Intelligent Analysis Method and System for the Composition of Urinary Tract Stones

Abstract

This disclosure relates to an intelligent analysis method and system for the composition of urinary tract stones, belonging to the field of urinary tract stone composition analysis technology. The intelligent analysis method for urinary tract stone composition, applied to stone type identification, includes: acquiring an atomic number image of urinary tract stones corresponding to a dual-energy abdominal image; extracting multiple component percentages from multiple bar charts corresponding to the effective atomic numbers in the atomic number image of the urinary tract stones, and identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar charts and multiple preset stone type component percentage intervals. Embodiments of this disclosure can achieve intelligent identification of stone types.

Description

Technical Field

[0001] This disclosure relates to the field of urinary tract stone composition analysis technology, and in particular to an intelligent analysis method and system for urinary tract stone composition. Background Technology

[0002] Urinary tract stones are a common disease, with an incidence rate of approximately 5.6% in China, among which kidney stones are the most common. Urinary tract stones can cause clinical symptoms such as pain, difficulty urinating, infection, and hematuria. If left untreated, they can easily lead to kidney damage or even kidney failure, and can also increase the incidence of many chronic diseases, including osteoporosis and cardiovascular disease. Furthermore, urinary tract stones often recur, with approximately 40% of patients experiencing one or more recurrences. Identifying the composition of urinary tract stones is crucial for selecting appropriate treatment methods based on different components and for guiding interventions to prevent recurrence. For example, uric acid stones can be dissolved by urine alkalization as the first choice, with surgery performed only when necessary; struvite / magnesium ammonium phosphate stones usually require preoperative antibiotic treatment; calcium oxalate, carbonate apatite, and calcium carbonate stones often require surgical treatment. To prevent recurrence of urinary tract stones, it is often necessary to restrict and modify the patient's diet according to the stone composition, identify related metabolic diseases, and administer medication.

[0003] Currently, the compositional diagnosis of postoperative urinary tract stones mainly relies on infrared spectroscopy analysis of the stone samples. This is of some significance in preventing recurrence of urinary tract stones, but it does not help in selecting the appropriate stone treatment method preoperatively. Preoperative compositional diagnosis of urinary tract stones now primarily relies on CT values ​​from routine CT scans. Based on the CT value, uric acid and non-uric acid stones can be roughly distinguished; for example, a CT value below 800 HU is often a uric acid stone, but it cannot further differentiate non-uric acid stones (calcium oxalate monohydrate, calcium oxalate dihydrate, carbonate apatite, struvite, calcium carbonate, etc.). Furthermore, after the application of average atomic number values ​​from energy-dispersive spectroscopy (EDS) in clinical practice, it can accurately distinguish between uric acid and non-uric acid stones; however, further differentiation and subclassification diagnosis of non-uric acid stones remain challenging.

[0004] Routine CT scans cannot further differentiate between non-uric acid stones because even among single-component non-uric acid stones, there is still overlap in the CT values ​​of stones containing components such as calcium oxalate monohydrate, calcium oxalate dihydrate, carbonate apatite, struvite, and calcium carbonate. Therefore, CT values ​​alone cannot differentiate them. Furthermore, mixed-component stones are far more common clinically than single-component stones. Even stones formed from a mixture of two components will yield different average CT values ​​depending on the proportions of the two components, making it impossible to accurately identify mixed stones with different components.

[0005] The average atomic number (ATO) value of spectral CT can accurately distinguish between uric acid stones and non-uric acid stones, and also has a certain diagnostic specificity for single-component non-uric acid stones. However, the ATO value of spectral CT is not applicable to mixed-component stones, which account for the largest proportion in clinical practice. This is because the calculation method for the ATO value of mixed components is closely related to the percentage and type of different components. Therefore, the ATO value cannot accurately distinguish between mixed urinary tract stones of different components. Summary of the Invention

[0006] This disclosure presents a technical solution for an intelligent analysis method and system for the composition of urinary tract stones.

[0007] According to one aspect of this disclosure, an intelligent analysis method for the composition of urinary tract stones is provided for stone type identification, comprising: acquiring an atomic number image of urinary tract stones 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 of multiple bar charts corresponding to the effective atomic numbers in the atomic number image of urinary tract stones from the peak values ​​of multiple bar charts; and identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar charts and multiple preset stone type component percentage intervals.

[0008] Preferably, the method for extracting multiple component percentages of peak values ​​corresponding to multiple bar charts corresponding to effective atomic numbers in the atomic number image of urinary tract stones includes: extracting multiple bar charts corresponding to effective atomic numbers in the atomic number image of urinary tract stones; and determining multiple component percentages of peak values ​​corresponding to multiple bar charts based on the multiple bar charts corresponding to the atomic number image of urinary tract stones.

[0009] Preferably, the method for determining the percentage of multiple components corresponding to peak values ​​in multiple bar charts based on the atomic number image of the urinary tract stones includes: obtaining a set bar chart color and / or a set bar chart width; extracting multiple bar charts corresponding to valid atomic numbers that satisfy the set bar chart color and / or set bar chart width in the atomic number image of the urinary tract stones based on the set bar chart color and / or set bar chart width; and determining the percentage of multiple components corresponding to peak values ​​in the multiple bar charts based on the multiple bar charts that satisfy the set bar chart color and / or set bar chart width in the atomic number image of the urinary tract stones.

[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 charts and multiple set stone type component percentage intervals includes: obtaining the set component percentage corresponding to the minimum value of the intervals of the set stone type component percentages; if the component percentages corresponding to the multiple bar charts are less than the set component percentages, then deleting the component percentages less than the set component percentages from the multiple component percentages corresponding to the multiple bar charts; otherwise, retaining the component percentages greater than or equal to the set component percentages; and 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 intervals.

[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 charts and multiple set stone type component percentage intervals includes: if the component percentage in the multiple component percentages is within the range of a certain set stone type component percentage interval in the multiple set stone type component percentage intervals; configuring the stone type corresponding to the certain set stone type component percentage interval 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 charts and multiple set stone type component percentage intervals further includes: if at least two component percentages corresponding to the multiple component percentages of the multiple bar charts have overlapping intervals, then obtain the iodine-based image corresponding to the dual-energy abdominal image; and identify the stone type corresponding to the overlapping interval 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 region based on the iodine-based image corresponding to the dual-energy abdominal image includes: determining the calorific value of the calorific image of the iodine-based image corresponding to the dual-energy abdominal image; calculating the average gray value and uniformity corresponding to the calorific value; if the average gray value is greater than a set average gray value and the uniformity is greater than a set uniformity, then the stone type is configured as calcium oxalate dihydrate stone; otherwise, the stone type is configured as struvite stone or carbonate apatite stone.

[0014] Preferably, the plurality of set stone type component percentage ranges include one or more of the following: 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 stone component percentage range, and struvite stone component percentage range.

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

[0016] According to one aspect of this disclosure, a method for intelligent analysis of the composition of urinary tract stones is provided for stone information extraction, comprising: the intelligent analysis method for the composition of urinary tract stones applied to stone type identification as described above; and segmenting the stones in the kidneys, ureters, and bladder of the dual-energy abdominal image to obtain a stone mask image; and extracting one or more of the following information based on the stone mask image and / or the dual-energy abdominal image: stone location, stone shape, stone calorific value, major and minor diameters of the stone, and stone volume corresponding to each stone type.

[0017] Preferably, the method for extracting one or more of the following information based on the stone mask image and / or the dual-energy abdominal image: stone location, stone shape, stone calorific value, major and minor diameters of the stone, and stone volume, for each stone type, includes: extracting a three-dimensional stone image with original grayscale values ​​from the three-dimensional dual-energy abdominal image based on the two-dimensional slice stone mask image in each two-dimensional slice dual-energy abdominal image and the stone mask image of the corresponding slice; calculating the average grayscale value corresponding to each stone type in the three-dimensional stone mask image to determine the calorific value corresponding to each stone type.

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

[0019] Preferably, the method for restoring grayscale values ​​of the three-dimensional concretionary grayscale image based on the mask values ​​and spatial positions of the stones in the two-dimensional slice concretionary grayscale image of the three-dimensional concretionary grayscale image and its corresponding slice concretionary grayscale image, to obtain a concretionary grayscale image with original grayscale values, includes: extracting the mask values ​​and spatial positions of the stones corresponding to each stone type in the two-dimensional slice concretionary grayscale image of the concretionary grayscale image; locating the spatial positions of the stones in the three-dimensional concretionary grayscale image based on the spatial positions of the stones corresponding to each stone type, to obtain spatial position positioning images of the two-dimensional slice concretionary grayscale image of the three-dimensional concretionary grayscale image; dividing the spatial position positioning images of the two-dimensional slice concretionary grayscale image of the three-dimensional concretionary grayscale image by the mask values ​​corresponding to the spatial positions of the stones to obtain a concretionary grayscale image with original grayscale values.

[0020] Preferably, the stone area corresponding to each stone type in each two-dimensional slice stone mask image in the three-dimensional stone mask image is calculated respectively; multiple stone areas are obtained; the major and minor axes corresponding to the largest stone area among the multiple stone areas corresponding to each stone type are calculated respectively, and the major and minor axes corresponding to each stone type are obtained respectively; edge detection is performed on the stones corresponding to each stone type in the stone mask image respectively, and three-dimensional stone edge mask lines are obtained; the three-dimensional stone edge mask lines corresponding to each stone type are fitted respectively, and the stone shape corresponding to each stone type is obtained respectively; three-dimensional reconstruction is performed on the mask images corresponding to each stone type in the stone mask image respectively, and the stone volume corresponding to each stone type is obtained.

[0021] Preferably, the medical image is segmented into kidney and / or ureter and / or bladder to obtain a three-dimensional kidney and / or ureter and / or bladder mask image; based on 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 positional 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, the stone location corresponding to each stone type is determined.

[0022] Preferably, the method for determining the stone location corresponding to each stone type based on the spatial positional relationship between the stone mask in the stone mask image and the lateral 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 multiple 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 multiple two-dimensional slice stone mask images is in the three-dimensional kidney and / or ureter and / or bladder mask image... If the spatial location of the left kidney and / or ureter and / or bladder is within the mask space of the two-dimensional stone mask image corresponding to the slice, then the stone corresponding to the stone mask space location is configured as a left kidney and / or ureter and / or bladder stone; if the spatial location of the stone mask corresponding to the plurality of two-dimensional stone mask images is within the spatial location of the right kidney and / or ureter and / or bladder mask of the two-dimensional stone mask image corresponding to the three-dimensional kidney and / or ureter and / or bladder mask image, then the stone corresponding to the stone mask space location is configured as a right kidney and / or ureter and / or bladder stone.

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

[0024] Preferably, the method for constructing the multi-mask label image fusion image representing the kidney, bladder, ureter, and stone respectively includes: acquiring the kidney mask label image, ureter mask label image, and stone mask image corresponding to the dual-energy abdominal training image; 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 respectively; 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 representing the kidney, bladder, ureter, and stone respectively corresponding to the dual-energy abdominal training image.

[0025] Preferably, the method for 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 fused image representing the kidney, bladder, ureter, and stone respectively corresponding to the dual-energy abdominal training image includes: determining the first spatial position corresponding to the first mask value in the kidney mask label image, the second spatial position corresponding to the second mask value in the ureter mask label image, and the third spatial position corresponding to the third mask value in the stone mask image; if the first spatial position and the second spatial position overlap with the third spatial position, then update the mask value corresponding to the overlapping spatial position to the third mask value corresponding to the third spatial position; otherwise, retain 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 fused image representing the kidney, bladder, ureter, and stone respectively corresponding to the dual-energy abdominal training image.

[0026] According to one aspect of this disclosure, a smart analysis device / system for the composition of urinary tract stones is provided for stone type identification, including:

[0027] The acquisition unit is used to acquire the atomic number image of urinary tract stones corresponding to the dual-energy abdominal image;

[0028] The extraction unit is used to extract the percentage of multiple components of the peak values ​​corresponding to the effective atomic numbers in the atomic number image of the urinary tract stones;

[0029] The identification unit is used to identify the stone type corresponding to the dual-energy abdominal image based on the percentage of multiple components corresponding to the multiple bar charts and multiple set stone type component percentage ranges.

[0030] According to one aspect of this disclosure, an intelligent analysis device / system for urinary tract stone composition is provided for stone information extraction, including:

[0031] The acquisition unit is used to acquire the atomic number image of urinary tract stones corresponding to the dual-energy abdominal image;

[0032] The component percentage extraction unit is used to extract the component percentages of multiple peaks corresponding to multiple bar charts in the atomic number image of the urinary tract stones;

[0033] The identification unit is used to identify the type of stone corresponding to the dual-energy abdominal image based on the percentage of multiple components corresponding to the multiple bar charts and multiple set percentage ranges of stone type components.

[0034] The segmentation unit is used to segment the stones in the kidneys, ureters and bladder of the dual-energy abdominal image to obtain a stone mask image;

[0035] The stone information extraction unit is used to extract one or more of the following information based on the stone mask image and / or the dual-energy abdominal image: stone location, stone shape, stone calorific value, major and minor diameters of the stone, and stone volume for each stone type.

[0036] According to one aspect of this disclosure, an intelligent analysis device / system for the composition of urinary tract stones is provided, 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 invoke the instructions stored in the memory to execute the aforementioned intelligent analysis method for the composition of urinary tract stones.

[0037] According to one aspect of this disclosure, an intelligent analysis device / system for the composition of urinary tract stones is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the above-described intelligent analysis method for the composition of urinary tract stones.

[0038] According to one aspect of this disclosure, an intelligent analysis device / system for the composition of urinary tract stones is provided, comprising: a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the above-described intelligent analysis method for the composition of urinary tract stones.

[0039] According to one aspect of this disclosure, an intelligent analysis device / system for the composition of urinary tract stones is provided, comprising: a computer program product including a computer program / instructions, which, when executed by a processor, implements the aforementioned intelligent analysis method for the composition of urinary tract stones.

[0040] In this disclosure, an intelligent analysis method and system for the composition of urinary tract stones are proposed to solve the technical problem that it is difficult to identify or recognize mixed urinary tract stones with different components, thus failing to meet the clinical diagnostic needs of mixed-component stones as there are more stones with single components than single-component stones.

[0041] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0042] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0044] Figure 1 A flowchart is shown for an intelligent analysis method for the composition of urinary tract stones according to an embodiment of the present disclosure;

[0045] Figure 2 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment;

[0046] Figure 3 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. Detailed Implementation

[0047] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0048] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0049] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0050] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0051] It is understood that the various methods and embodiments for intelligent analysis of urinary tract stone components mentioned in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further.

[0052] In addition, this disclosure also provides an intelligent analysis device / system for the composition of urinary tract stones, electronic equipment, computer-readable storage medium, program, and program product. All of the above can be used to implement any of the intelligent analysis methods for the composition of urinary tract stones provided in this disclosure. The corresponding technical solutions and descriptions are described in the corresponding descriptions in the method section and will not be repeated here.

[0053] Figure 1 A flowchart illustrating an intelligent analysis method for the composition of urinary tract stones according to an embodiment of this disclosure is shown. Figure 1 As shown, the intelligent analysis method for urinary tract stone components, applied to stone type identification, includes: Step S101: acquiring the atomic number image of urinary tract stones corresponding to a dual-energy abdominal image; wherein, the dual-energy abdominal image is configured as a dual-energy abdominal CT image; Step S102: extracting multiple component percentages from the peak values ​​corresponding to multiple bar graphs of effective atomic numbers in the atomic number image of urinary tract stones; Step S103: identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and multiple set stone type component percentage intervals. This method addresses the technical problem of difficulty in identifying or distinguishing mixed urinary tract stones of different components, thus failing to meet the clinical diagnostic needs of mixed-component stones being more prevalent than single-component stones.

[0054] Step S101: Obtain the atomic number image of the urinary tract stone 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 this disclosure and other possible embodiments, a spectral CT device (dual-energy CT device, spectral CT device) is used to scan the patient's abdomen to obtain a three-dimensional dual-energy abdominal image (dual-energy abdominal image / dual-energy spectral abdominal image / dual-energy abdominal CT image / dual-energy spectral abdominal CT image / spectral abdominal image / spectral abdominal CT image).

[0056] In the embodiments of this disclosure and other possible embodiments, the spectral CT device (dual-energy CT device, spectral CT device), namely the fast tube voltage switching dual-source CT device, adopts instantaneous kVp switching technology to complete the switching between high and low single energy in an extremely short time (<0.25ms), achieving three-in-one (simultaneous, same direction, same source) dual energy. It overcomes the defects of dual-tube dual-source CT, thereby avoiding the subtle angular differences between the scanning planes of the two tubes, improving the accuracy of data acquisition to provide superior CT scan images.

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

[0058] Step S102: Extract the percentage of multiple components corresponding to the peak values ​​of multiple bar charts corresponding to the effective atomic numbers in the atomic number image of the urinary tract stones.

[0059] In embodiments of this disclosure and other possible embodiments, the method for extracting multiple component percentages of peak values ​​corresponding to multiple bar charts corresponding to effective atomic numbers in the atomic number image of urinary tract stones includes: extracting multiple bar charts corresponding to effective atomic numbers in the atomic number image of urinary tract stones; and determining multiple component percentages of peak values ​​corresponding to multiple bar charts based on the multiple bar charts corresponding to the atomic number image of urinary tract stones.

[0060] In embodiments of this disclosure, the method for determining multiple component percentages of peak values ​​corresponding to multiple bar charts based on multiple bar charts corresponding to the atomic number image of urinary tract stones includes: obtaining a set bar chart color and / or a set bar chart width; extracting multiple bar charts corresponding to valid atomic numbers that satisfy the set bar chart color and / or set bar chart width from the atomic number image of urinary tract stones based on the set bar chart color and / or set bar chart width; and determining multiple component percentages of peak values ​​corresponding to multiple bar charts based on the multiple bar charts that satisfy the set bar chart color and / or set bar chart width from the atomic number image of urinary tract stones.

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

[0062] In the embodiments of this disclosure and other possible embodiments, before extracting multiple bar charts corresponding to the effective atomic numbers in the atomic number image of urinary tract stones, or before extracting multiple bar charts corresponding to the effective atomic numbers in the atomic number image of urinary tract stones that satisfy the set bar chart color and / or set bar chart width, the atomic number image of urinary tract stones is corrected to obtain a corrected atomic number image of urinary tract stones; multiple component percentages of the peak values ​​corresponding to the multiple bar charts corresponding to the effective atomic numbers in the corrected atomic number image of urinary tract stones are extracted, or multiple component percentages of the peak values ​​corresponding to the multiple bar charts corresponding to the effective atomic numbers in the corrected atomic number image of urinary tract stones that satisfy the set bar chart color and / or set bar chart width are extracted.

[0063] In the embodiments of this disclosure and other possible embodiments, the method for correcting the atomic number image of the urinary tract stones to obtain a corrected atomic number image of the urinary tract stones includes: performing at least one correction processing operation on the atomic number image of the urinary tract stones, such as angle adjustment, contrast enhancement, scaling, etc., to obtain a corrected atomic number image of the urinary tract stones, so as to extract multiple bar charts corresponding to the effective atomic numbers in the later stage.

[0064] In the embodiments of this disclosure and other possible embodiments, optical character recognition technology is used to extract multiple component percentages of the peak values ​​corresponding to multiple bar charts of the effective atomic number in the atomic number image of the urinary tract stone; or, optical character recognition technology is used to extract multiple component percentages of the peak values ​​corresponding to multiple bar charts of the effective atomic number in the corrected atomic number image of the urinary tract stone that satisfy the set bar chart color and / or set bar chart width.

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

[0066] In embodiments of this 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 charts and multiple set stone type component percentage intervals includes: obtaining a set component percentage corresponding to a value less than the minimum value of the set stone type component percentage intervals; if the component percentages corresponding to the multiple bar charts are less than the set component percentages, deleting the component percentages less than the set component percentages from the multiple component percentages corresponding to the multiple bar charts; otherwise, retaining the component percentages greater than or equal to the set component percentages; 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 intervals.

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

[0068] In the embodiments disclosed herein 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; atomic number peak recognition and morphological calculation require using Python's NumPy, SciPy or OpenCV libraries to extract multiple component percentages of peaks corresponding to multiple bar charts corresponding to the effective atomic numbers in the atomic number image of urinary tract stones, or to extract multiple component percentages of peaks corresponding to multiple bar charts corresponding to the effective atomic numbers in the atomic number image of urinary tract stones that satisfy the setting of bar chart color and / or setting of bar chart width.

[0069] Step S103: Based on the percentage of multiple components corresponding to the multiple bar charts and the percentage range of multiple set stone type components, identify the stone type corresponding to the dual-energy abdominal image.

[0070] In embodiments of this 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 charts and multiple set stone type component percentage intervals includes: if the component percentage in the multiple component percentages is within the range of a certain set stone type component percentage interval in the multiple set stone type component percentage intervals; configuring the stone type corresponding to the certain set stone type component percentage interval as the stone type corresponding to the component percentage within the range.

[0071] In the embodiments of this disclosure, if at least two of the percentages of components corresponding to the plurality of bar graphs have overlapping intervals corresponding to the percentage intervals of the set stone type components, then the iodine-based graph corresponding to the dual-energy abdominal image is obtained; based on the iodine-based graph corresponding to the dual-energy abdominal image, the stone type corresponding to the overlapping interval is identified.

[0072] In embodiments of this disclosure, the method for identifying the stone type corresponding to the overlapping region based on the iodine-based image corresponding to the dual-energy abdominal image includes: determining the grayscale value of the iodine-based image corresponding to the dual-energy abdominal image; calculating the average grayscale value and uniformity corresponding to the grayscale value; if the average grayscale value is greater than a set average grayscale value and the uniformity is greater than a set uniformity, then the stone type is configured as calcium oxalate dihydrate stone; otherwise, the stone type is configured as struvite stone or carbonate apatite stone.

[0073] In the embodiments of this 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 the embodiments of this 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 calorific value, the minimum value represents the minimum value corresponding to the calorific value, and the average value represents the average calorific value corresponding to the calorific value.

[0075] In the embodiments of this disclosure, the plurality of set stone type component percentage ranges include one or more of the following: 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 stone component percentage range, and struvite stone component percentage range.

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

[0077] In the embodiments and other possible embodiments disclosed herein, 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 stones is greater than 12.5 and all have CT images showing uneven density; the Zeff peak value of struvite stones is less than 12.5 and all have CT images showing uneven density.

[0078] In embodiments of this 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 charts and multiple set stone type component percentage intervals includes: constructing a stone component reference table according to the multiple set stone type component percentage intervals; and performing a table lookup and comparison operation on the multiple component percentages corresponding to the multiple bar charts based on the stone component reference table to identify the stone type corresponding to the dual-energy abdominal image.

[0079] In the embodiments of this disclosure, an intelligent analysis method for the composition of urinary tract stones is also proposed for stone information extraction, including: the intelligent analysis method for the composition of urinary tract stones applied to stone type identification as described above; and, using a preset segmentation model (kidney / bladder / ureter / stone segmentation model), segmenting the stones in the kidney, ureter, and bladder in the dual-energy abdominal image to obtain a stone mask image; and, based on the stone mask image and / or the dual-energy abdominal image, extracting one or more of the following information corresponding to each stone type: stone location, stone shape, stone calorific value, the major and minor diameters of the stone, and stone volume.

[0080] In embodiments of this disclosure, a method for extracting one or more of the following information based on the stone mask image and / or the dual-energy abdominal image: stone location, stone shape, stone calorific value, major and minor diameters of the stone, and stone volume, for each stone type, includes: extracting a three-dimensional stone image with original grayscale values ​​from the three-dimensional dual-energy abdominal image based on the two-dimensional slice stone mask image in the stone mask image of each two-dimensional slice of the dual-energy abdominal image and the stone mask image of the corresponding slice; calculating the average grayscale value corresponding to each stone type in the three-dimensional stone mask image to determine the calorific value corresponding to each stone type.

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

[0082] In embodiments of this disclosure and other possible embodiments, the method for restoring grayscale values ​​of the three-dimensional concretionary grayscale image based on the mask values ​​and spatial positions of the stones in the two-dimensional slice concretionary grayscale image of the three-dimensional concretionary grayscale image and its corresponding slice concretionary grayscale image to obtain a concretionary grayscale image with original grayscale values ​​includes: extracting the mask values ​​and spatial positions of the stones in the two-dimensional slice concretionary grayscale image of the ...

[0083] In the embodiments of this disclosure, the stone area corresponding to each stone type in each two-dimensional slice stone mask image in the three-dimensional stone mask image is calculated respectively; multiple stone areas are obtained; the major and minor axes corresponding to the largest stone area among the multiple stone areas corresponding to each stone type are calculated respectively, and the major and minor axes corresponding to each stone type are obtained; edge detection is performed on the stones corresponding to each stone type in the stone mask image respectively, and three-dimensional stone edge mask lines are obtained; the three-dimensional stone edge mask lines corresponding to each stone type are fitted respectively, and the stone shape corresponding to each stone type is obtained; three-dimensional reconstruction is performed on the mask images corresponding to each stone type in the stone mask image respectively, and the stone volume corresponding to each stone type is obtained.

[0084] In the embodiments of this disclosure, a preset segmentation model (kidney / bladder / ureter / stone segmentation model) is used to segment medical images into kidneys and / or ureters and / or bladders to obtain three-dimensional kidney and / or ureter and / or bladder mask images. Based on the three-dimensional kidney and / or ureter and / or bladder mask images, the kidney and / or ureter and / or bladder masks in the three-dimensional kidney and / or ureter and / or bladder mask images are configured as left kidney and / or ureter and / or bladder masks and right kidney and / or ureter and / or bladder masks. Based on the spatial positional relationship between the stone masks 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 images, the stone locations corresponding to each stone type are determined.

[0085] In embodiments of this disclosure and other possible embodiments, the method for determining the location of stones corresponding to each stone type based on the spatial positional relationship between the stone mask in the stone mask image and the lateral 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 multiple 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 multiple two-dimensional slice stone mask images is within the three-dimensional kidney and / or ureter and / or bladder mask image, the method further includes: extracting multiple two-dimensional slice stone mask images corresponding to each stone type from the three-dimensional kidney and / or ureter and / or bladder ... If the spatial location of the stone mask in the two-dimensional slice stone mask image corresponding to the slice of the three-dimensional slice stone mask image corresponding to the slice of the kidney and / or ureter and / or bladder mask image is within the spatial location of the left kidney and / or ureter and / or bladder mask, then the stone corresponding to the spatial location of the stone mask is configured as a left kidney and / or ureter and / or bladder stone; if the spatial location of the stone mask corresponding to the multiple two-dimensional slice stone mask images is within the spatial location of the right kidney and / or ureter and / or bladder mask in the two-dimensional slice stone mask image corresponding to the slice of the three-dimensional kidney and / or ureter and / or bladder mask image, then the stone corresponding to the spatial location of the stone mask is configured as a right kidney and / or ureter and / or bladder stone.

[0086] In embodiments of this disclosure and other possible embodiments, the method for segmenting the kidney, ureter, and bladder stones in the dual-energy abdominal image to obtain a stone mask image includes: acquiring a preset segmentation network, a dual-energy abdominal training image corresponding to the preset segmentation network, and a multi-mask label fusion image representing the kidney, ureter, and stones corresponding to the dual-energy abdominal training image; training the preset segmentation network on the dual-energy abdominal training image and its corresponding multi-mask label fusion image to obtain a corresponding stone segmentation model (preset segmentation model, kidney / bladder / ureter / stone segmentation model); and segmenting the kidney, ureter, and bladder stones in the dual-energy abdominal image based on the stone segmentation model to obtain a stone mask image.

[0087] In embodiments of this disclosure and other possible embodiments, the method for constructing the multi-mask label image fusion image representing the kidney, bladder, ureter, and stones respectively includes: acquiring the kidney mask label image, bladder mask label image, ureter mask label image, and stone mask image corresponding to the dual-energy abdominal training image; 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 respectively; 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 stones respectively corresponding to the dual-energy abdominal training image.

[0088] In embodiments of this 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 fused image corresponding to the dual-energy abdominal training image, representing the kidney, bladder, ureter, and stone respectively, includes: determining the first spatial position corresponding to the first mask value in the kidney mask label image, the second spatial position corresponding to the second mask value in the bladder mask label image, and the third spatial position corresponding to the stone in the ureter mask label image, respectively. The third spatial position corresponding to the third mask value and the 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, then the mask value corresponding to the overlapping spatial position is updated to the fourth mask value corresponding to the fourth spatial position; otherwise, 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 are retained to obtain the multi-mask label fusion image corresponding to the dual-energy abdominal training image, which respectively represents the kidney, bladder, ureter, and stone.

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

[0090] In embodiments of this disclosure and other possible embodiments, the method for determining a stone mask label image based on an iodine-based image corresponding to the dual-energy abdominal training image includes: delineating stones in the iodine-based image corresponding to the dual-energy abdominal training image to determine a stone mask label image.

[0091] In embodiments of this disclosure and other possible embodiments, the method for determining organ mask label images based on water-based images corresponding to dual-energy abdominal training images includes: delineating organs in water-based images corresponding to dual-energy abdominal training images to determine organ mask label images.

[0092] In embodiments of this disclosure and other possible embodiments, the process of training the preset segmentation network using the dual-energy abdominal training image and its corresponding multi-mask label fusion image includes: obtaining the organ loss function and its corresponding organ loss weight value for the organ, and the stone loss function and its corresponding stone loss weight value that is less than the organ loss weight value for the stone; during the training of the preset segmentation network, if the organ loss value corresponding to the organ loss function is less than a first preset loss value and / or the total loss value corresponding to the organ loss function and the stone loss function is less than a second preset loss value, then during the training of the preset segmentation network... During each training session of the network, the stone loss weight value is increased and adjusted according to a set ratio to obtain a stone loss adjustment weight value. Based on the stone loss adjustment weight value, the organ loss weight value corresponding to the organ loss weight value is determined. Based on the stone loss adjustment weight value and the organ loss adjustment weight value, 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 are calculated. If the stone loss value is less than a third set loss value and / or the total loss value corresponding to the organ loss function and the stone loss function is less than a fourth set loss value, then the training of the preset segmentation network is stopped.

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

[0094] In the embodiments of this 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] In the embodiments of this disclosure and other possible embodiments, the organ loss function and the stone loss function are respectively configured as one or more of the following: cross-entropy loss function, Dice loss function, Focal loss function, Tversky loss function, and IoU loss function.

[0096] In the embodiments of this disclosure and other possible embodiments, the preset segmentation network is configured as one or more of the following segmentation networks: Unet, ResUnet, Unet++, ResUnet++, nnUnet, SegNet, PSPNet, DeepLab, RefineNet, Medformer, or improved segmentation networks thereof.

[0097] In the embodiments of this disclosure and other possible embodiments, the atomic number peak value of energy-dispersive CT is used as a method for evaluating the composition of urinary tract stones. Since the atomic number peak values ​​of different components have certain specificities, accurate assessment of common urinary tract stone components can be made. This technique is applicable not only to single-component urinary tract stones (manifested as one atomic number peak) but also to multi-component urinary tract stones (manifested as multiple atomic number peaks), meeting the clinical diagnostic needs for mixed-component stones rather than single-component stones.

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

[0099] In some embodiments, the functions or modules of the apparatus or system provided in this disclosure can be used to perform the methods described in the above embodiments of the intelligent analysis method for urinary tract stone components. The specific implementation can be referred to the description of the above embodiments of the intelligent analysis method for urinary tract stone components, which will not be repeated here for the sake of brevity.

[0100] According to one aspect of the embodiments of this disclosure, an intelligent analysis device / system for urinary tract stone components is provided for stone type identification, comprising: an acquisition unit for acquiring an atomic number image of urinary tract stones corresponding to a dual-energy abdominal image; an extraction unit for extracting multiple component percentages of multiple bar charts corresponding to the effective atomic numbers in the atomic number image of urinary tract stones; and an identification unit for identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar charts and multiple preset stone type component percentage ranges. This addresses the technical problem of difficulty in identifying or distinguishing mixed urinary tract stones with different components, thus failing to meet the clinical diagnostic needs for mixed-component stones rather than single-component stones.

[0101] According to one aspect of the present disclosure, an intelligent analysis device / system for urinary tract stone components is provided, applied to stone information extraction, comprising: an acquisition unit for acquiring an atomic number image of urinary tract stones corresponding to a dual-energy abdominal image; a component percentage extraction unit for extracting multiple component percentages of multiple bar charts corresponding to the effective atomic numbers in the atomic number image of urinary tract stones; an identification unit for identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar charts and multiple set stone type component percentage intervals; a segmentation unit for segmenting the dual-energy abdominal image for stones in the kidneys, ureters, and bladder to obtain a stone mask image; and a stone information extraction unit for extracting one or more of the following information based on the stone mask image and / or the dual-energy abdominal image: stone location, stone shape, stone calorific value, major and minor diameters of the stone, and stone volume corresponding to each stone type. This addresses the technical problem of the difficulty in differentiating or identifying mixed urinary tract stones of different components, which makes it impossible to meet the clinical diagnostic needs when there are more mixed-component stones than single-component stones.

[0102] According to one aspect of the embodiments of this disclosure, an intelligent analysis device / system for urinary tract stone components is provided, 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 invoke the instructions stored in the memory to execute the aforementioned intelligent analysis method for urinary tract stone components. This addresses the technical problem of difficulty in identifying or distinguishing mixed urinary tract stones of different components, thus failing to meet the clinical diagnostic needs when mixed-component stones outnumber single-component stones.

[0103] According to one aspect of the embodiments of this disclosure, a smart analysis device / system for the composition of urinary tract stones is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the aforementioned smart analysis method for the composition of urinary tract stones. This addresses the technical problem of difficulty in identifying or distinguishing mixed urinary tract stones of different compositions, thus failing to meet the clinical diagnostic needs when mixed-component stones outnumber single-component stones.

[0104] According to one aspect of the embodiments of this disclosure, an intelligent analysis device / system for the composition of urinary tract stones is provided, comprising: a computer-readable storage medium storing computer program instructions thereon, wherein the computer program instructions, when executed by a processor, implement the aforementioned intelligent analysis method for the composition of urinary tract stones. This addresses the technical problem of difficulty in identifying or distinguishing mixed urinary tract stones of different compositions, thus failing to meet the clinical diagnostic needs when mixed-component stones outnumber single-component stones.

[0105] According to one aspect of the embodiments of this disclosure, an intelligent analysis device / system for the composition of urinary tract stones is provided, comprising: a computer program product including a computer program / instructions, which, when executed by a processor, implements the aforementioned intelligent analysis method for the composition of urinary tract stones. This addresses the technical problem of difficulty in identifying or distinguishing mixed urinary tract stones of different compositions, thus failing to meet the clinical diagnostic needs when mixed-component stones outnumber single-component stones.

[0106] Those skilled in the art will understand that, in the above-described intelligent analysis method for urinary tract stone components in specific embodiments, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0107] This disclosure also proposes a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the aforementioned intelligent analysis method for the composition of urinary tract stones. The computer-readable storage medium can be a non-volatile computer-readable storage medium. This addresses the technical problem of difficulty in identifying or distinguishing mixed urinary tract stones of different components, thus failing to meet the clinical diagnostic needs when mixed-component stones outnumber single-component stones.

[0108] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured for the aforementioned intelligent analysis method for the components of urinary tract stones. The electronic device can be provided as a terminal, server, or other form of device. This addresses the technical problem of difficulty in identifying or distinguishing mixed urinary tract stones of different components, thus failing to meet the clinical diagnostic needs when mixed-component stones outnumber single-component stones.

[0109] Figure 2 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, or other terminal.

[0110] Reference Figure 2 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply 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] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0112] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can 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 storage, flash memory, magnetic disk, or optical disk.

[0113] Power supply component 806 provides power to various components of electronic device 800. 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 to electronic device 800.

[0114] 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 may be implemented as a touchscreen 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 may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

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

[0116] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

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

[0118] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may 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 may 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 to perform the methods described above.

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

[0121] Figure 3 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. For example, the electronic device 1900 may be provided as a server. (Refer to...) Figure 3The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0122] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

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

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

[0125] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0126] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic 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 them to the computer-readable storage media in the respective computing / processing device.

[0127] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status 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 execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via 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., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0128] Various aspects of this 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 this 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, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0130] 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, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0131] The flowcharts and block diagrams in the accompanying drawings 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 a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0132] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for intelligent analysis of the composition of urinary tract stones, applied to stone type identification, characterized in that, include: Obtain atomic number images of urinary tract stones corresponding to dual-energy abdominal imaging; Extract the percentage of multiple components corresponding to the peak values ​​of multiple bar charts corresponding to the effective atomic numbers in the atomic number image of the urinary tract stones; Based on the multiple component percentages corresponding to the multiple bar charts and the multiple set stone type component percentage intervals, the stone type corresponding to the dual-energy abdominal image is identified, including: if at least two component percentage intervals corresponding to the set stone type component percentages in the multiple bar charts have overlapping intervals, then the iodine-based image corresponding to the dual-energy abdominal image is obtained; the calorific value of the iodine-based image corresponding to the dual-energy abdominal image is determined; the average gray value and uniformity corresponding to the calorific value are calculated respectively; if the average gray value is greater than the set average gray value and the uniformity is greater than the set uniformity, then the stone type is configured as calcium oxalate dihydrate stone; otherwise, the stone type is configured as struvite stone or carbonate apatite stone.

2. The intelligent analysis method for the composition of urinary tract stones according to claim 1, applied to stone type identification, is characterized in that, The extraction of multiple component percentages from the peak values ​​corresponding to the effective atomic numbers in the atomic number image of the urinary tract stones includes: Get the set bar chart color and / or set the bar chart width; Based on the set bar chart color and / or set bar chart width, extract multiple bar charts corresponding to the effective atomic numbers that satisfy the set bar chart color and / or set bar chart width from the atomic number image of urinary tract stones; Based on the atomic number image of the urinary tract stones, multiple bar charts that satisfy the set bar chart color and / or set bar chart width are used to determine the percentage of multiple components corresponding to the peak values ​​of the multiple bar charts.

3. The intelligent analysis method for the composition of urinary tract stones according to any one of claims 1 or 2, applied to stone type identification, is characterized in that, The process of identifying the stone type corresponding to the dual-energy abdominal image based on the percentage of multiple components corresponding to the multiple bar charts and multiple set stone type component percentage ranges includes: Obtain the percentage of a given component that is less than the minimum value in the specified range of percentages for different stone types; If the percentage of a component corresponding to multiple bar charts is less than the set percentage of a component, then delete the percentage of a component that is less than the set percentage of a component from the multiple percentage of a component corresponding to the multiple bar charts. Otherwise, retain the percentage of the component that is greater than or equal to the set percentage of the component; Based on the retained percentages of multiple components and multiple set percentage ranges of component components for different stone types, the stone type corresponding to the dual-energy abdominal image is identified.

4. The intelligent analysis method for the composition of urinary tract stones according to claim 3, applied to stone type identification, is characterized in that, The method of identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar charts and multiple set stone type component percentage ranges further includes: If the percentage of a component among the plurality of component percentages falls within the range of a certain set percentage range of a certain stone type component percentage; Configure the stone type corresponding to a certain set stone type component percentage range as the stone type corresponding to the component percentage within the range.

5. The intelligent analysis method for the composition of urinary tract stones according to any one of claims 1, 2, or 4, applied to stone type identification, characterized in that, The multiple set percentage ranges for stone types include one or more of the following: percentage ranges for uric acid stones, percentage ranges for calcium oxalate monohydrate stones, percentage ranges for calcium oxalate dihydrate stones, percentage ranges for carbonate apatite stones, percentage ranges for calcium carbonate stones, and percentage ranges for struvite stones.

6. The intelligent analysis method for the composition of urinary tract stones according to claim 3, applied to stone type identification, is characterized in that, The multiple set percentage ranges for stone types include one or more of the following: percentage ranges for uric acid stones, percentage ranges for calcium oxalate monohydrate stones, percentage ranges for calcium oxalate dihydrate stones, percentage ranges for carbonate apatite stones, percentage ranges for calcium carbonate stones, and percentage ranges for struvite stones.

7. The intelligent analysis method for the composition of urinary tract stones according to claim 5, applied to stone type identification, is characterized in that, The percentage range of uric acid stone components is configured as 6.5-10.5, the percentage range of calcium oxalate monohydrate stone components is configured as 13.3-14.0, the percentage range of calcium oxalate dihydrate stone components is configured as 12.0-13.3, the percentage range of carbonate apatite stone components is configured as 14.0-15.0, the percentage range of calcium carbonate phosphate stone components is configured as greater than 12.5, and the percentage range of struvite stone components is configured as less than 12.

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8. The intelligent analysis method for the composition of urinary tract stones according to claim 6, applied to stone type identification, is characterized in that, The percentage range of uric acid stone components is configured as 6.5-10.5, the percentage range of calcium oxalate monohydrate stone components is configured as 13.3-14.0, the percentage range of calcium oxalate dihydrate stone components is configured as 12.0-13.3, the percentage range of carbonate apatite stone components is configured as 14.0-15.0, the percentage range of calcium carbonate phosphate stone components is configured as greater than 12.5, and the percentage range of struvite stone components is configured as less than 12.

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9. A method for intelligent analysis of the components of urinary tract stones, applied to stone information extraction, characterized in that, include: A method for intelligent analysis of urinary tract stone composition applied to stone type identification as described in any one of claims 1-8; and, The stones in the kidneys, ureters, and bladder are segmented 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, extract one or more of the following information corresponding to each stone type: stone location, stone shape, stone calorific value, major and minor diameters of the stone, and stone volume.

10. The intelligent analysis method for the composition of urinary tract stones according to claim 9, applied to stone information extraction, characterized in that, Based on the stone mask image and / or the dual-energy abdominal image, one or more pieces of information are extracted from each stone type, including stone location, stone shape, stone calorific value, major and minor diameters of the stone, and stone volume, including: Based on the two-dimensional sliced ​​dual-energy abdominal image and the corresponding two-dimensional sliced ​​stone mask image in the stone mask image of each slice in the three-dimensional dual-energy abdominal image, a three-dimensional stone image with original grayscale values ​​is extracted from the three-dimensional dual-energy abdominal image; the average grayscale value corresponding to each stone type in the three-dimensional stone image is calculated to determine the grayscale value of the stone corresponding to each stone type; and / or, Calculate the area of ​​each stone type in each two-dimensional slice of the stone mask image in the three-dimensional stone mask image; obtain multiple stone areas; calculate the major and minor axes corresponding to the largest stone area among the multiple stone areas corresponding to each stone type; perform edge detection on the stones corresponding to each stone type in the stone mask image to obtain three-dimensional stone edge mask lines; fit the three-dimensional stone edge mask lines corresponding to each stone type 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 to obtain the stone volume corresponding to each stone type; and / or, The dual-energy abdominal image is segmented into kidney and / or ureter and / or bladder to obtain a three-dimensional kidney and / or ureter and / or bladder mask image. Based on the three-dimensional kidney and / or ureter and / or bladder mask image, the kidney and / or ureter and / or bladder mask in the three-dimensional kidney and / or ureter and / or bladder mask image is 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 positional 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, the stone location corresponding to each stone type is determined.

11. An intelligent analysis system for the composition of urinary tract stones, applied to stone type identification, characterized in that, include: The acquisition unit is used to acquire the atomic number image of urinary tract stones corresponding to the dual-energy abdominal image; The extraction unit is used to extract the percentage of multiple components of the peak values ​​corresponding to the effective atomic numbers in the atomic number image of the urinary tract stones; The identification unit is used to identify the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar charts and multiple set stone type component percentage intervals, including: if at least two component percentage intervals corresponding to the set stone type component percentages in the multiple bar charts have overlapping intervals, then obtain the iodine-based image corresponding to the dual-energy abdominal image; determine the calorific value of the iodine-based image corresponding to the dual-energy abdominal image; calculate the average gray value and uniformity corresponding to the calorific value respectively; if the average gray value is greater than the set average gray value and the uniformity is greater than the set uniformity, then configure the stone type as calcium oxalate dihydrate stone; otherwise, configure the stone type as struvite stone or carbonate apatite stone.

12. An intelligent analysis system for the composition of urinary tract stones, applied to stone information extraction, characterized in that, include: The acquisition unit is used to acquire the atomic number image of urinary tract stones corresponding to the dual-energy abdominal image; The component percentage extraction unit is used to extract the component percentages of multiple peaks corresponding to multiple bar charts in the atomic number image of the urinary tract stones; The identification unit is used to identify the stone type corresponding to the dual-energy abdominal image based on the percentage of multiple components corresponding to the multiple bar charts and the percentage range of multiple set stone type components. This includes: if at least two of the percentage of components corresponding to the multiple bar charts have overlapping percentage ranges corresponding to the set stone type components, then obtaining the iodine-based image corresponding to the dual-energy abdominal image; determining the calorific value of the iodine-based image corresponding to the dual-energy abdominal image; calculating the average gray value and uniformity corresponding to the calorific value; if the average gray value is greater than a set average gray value and the uniformity is greater than a set uniformity, then configuring the stone type as calcium oxalate dihydrate stone; otherwise, configuring the stone type as struvite stone or carbonate apatite stone. The segmentation unit is used to segment the stones in the kidneys, ureters, and bladder of the dual-energy abdominal image to obtain a stone mask image; The stone information extraction unit is used to extract one or more of the following information based on the stone mask image and / or the dual-energy abdominal image: stone location, stone shape, stone calorific value, major and minor diameters of the stone, and stone volume for each stone type.

13. An intelligent analysis system for the composition of urinary tract stones, characterized in that, include: 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 invoke the instructions stored in the memory to execute the intelligent analysis method for the composition of urinary tract stones according to any one of claims 1 to 10; or, Includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the intelligent analysis method for the composition of urinary tract stones according to any one of claims 1 to 10; or, Includes: a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, implement the intelligent analysis method for the composition of urinary tract stones according to any one of claims 1 to 10; or, Includes: a computer program product, including computer program instructions that, when executed by a processor, implement the intelligent analysis method for the composition of urinary tract stones as described in any one of claims 1 to 10.

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