mask images is within the spatial position of the right kidney and / or ureter and / or bladder mask in the two-dimensional slice stone mask image corresponding to the corresponding slice of the three-dimensional kidney and / or ureter and / or bladder mask image, the stone corresponding to the spatial position of the stone mask is configured as a right kidney and / or ureter and / or bladder stone.

[0086] In an embodiment of the present disclosure and other possible embodiments, the method for segmenting stones in the kidneys, ureters, and bladder from the dual-energy abdominal images to obtain stone mask images includes: obtaining a preset segmentation network, dual-energy abdominal training images for training the preset segmentation network, and a multi-mask label fusion image corresponding to the dual-energy abdominal training images and representing the kidneys, ureters, and stones; training the preset segmentation network with the dual-energy abdominal training images and their corresponding multi-mask label fusion images to obtain a corresponding stone segmentation model (preset segmentation model, kidney / bladder / ureter / stone segmentation model); and segmenting stones in the kidneys, ureters, and bladder from the dual-energy abdominal images based on the stone segmentation model to obtain stone mask images.

[0087] In an embodiment of the present disclosure and other possible embodiments, the method for constructing a multi-mask label image fusion image respectively representing the kidneys, bladder, ureters, and stones includes: obtaining a kidney mask label image, a bladder mask label image, a ureter mask label image, and a stone mask image corresponding to the dual-energy abdominal training images; respectively assigning a first mask value, a second mask value, a third mask value, and a fourth mask value to the kidney mask label image, the bladder mask label image, the ureter mask label image, and the stone mask image; and fusing the kidney mask label image corresponding to the first mask value, the bladder mask label image corresponding to the second mask value, the ureter mask image corresponding to the third mask value, and the stone mask image corresponding to the fourth mask value to obtain a multi-mask label fusion image corresponding to the dual-energy abdominal training images and respectively representing the kidneys, bladder, ureters, and stones.

[0088] In the embodiments of the present disclosure and other possible embodiments, the method of fusing the kidney mask label image corresponding to the first mask value, the bladder mask label image corresponding to the second mask value, the ureter mask image corresponding to the third mask value, and the stone mask image corresponding to the fourth mask value to obtain the multi-mask label fusion image representing the kidney, bladder, ureter, and stone corresponding to the dual-energy abdominal training image includes: respectively determining a first spatial position corresponding to the first mask value in the kidney mask label image, a second spatial position corresponding to the second mask value in the bladder mask label image, a third spatial position corresponding to the third mask value in the ureter mask image, and a fourth spatial position corresponding to the fourth mask value in the stone mask image; if the first spatial position, the second spatial position, and the third spatial position overlap with the fourth spatial position, updating the mask value corresponding to the overlapping spatial position to the fourth mask value corresponding to the fourth spatial position; otherwise, retaining the first mask value corresponding to the first spatial position, the second mask value corresponding to the second spatial position, and the third mask value corresponding to the third spatial position to obtain the multi-mask label fusion image representing the kidney, bladder, ureter, and stone corresponding to the dual-energy abdominal training image.

[0089] In the embodiments of the present disclosure and other possible embodiments, before obtaining the organ mask label image corresponding to at least one organ of the kidney, ureter, and bladder in the dual-energy abdominal training image and the stone mask label image within the organ, the stone mask label image is determined based on the iodine-based image corresponding to the dual-energy abdominal training image, and the organ mask label image is determined based on the water-based image corresponding to the dual-energy abdominal training image.

[0090] In the embodiments of the present disclosure and other possible embodiments, the method of determining the stone mask label image based on the iodine-based image corresponding to the dual-energy abdominal training image includes: performing stone delineation on the iodine-based image corresponding to the dual-energy abdominal training image to determine the stone mask label image.

[0091] In the embodiments of the present disclosure and other possible embodiments, the method of determining the organ mask label image based on the water-based image corresponding to the dual-energy abdominal training image includes: performing organ delineation on the water-based image corresponding to the dual-energy abdominal training image to determine the organ mask label image.

[0092] In the embodiments of the present disclosure and other possible embodiments, during the process of training the preset segmentation network by using the dual-energy abdominal training images and their corresponding multi-mask label fusion images, the following steps are included: obtaining the organ loss function corresponding to the organ and its corresponding organ loss weight value, the stone loss function corresponding to the stone and its corresponding stone loss weight value that is less than the organ loss weight value; during the process of training the preset segmentation network, if the organ loss value corresponding to the organ loss function is less than the first set loss value and / or the total loss value corresponding to the organ loss function and the stone loss function is less than the second set loss value, then during each training process of continuously training the preset segmentation network, the stone loss weight value is increased and regulated according to a set ratio to obtain a stone loss regulation weight value; according to the stone loss regulation weight value, determining the organ loss regulation weight value corresponding to the organ loss weight value; based on the stone loss regulation weight value and the organ loss regulation weight value, calculating the stone loss value corresponding to the stone loss function and the total loss value corresponding to the organ loss function and the stone loss function; if the stone loss value is less than the third set loss value and / or the total loss value corresponding to the organ loss function and the stone loss function is less than the fourth set loss value, then stop training the preset segmentation network.

[0093] In the embodiments of the present disclosure and other possible embodiments, the method for determining the organ loss regulation weight value corresponding to the organ loss weight value according to the stone loss regulation weight value includes: obtaining the set configuration value corresponding to the sum of the organ loss weight value and the stone loss weight value; subtracting the stone loss regulation weight value from the set configuration value to determine the organ loss regulation weight value corresponding to the organ loss weight value.

[0094] Among them, in the embodiments of the present disclosure and other possible embodiments, the set configuration value corresponding to the sum of the organ loss weight value and the stone loss weight value is configured to be 1.

[0095] Among them, in the embodiments of the present disclosure and other possible embodiments, the organ loss function and the stone loss function are respectively configured as one or several of a cross-entropy loss function, a Dice loss function, a Focal loss function, a Tversky loss function, a Tversky loss function, and an IoU loss function.

[0096] Among them, in the embodiments of the present disclosure and other possible embodiments, the preset segmentation network is configured as one or several of segmentation networks such as Unet, ResUnet, Unet++, ResUnet++, nnUnet, SegNet, PSPNet, DeepLab, RefineNet, Medformer or their improved segmentation networks.

[0097] In the embodiments of the present disclosure and other possible embodiments, the method of using the peak atomic number of spectral CT as a method for evaluating the composition of urinary tract stones can accurately evaluate the common urinary tract stone components because the peak atomic numbers of different components have certain specificities. This technical method is applicable not only to urinary tract stones with a single component (manifested as one atomic number peak) but also to urinary tract stones with multiple components (manifested as multiple atomic number peaks), and can meet the clinical diagnostic needs where there are more mixed-component stones than single-component stones.

[0098] The execution subject of the intelligent analysis method for urinary tract stone components can be an intelligent analysis device / system for urinary tract stone components. For example, the intelligent analysis method for urinary tract stone components can be executed by a terminal device, a server, or other processing devices. Among them, the terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementation manners, the intelligent analysis method for urinary tract stone components can be implemented by a processor calling computer-readable instructions stored in a memory.

[0099] In some embodiments, the functions or modules included in the device or system provided in the embodiments of the present disclosure can be used to execute the method described in the above embodiments of the intelligent analysis method for urinary tract stone components. Its specific implementation can refer to the description of the above embodiments of the intelligent analysis method for urinary tract stone components. For the sake of brevity, it will not be elaborated here.

[0100] According to one aspect of the embodiments of the present disclosure, there is provided an intelligent analysis device / system for urinary calculus components, which is applied to the identification of calculus types and includes: an acquisition unit for acquiring an atomic number image of a urinary calculus corresponding to a dual-energy abdominal image; an extraction unit for extracting multiple component percentages corresponding to the peaks of multiple bar graphs corresponding to the effective atomic numbers in the atomic number image of the urinary calculus; and an identification unit for identifying the calculus type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and multiple set calculus type component percentage ranges. This is to solve the technical problem that it is difficult to distinguish or identify mixed urinary calculi of different components, so that the diagnostic requirements of mixed component calculi being more than single component calculi in clinical practice cannot be met.

[0101] According to one aspect of the embodiments of the present disclosure, there is provided an intelligent analysis device / system for urinary calculus components, which is applied to the extraction of calculus information and includes: an acquisition unit for acquiring an atomic number image of a urinary calculus corresponding to a dual-energy abdominal image; a component percentage extraction unit for extracting multiple component percentages corresponding to the peaks of multiple bar graphs corresponding to the effective atomic numbers in the atomic number image of the urinary calculus; an identification unit for identifying the calculus type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and multiple set calculus type component percentage ranges; a segmentation unit for segmenting the calculi in the kidneys, ureters, and bladder in the dual-energy abdominal image to obtain a calculus mask image; and a calculus information extraction unit for extracting one or several pieces of information such as the calculus position, calculus shape, calculus gray value, major axis and minor axis corresponding to the calculus, and calculus volume corresponding to each calculus type based on the calculus mask image and / or the dual-energy abdominal image. This is to solve the technical problem that it is difficult to distinguish or identify mixed urinary calculi of different components, so that the diagnostic requirements of mixed component calculi being more than single component calculi in clinical practice cannot be met.

[0102] According to one aspect of the embodiments of the present disclosure, there is provided an intelligent analysis device / system for urinary calculus components, including: an electronic device, where the electronic device is configured with a processor and a memory for storing instructions executable by the processor; wherein, the processor is configured to call the instructions stored in the memory to execute the above-mentioned intelligent analysis method for urinary calculus components. This is to solve the technical problem that it is difficult to distinguish or identify mixed urinary calculi of different components, so that the diagnostic requirements of mixed component calculi being more than single component calculi in clinical practice cannot be met.

[0103] According to one aspect of the embodiments of the present disclosure, there is provided an intelligent analysis device / system for urinary calculus components, including: a processor; a memory for storing instructions executable by the processor; wherein, the processor is configured to call the instructions stored in the memory to execute the above-mentioned intelligent analysis method for urinary calculus components. To solve the technical problem that it is difficult to identify or recognize mixed urinary calculi of different components, so that the diagnostic requirements of mixed-component calculi being more than single-component calculi in clinical practice cannot be met.

[0104] According to one aspect of the embodiments of the present disclosure, there is provided an intelligent analysis device / system for urinary calculus components, including: a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the above-mentioned intelligent analysis method for urinary calculus components is implemented. To solve the technical problem that it is difficult to identify or recognize mixed urinary calculi of different components, so that the diagnostic requirements of mixed-component calculi being more than single-component calculi in clinical practice cannot be met.

[0105] According to one aspect of the embodiments of the present disclosure, there is provided an intelligent analysis device / system for urinary calculus components, including: a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, the above-mentioned intelligent analysis method for urinary calculus components is implemented. To solve the technical problem that it is difficult to identify or recognize mixed urinary calculi of different components, so that the diagnostic requirements of mixed-component calculi being more than single-component calculi in clinical practice cannot be met.

[0106] Those skilled in the art can understand that in the above-mentioned intelligent analysis method for urinary calculus components in the specific implementation manner, the writing order of each step does not mean a strict execution order that constitutes any limitation to the implementation process, and the specific execution order of each step should be determined by its function and possible internal logic.

[0107] The embodiments of the present disclosure also propose a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the above-mentioned intelligent analysis method for urinary calculus components is implemented. The computer-readable storage medium can be a non-volatile computer-readable storage medium. To solve the technical problem that it is difficult to identify or recognize mixed urinary calculi of different components, so that the diagnostic requirements of mixed-component calculi being more than single-component calculi in clinical practice cannot be met.

[0108] Embodiments of the present disclosure also provide an electronic device, including: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to perform the above-mentioned intelligent analysis method for urinary calculus components. The electronic device may be provided as a terminal, a server or other forms of devices. This is to solve the technical problem that it is difficult to identify or recognize mixed urinary calculi of different components, so that the clinical diagnostic requirements for mixed-component calculi, which are more than single-component calculi, cannot be met.

[0109] Figure 2 FIG. 4 is a block diagram of an electronic device 800 shown according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or other terminals.

[0110] Referring to Figure 2 FIG. 4, the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0111] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communication, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0112] The memory 804 is configured to store various types of data to support the operation of the electronic device 800. Examples of these data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk.

[0113] The power supply component 806 provides power for various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.

[0114] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can not only sense the boundaries of the touch or swipe actions, but also detect the duration and pressure associated with the touch or swipe operations. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.

[0115] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC) that is configured to receive external audio signals when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 further includes a speaker for outputting audio signals.

[0116] The I / O interface 812 provides an interface between the processing component 802 and a peripheral interface module, which can be a keyboard, a click wheel, buttons, etc. These buttons can include, but are not limited to: a home button, a volume button, a power-on button, and a lock button.

[0117] The sensor assembly 814 includes one or more sensors for providing an assessment of various aspects of the state of the electronic device 800. For example, the sensor assembly 814 can detect the on / off state of the electronic device 800, the relative positioning of components, such as the display and keypad of the electronic device 800. The sensor assembly 814 can also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the change in temperature of the electronic device 800. The sensor assembly 814 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 can also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 can also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0118] The communication component 816 is configured to facilitate communication between the electronic device 800 and other devices in a wired or wireless manner. The electronic device 800 can access a wireless network based on communication standards, such as WiFi, 2G, or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0119] In an exemplary embodiment, the electronic device 800 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above method.

[0120] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions, and the above computer program instructions can be executed by a processor 820 of the electronic device 800 to complete the above method.

[0121] Figure 3 is a block diagram of an electronic device 1900 shown in accordance with an exemplary embodiment. For example, the electronic device 1900 can be provided as a server. Referring to Figure 3, the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by a memory 1932 for storing instructions executable by the processing component 1922, such as application programs. The application programs stored in the memory 1932 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0122] The electronic device 1900 may further include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.

[0123] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as the memory 1932 including computer program instructions, and the above computer program instructions can be executed by the processing component 1922 of the electronic device 1900 to complete the above method.

[0124] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0125] The computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device, such as a punched card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium used herein is not construed as being an instantaneous signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0126] The computer-readable program instructions described herein can be downloaded to various computing / processing devices from a computer-readable storage medium or downloaded to an external computer or external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0127] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0128] Aspects of the present disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0129] These computer-readable program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce a machine such that the instructions, when executed by the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that causes a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises a manufacture including instructions which implement various aspects of the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0130] The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process such that the instructions executed on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0131] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of code, or a portion of an instruction, and the module, segment of code, or portion of an instruction may include one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by special-purpose hardware-based systems that perform the specified functions or acts, or by combinations of special-purpose hardware and computer instructions.

[0132] The embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the market, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. An intelligent analysis method for the composition of urinary tract stones, applied to the identification of stone types, characterized in that, Including: Obtaining a urinary system stone atomic number image corresponding to a dual-energy abdominal image; Extracting multiple component percentages corresponding to the peaks of multiple strip charts corresponding to the effective atomic numbers in the urinary system stone atomic number image; Identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple strip charts and multiple set stone type component percentage ranges.

2. The intelligent analysis method for urinary calculus components according to claim 1, which is applied to the identification of calculus types, is characterized in that, The method for extracting multiple component percentages corresponding to the peaks of multiple strip charts corresponding to the effective atomic numbers in the urinary system stone atomic number image includes: Obtaining a set strip chart color and / or a set strip chart width; Extracting multiple strip charts corresponding to the effective atomic numbers in the urinary system stone atomic number image that meet the set strip chart color and / or the set strip chart width according to the set strip chart color and / or the set strip chart width; Determining multiple component percentages corresponding to the peaks of the multiple strip charts according to the multiple strip charts in the urinary system stone atomic number image that meet the set strip chart color and / or the set strip chart width.

3. The intelligent analysis method for urinary calculus components according to any one of claims 1 or 2, when applied to the identification of calculus types, is characterized in that The method for identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple strip charts and multiple set stone type component percentage ranges includes: Obtaining a set component percentage smaller than the minimum value of the interval in the set stone type component percentage range; If the component percentages corresponding to the multiple strip charts are smaller than the set component percentage, deleting the component percentages corresponding to those smaller than the set component percentage from the multiple component percentages corresponding to the multiple strip charts; Otherwise, retaining the component percentages corresponding to those greater than or equal to the set component percentage; Identifying the stone type corresponding to the dual-energy abdominal image based on the retained multiple component percentages and multiple set stone type component percentage ranges.

4. The intelligent analysis method for urinary calculus composition according to claim 3, which is applied to calculus type identification, is characterized in that The method for identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple strip charts and multiple set stone type component percentage ranges includes: If the component percentage in the multiple component percentages is within the range of a certain set stone type component percentage range in the multiple set stone type component percentage ranges; configuring the stone type corresponding to the certain set stone type component percentage range as the stone type corresponding to the component percentage within the range; and / or, The method for identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple strip charts and multiple set stone type component percentage ranges further includes: If there is an overlapping interval in the set stone type component percentage ranges corresponding to at least two of the multiple component percentages corresponding to the multiple strip charts, obtaining an iodine-based image corresponding to the dual-energy abdominal image; identifying the stone type corresponding to the overlapping interval based on the iodine-based image corresponding to the dual-energy abdominal image; and / or, The method for identifying the stone type corresponding to the overlapping interval based on the iodine-based image corresponding to the dual-energy abdominal image includes: determining the stone gray value of the iodine-based image corresponding to the dual-energy abdominal image; respectively calculating the average gray value and uniformity corresponding to the stone gray value; if the average gray value is greater than the set average gray value and the uniformity is greater than the set uniformity, configuring the stone type as calcium oxalate dihydrate stone; otherwise, configuring the stone type as struvite stone or carbonate apatite stone.

5. The intelligent analysis method for urinary calculus components according to any one of claims 1-4, when applied to stone type identification, is characterized in that The multiple set stone type component percentage intervals include: one or several of the uric acid stone component percentage interval, calcium oxalate monohydrate stone component percentage interval, calcium oxalate dihydrate stone component percentage interval, carbonate apatite stone component percentage interval, calcium carbonate phosphate stone component percentage interval, struvite stone component percentage interval; and / or, The uric acid stone component percentage interval is configured as 6.5 - 10.5, the calcium oxalate monohydrate stone component percentage interval is configured as 13.3 - 14.0, the calcium oxalate dihydrate stone component percentage interval is configured as 12.0 - 13.3, the carbonate apatite stone component percentage interval is configured as 14.0 - 15.0, the calcium carbonate phosphate stone component percentage interval is configured as greater than 12.5, and the struvite stone component percentage interval is configured as less than 12.

5.

6. An intelligent analysis method for the composition of urinary tract stones, applied to the extraction of stone information, is characterized in that, Including: The intelligent analysis method for urinary system stone components applied to stone type identification according to any one of claims 1 - 5; And, Segmenting the stones in the kidneys, ureters and bladder in the dual-energy abdominal image to obtain a stone mask image; Based on the stone mask image and / or the dual-energy abdominal image, extracting one or several pieces of information such as the stone position, stone shape, stone gray value, major axis and minor axis corresponding to the stone, and stone volume corresponding to each stone type.

7. The intelligent analysis method for urinary calculus components according to claim 6, which is applied to calculus information extraction, is characterized in that The method for extracting one or several pieces of information such as the stone position, stone shape, stone gray value, major axis and minor axis corresponding to the stone, and stone volume corresponding to each stone type based on the stone mask image and / or the dual-energy abdominal image includes: Respectively based on each two-dimensional slice dual-energy abdominal image in the three-dimensional dual-energy abdominal image and the two-dimensional slice stone mask image corresponding to the corresponding slice, extracting a three-dimensional stone image with the original gray value from the three-dimensional dual-energy abdominal image; respectively calculating the average gray value corresponding to each stone type in the three-dimensional stone mask image to determine the stone gray value corresponding to each stone type; and / or, Calculate the stone areas corresponding to each stone type in each two-dimensional slice stone mask image in the three-dimensional stone mask image respectively; obtain a plurality of stone areas; calculate the major axis and minor axis corresponding to the largest stone area among the plurality of stone areas corresponding to each stone type respectively, and obtain the major axis and minor axis corresponding to each stone type; perform edge detection on the stones corresponding to each stone type on the stone mask image respectively to obtain a three-dimensional stone edge mask line; fit the three-dimensional stone edge mask lines corresponding to each stone type respectively to obtain the stone shapes corresponding to each stone type; perform three-dimensional reconstruction on the mask images corresponding to each stone type in the stone mask image respectively to obtain the stone volumes corresponding to each stone type; and / or, Segment the medical image for the kidney and / or ureter and / or bladder to obtain a three-dimensional kidney and / or ureter and / or bladder mask image; according to the three-dimensional kidney and / or ureter and / or bladder mask image, configure the kidney and / or ureter and / or bladder masks in the three-dimensional kidney and / or ureter and / or bladder mask image as the left kidney and / or ureter and / or bladder mask and the right kidney and / or ureter and / or bladder mask; based on the spatial position relationship between the stone masks in the stone mask image and the left kidney and / or ureter and / or bladder mask and the right kidney and / or ureter and / or bladder mask in the three-dimensional kidney and / or ureter and / or bladder mask image, determine the stone positions corresponding to each stone type.

8. An intelligent analysis system for urinary calculus components, which is applied to the identification of calculus types, is characterized in that, Comprising: An acquisition unit, configured to acquire a urinary tract stone atomic number image corresponding to a dual-energy abdominal image; An extraction unit, configured to extract multiple component percentages corresponding to the peaks of multiple strip charts corresponding to the effective atomic numbers in the urinary tract stone atomic number image; An identification unit, configured to identify the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple strip charts and multiple set stone type component percentage ranges.

9. An intelligent analysis system for urinary calculus components, applied to stone information extraction, is characterized in that Comprising: An acquisition unit, configured to acquire a urinary tract stone atomic number image corresponding to a dual-energy abdominal image; A component percentage extraction unit, configured to extract multiple component percentages corresponding to the peaks of multiple strip charts corresponding to the effective atomic numbers in the urinary tract stone atomic number image; An identification unit, configured to identify the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple strip charts and multiple set stone type component percentage ranges; A segmentation unit, configured to segment the stones in the kidney, ureter and bladder in the dual-energy abdominal image to obtain a stone mask image; A stone information extraction unit, configured to extract one piece of information or several pieces of information among the stone position, stone shape, stone gray value, major axis and minor axis corresponding to the stone, and stone volume corresponding to each stone type based on the stone mask image and / or the dual-energy abdominal image.

10. An intelligent analysis system for the components of urinary tract stones, characterized in that, Comprising: An electronic device, the electronic device being configured with a processor and a memory for storing processor-executable instructions; wherein, the processor is configured to call the instructions stored in the memory to execute the intelligent analysis method for urinary calculus components according to any one of claims 1 to 7; or, Comprising: a processor; a memory for storing processor-executable instructions; wherein, the processor is configured to call the instructions stored in the memory to execute the intelligent analysis method for urinary calculus components according to any one of claims 1 to 7; or, Comprising: a computer-readable storage medium, on which computer program instructions are stored, and the computer program instructions, when executed by a processor, implement the intelligent analysis method for urinary calculus components according to any one of claims 1 to 7; or, Comprising: a computer program product, including a computer program / instructions, and the computer program / instructions, when executed by a processor, implement the intelligent analysis method for urinary calculus components according to any one of claims 1 to 7.

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