Abdominal medical image processing method and system

Abstract

The invention relates to an abdominal medical image processing method and system, and relates to the technical field of abdominal image processing. The abdominal medical image processing method comprises the following steps: acquiring a preset segmentation network, a dual-energy abdominal training image used for training the preset segmentation network, and a multi-mask label fusion image which corresponds to the dual-energy abdominal training image and represents the kidney, the ureter, the bladder and the calculus; training the preset segmentation network by using the dual-energy abdomen training image and the corresponding multi-mask label fusion image to obtain a corresponding calculus segmentation model; and based on the stone segmentation model, segmenting stones in the kidney, the ureter and the bladder of the dual-energy abdomen image to obtain a stone mask image. According to the embodiment of the invention, the abdominal medical image can be processed.

Description

Technical Field

[0001] The present disclosure relates to the technical field of abdominal image processing, and in particular to a method and system for processing abdominal medical images. Background Art

[0002] Urinary stones are a common urinary tract disease. They can occur anywhere in the kidneys, bladder, ureters, and urethra, but are most common in the kidneys, bladder, and ureters. Clinical manifestations vary depending on the location of the stone. Early diagnosis and accurate measurement are crucial for developing effective treatment plans.

[0003] Urinary stones can cause symptoms such as pain, dysuria, infection, and hematuria. Left untreated, they can lead to renal impairment and even renal failure. They can also increase the incidence of many chronic diseases, including osteoporosis and cardiovascular disease. Furthermore, urinary stones often recur, with approximately 40% of patients experiencing one or more recurrent episodes.

[0004] Currently, the diagnosis of the composition of postoperative urinary stones mainly relies on infrared spectroscopy analysis of stone samples. This is of certain significance for preventing the recurrence of urinary stones, but it does not help to select the appropriate stone treatment method before surgery. The diagnosis of urinary stones relies on medical imaging technologies such as X-rays and CT scans. However, because the spatial positions corresponding to the kidneys, ureters, and bladder include the spatial positions corresponding to urinary stones, there is a lack of a multi-mask label fusion image that can represent the kidneys, ureters, bladder, and stones. As a result, the stone segmentation does not fully utilize the anatomical structural information of the kidneys, ureters, bladder, and stones in their spatial positions, and thus lacks the synergistic effect of the anatomical structural information of the kidneys, ureters, bladder, and stones.

[0005] Therefore, it is necessary to propose a technical solution for abdominal image processing based on multi-mask label fusion images representing the kidneys, ureters, bladder and stones, so as to solve the current technical problem of insufficient utilization of the anatomical structure information of the spatial positions of the kidneys, ureters, bladder and stones, and the lack of synergistic effect of the anatomical structure information of the kidneys, ureters, bladder and stones. Summary of the Invention

[0006] The present disclosure proposes a method for processing abdominal medical images and a corresponding technical solution of the system.

[0007] According to one aspect of the present disclosure, a method for processing an abdominal medical image is provided, comprising:

[0008] Obtaining a preset segmentation network, a dual-energy abdominal training image corresponding to the preset segmentation network for training, and a multi-mask label fusion image representing the kidney, ureter, bladder, and stone corresponding to the dual-energy abdominal training image;

[0009] Using the dual-energy abdominal training image and the corresponding multi-mask label fusion image, the preset segmentation network is trained to obtain a corresponding stone segmentation model;

[0010] Based on the stone segmentation model, the stones in the kidney, ureter and bladder are segmented in the dual-energy abdominal image to obtain a stone mask image.

[0011] Preferably, the method for constructing the multi-mask label fusion image representing the kidney, bladder, ureter, ureter and stone respectively includes: obtaining an organ mask label image corresponding to at least one organ of the kidney, ureter and bladder in the dual-energy abdominal training image and a stone mask label image in the organ; generating a multi-mask label fusion image corresponding to the dual-energy abdominal training image based on the organ mask label image and the stone mask label image.

[0012] Preferably, the method for generating a multi-mask label fusion image corresponding to the dual-energy abdominal training image based on the organ mask label image and the stone mask label image includes: generating a multi-mask label fusion image corresponding to the dual-energy abdominal training image based on the organ mask value corresponding to the organ mask label image and the organ spatial position corresponding to the organ mask value, the stone mask value corresponding to the stone mask label image and the second spatial position corresponding to the stone mask value.

[0013] Preferably, before obtaining the organ mask label image corresponding to at least one organ of the kidney, ureter, and bladder in the dual-energy abdominal training image and the stone mask label image in the organ, the stone mask label image is determined based on the iodine-based image / calcium image corresponding to the dual-energy abdominal training image, and the organ mask label image is determined based on the water-based image / soft tissue image corresponding to the dual-energy abdominal training image.

[0014] Preferably, the method of using the dual-energy abdominal training image and the corresponding multi-mask label fusion image to train the preset segmentation network to obtain the corresponding stone segmentation model includes: obtaining the set loss value corresponding to the stone segmentation model; in the process of training the preset segmentation network using the dual-energy abdominal training image and the corresponding multi-mask label fusion image, if the loss value corresponding to the stone segmentation model is less than or equal to the set loss value, stopping training the preset segmentation network to obtain the corresponding stone segmentation model.

[0015] Preferably, the method for processing abdominal medical images further includes: extracting corresponding stone information based on the dual-energy abdominal image and / or stone mask image; wherein the stone information is configured as one or more of the following information: stone position, stone shape, stone calcification value, the corresponding long and short diameters of the stone, stone area or stone volume.

[0016] Preferably, the stone position corresponding to the stone information is determined based on the kidney and / or ureter and / or bladder mask images corresponding to the dual-energy abdominal image and the stone mask image.

[0017] Preferably, the limescale value corresponding to the stone information is determined based on the dual-energy abdominal image and the corresponding stone mask image.

[0018] Preferably, based on the stone mask image, one or more of the stone shape corresponding to the stone information, the major diameter and minor diameter corresponding to the stone, and the stone volume are determined.

[0019] Preferably, the method for processing abdominal medical images further includes: if the dual-energy abdominal image is configured as a three-dimensional dual-energy abdominal CT image, then based on the urinary stone atomic number image corresponding to the dual-energy abdominal image, identifying the stone type corresponding to the stone information.

[0020] According to one aspect of the present disclosure, there is provided an apparatus / system for processing abdominal medical images, comprising:

[0021] an acquisition unit, configured to acquire a preset segmentation network, a dual-energy abdominal training image corresponding to the preset segmentation network for training, and a multi-mask label fusion image corresponding to the dual-energy abdominal training image representing the kidney, ureter, bladder, and stone;

[0022] a training unit, configured to train the preset segmentation network using the dual-energy abdominal training image and the corresponding multi-mask label fusion image to obtain a corresponding stone segmentation model;

[0023] The processing unit is used to segment the stones in the kidney, ureter and bladder of the dual-energy abdominal image based on the stone segmentation model to obtain a stone mask image.

[0024] According to one aspect of the present disclosure, a device / system for processing abdominal medical images is provided, comprising: an electronic device configured with a processor and a memory for storing processor-executable instructions; wherein the processor is configured to call instructions stored in the memory to execute the above-mentioned method for processing abdominal medical images.

[0025] According to one aspect of the present disclosure, a device / system for processing abdominal medical images is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to call the instructions stored in the memory to execute the above-mentioned method for processing abdominal medical images.

[0026] According to one aspect of the present disclosure, a device / system for processing abdominal medical images is provided, comprising: a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions implement the above-mentioned method for processing abdominal medical images when executed by a processor.

[0027] According to one aspect of the present disclosure, a device / system for processing abdominal medical images is provided, comprising: a computer program product including a computer program / instruction, which implements the above-mentioned method for processing abdominal medical images when executed by a processor.

[0028] In the disclosed embodiments, a method and system for processing abdominal medical images are proposed to address the current technical problem of insufficient utilization of the anatomical structural information of the spatial locations of the kidneys, ureters, bladder, and stones, and the resulting lack of synergistic effects of the anatomical structural information of the kidneys, ureters, bladder, and stones.

[0029] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

[0030] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used to explain the technical solutions of the present disclosure.

[0032] Figure 1 A flowchart illustrating a method for processing an abdominal medical image according to an embodiment of the present disclosure;

[0033] Figure 2 is a block diagram of an electronic device 800 according to an exemplary embodiment;

[0034] Figure 3 is a block diagram of an electronic device 1900 according to an exemplary embodiment. DETAILED DESCRIPTION

[0035] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0036] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0037] The term "and / or" herein simply describes an association relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can represent the existence of three situations: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0038] In addition, numerous specific details are provided in the following detailed description to better illustrate the present disclosure. Those skilled in the art will appreciate that the present disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main points of the present disclosure.

[0039] It can be understood that the various method embodiments for processing abdominal medical images mentioned in the present disclosure can be combined with each other to form combined embodiments without violating the principle logic. Due to space limitations, the present disclosure will not elaborate on them.

[0040] In addition, the present disclosure also provides an abdominal medical image processing device / system, electronic device, computer-readable storage medium, and program, all of which can be used to implement any abdominal medical image processing method provided by the present disclosure. The corresponding technical solutions and descriptions are referred to the corresponding records in the method section and will not be repeated here.

[0041] Figure 1 FIG. 1 is a flow chart showing a method for processing abdominal medical images according to an embodiment of the present disclosure. Figure 1As shown, the abdominal medical image processing method includes: step S101: obtaining a preset segmentation network, a dual-energy abdominal training image corresponding to the preset segmentation network, and a multi-mask label fusion image corresponding to the dual-energy abdominal training image, representing the kidneys, ureters, bladder, and stones; step S102: using the dual-energy abdominal training image and its corresponding multi-mask label fusion image to train the preset segmentation network to obtain a corresponding stone segmentation model; step S103: based on the stone segmentation model, segmenting the stones in the kidneys, ureters, and bladder in the dual-energy abdominal image to obtain a stone mask image. This method solves the current technical problem of insufficient utilization of the anatomical structural information of the kidneys, ureters, bladder, and stones in their spatial locations, and thus lacking the synergistic effect of the anatomical structural information of the kidneys, ureters, bladder, and stones.

[0042] Step S101: obtaining a preset segmentation network, a dual-energy abdominal training image corresponding to the preset segmentation network for training, and a multi-mask label fusion image representing the kidney, ureter, bladder and stone corresponding to the dual-energy abdominal training image.

[0043] In the embodiments of the present 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).

[0044] In the embodiment of the present disclosure and other possible embodiments, a dual-energy digital X-ray machine is used to photograph the patient's abdomen to obtain a two-dimensional dual-energy abdominal image (dual-energy abdominal image / dual-energy abdominal X-ray image).

[0045] In the embodiments of the present disclosure and other possible embodiments, the dual-energy abdominal training image can be configured as 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) or a two-dimensional dual-energy abdominal image (dual-energy abdominal image / dual-energy abdominal X-ray image).

[0046] In the embodiments of the present disclosure and other possible embodiments, the preset segmentation network is configured as one or more segmentation networks such as Unet, ResUnet, Unet++, ResUnet++, nnUnet, SegNet, PSPNet, DeepLab, RefineNet, Medformer or improved segmentation networks thereof.

[0047] In an embodiment of the present disclosure, a method for constructing the multi-mask label fusion image representing the kidney, bladder, ureter, ureter and stone respectively includes: obtaining an organ mask label image corresponding to at least one organ of the kidney, ureter, and bladder in the dual-energy abdominal training image and a stone mask label image in the organ; and generating a multi-mask label fusion image corresponding to the dual-energy abdominal training image based on the organ mask label image and the stone mask label image.

[0048] In an embodiment of the present disclosure, the method for generating a multi-mask label fusion image corresponding to the dual-energy abdominal training image based on the organ mask label image and the stone mask label image includes: generating a multi-mask label fusion image corresponding to the dual-energy abdominal training image based on the organ mask value corresponding to the organ mask label image and the organ spatial position corresponding to the organ mask value, the stone mask value corresponding to the stone mask label image and the second spatial position corresponding to the stone mask value; and / or,

[0049] In an embodiment of the present disclosure, before obtaining the organ mask label image corresponding to at least one organ of the kidney, ureter, and bladder in the dual-energy abdominal training image and the stone mask label image in the organ, the stone mask label image is determined based on the iodine-based image / calcium image corresponding to the dual-energy abdominal training image, and the organ mask label image is determined based on the water-based image / soft tissue image corresponding to the dual-energy abdominal training image.

[0050] In the embodiments of the present disclosure and other possible embodiments, the method for determining a stone mask label image based on the iodine-based image / calcium image corresponding to the dual-energy abdominal training image includes: outlining the stone on the iodine-based image / calcium image corresponding to the dual-energy abdominal training image, and determining the stone mask label image.

[0051] In the embodiments of the present disclosure and other possible embodiments, the method for determining the organ mask label image based on the water-based image / soft tissue image corresponding to the dual-energy abdominal training image includes: outlining the organs in the water-based image / soft tissue image corresponding to the dual-energy abdominal training image to determine the organ mask label image.

[0052] In the embodiments of the present disclosure and other possible embodiments, the method for generating a multi-mask label fusion image corresponding to the dual-energy abdominal training image based on the organ mask value corresponding to the organ mask label image and the organ spatial position corresponding to the organ mask value, the stone mask value corresponding to the stone mask label image and the stone spatial position corresponding to the stone mask value, includes: respectively determining the organ spatial position corresponding to the organ mask value in the organ mask label image and the stone spatial position corresponding to the stone mask value in the stone mask image; if the organ spatial position overlaps with the stone spatial position, updating the mask value corresponding to the overlapping spatial position to the stone mask value corresponding to the stone spatial position; otherwise, retaining the organ mask value corresponding to the organ spatial position, and generating a multi-mask label fusion image corresponding to the dual-energy abdominal training image.

[0053] In the embodiments of the present disclosure and other possible embodiments, before respectively determining the organ spatial position corresponding to the organ mask value in the organ mask label image and the stone spatial position corresponding to the stone mask value in the stone mask image, it also includes: if the number corresponding to the stone mask image is greater than 1, then respectively obtaining the stone spatial positions corresponding to multiple stone mask images; judging whether there is spatial position overlap between the stone spatial positions corresponding to multiple stone mask images; if there is spatial position overlap, then respectively generating multi-mask label fusion images of each stone corresponding to the spatial position overlap and its corresponding organ, and generating multi-mask label fusion images of stones corresponding to non-overlapping spatial positions and their corresponding organs; otherwise, generating multi-mask label fusion images corresponding to all stones and their corresponding organs together.

[0054] Step S102: using the dual-energy abdominal training image and the corresponding multi-mask label fusion image, the preset segmentation network is trained to obtain a corresponding stone segmentation model.

[0055] In an embodiment of the present disclosure, the method of using the dual-energy abdominal training image and the corresponding multi-mask label fusion image to train the preset segmentation network to obtain the corresponding stone segmentation model includes: obtaining a set loss value corresponding to the stone segmentation model; in the process of training the preset segmentation network using the dual-energy abdominal training image and the corresponding multi-mask label fusion image, if the loss value corresponding to the stone segmentation model is less than or equal to the set loss value, stopping training the preset segmentation network to obtain the corresponding stone segmentation model.

[0056] In the embodiments of the present disclosure and other possible embodiments, the process of training the preset segmentation network using the dual-energy abdominal training image and the corresponding multi-mask label fusion image includes: obtaining the organ loss function corresponding to the organ and the corresponding organ loss weight value, the stone loss function corresponding to the stone and the corresponding stone loss weight value that is less than the organ loss weight value; in the process of training the preset segmentation network, if the organ loss value corresponding to the organ loss function is less than the first set loss value and / or the total loss value corresponding to the organ loss function and the stone loss function is less than the second set loss value, then in the process of training the preset segmentation network, During each training process of the continued training of the network, the stone loss weight value is increased and regulated according to the set ratio to obtain the stone loss regulation weight value; based on the stone loss regulation weight value, the organ loss regulation weight value corresponding to the organ loss weight value is determined; based on the stone loss regulation weight value and the organ loss regulation weight value, the stone loss value corresponding to the stone loss function and the organ loss function and the total loss value corresponding to the stone loss function are calculated; if the stone loss value is less than the third set loss value and / or the total loss value corresponding to the organ loss function and the stone loss function is less than the fourth set loss value, the training of the preset segmentation network is stopped.

[0057] In the embodiments of the present 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; wherein 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.

[0058] In the embodiments of the present disclosure and other possible embodiments, the organ loss function and the stone loss function are respectively configured as one or more of the cross entropy loss function, Dice loss function, Focal loss function, Tversky loss function, Tversky loss function, and IoU loss function.

[0059] Step S103: Based on the stone segmentation model, the dual-energy abdominal image is segmented to identify stones in the kidney, ureter and bladder to obtain a stone mask image.

[0060] In an embodiment of the present disclosure, the method for processing abdominal medical images further includes: extracting corresponding stone information based on the dual-energy abdominal image and / or the stone mask image; wherein the stone information is configured as one or more of the following information: stone position, stone shape, stone calcification value, the corresponding long and short diameters of the stone, stone area or stone volume.

[0061] In an embodiment of the present disclosure, the stone position corresponding to the stone information is determined based on the kidney and / or ureter and / or bladder mask images corresponding to the dual-energy abdominal image and the stone mask image.

[0062] In the embodiments of the present disclosure and other possible embodiments, the method for determining the stone position corresponding to the stone information based on the kidney and / or ureter and / or bladder mask image and the stone mask image corresponding to the dual-energy abdominal image includes: performing kidney and / or ureter and / or bladder segmentation on the dual-energy abdominal image to obtain a kidney and / or ureter and / or bladder mask image; configuring the kidney and / or ureter and / or bladder mask in the kidney and / or ureter and / or bladder mask image as a left kidney and / or ureter and / or bladder mask and a right kidney and / or ureter and / or bladder mask according to the kidney and / or ureter and / or bladder mask image; and determining the stone position corresponding to each stone type based on the stone mask in the stone mask image and the spatial position relationship between the left kidney and / or ureter and / or bladder mask and the right kidney and / or ureter and / or bladder mask in the kidney and / or ureter and / or bladder mask image.

[0063] In the embodiments of the present disclosure and other possible embodiments, the method for determining the stone position corresponding to each stone type based on the stone mask in the stone mask image and the spatial position relationship between the lateral kidney and / or ureter and / or bladder mask and the right kidney and / or ureter and / or bladder mask in the kidney and / or ureter and / or bladder mask image includes: if the dual-energy abdominal image is configured as a three-dimensional dual-energy abdominal CT image; extracting multiple two-dimensional slice stone mask images corresponding to each stone type from the stone mask image; if the spatial position of the stone mask corresponding to the multiple two-dimensional slice stone mask images is in the kidney and / or ureter and / or bladder mask image is within the left kidney and / or ureter and / or bladder mask spatial position of the two-dimensional slice stone mask image of the slice corresponding to the kidney and / or ureter and / or bladder mask image, then the stone corresponding to the stone mask spatial position is configured as a left kidney and / or ureter and / or bladder stone; if the stone mask spatial positions corresponding to the multiple two-dimensional slice stone mask images are within the right kidney and / or ureter and / or bladder mask spatial position of the two-dimensional slice stone mask image of the slice corresponding to the kidney and / or ureter and / or bladder mask image, then the stone corresponding to the stone mask spatial position is configured as a right kidney and / or ureter and / or bladder stone.

[0064] In the embodiments of the present disclosure and other possible embodiments, the method for determining the stone position corresponding to each stone type based on the stone mask in the stone mask image and the spatial position relationship between the lateral kidney and / or ureter and / or bladder mask and the right kidney and / or ureter and / or bladder mask in the kidney and / or ureter and / or bladder mask image includes: if the dual-energy abdominal image is configured as a two-dimensional dual-energy abdominal X-ray image; performing organ segmentation on the dual-energy abdominal image to obtain a two-dimensional organ mask image; according to the two-dimensional organ mask image, configuring the organ mask in the two-dimensional organ mask image as a left organ mask and a right organ mask; and determining the stone position corresponding to each stone type based on the stone mask in the stone mask image and the spatial position relationship between the lateral organ mask and the right organ mask in the two-dimensional organ mask image.

[0065] In the embodiments of the present disclosure and other possible embodiments, the method for determining the stone position corresponding to each stone type based on the spatial position relationship between the stone mask in the stone mask image and the side organ mask and the right organ mask in the organ mask image includes: if the stone mask spatial position corresponding to the stone mask image is within the left organ mask spatial position corresponding to the organ mask image, then the stone corresponding to the stone mask spatial position is configured as a left organ stone; if the stone mask spatial position corresponding to the stone mask image is within the right organ mask spatial position corresponding to the organ mask image, then the stone corresponding to the stone mask spatial position is configured as a right organ stone.

[0066] In an embodiment of the present disclosure, the limescale value corresponding to the stone information is determined based on the dual-energy abdominal image and the corresponding stone mask image.

[0067] In the embodiments of the present disclosure and other possible embodiments, the method for determining the grayscale value corresponding to the stone information based on the dual-energy abdominal image and its corresponding stone mask image includes: if the dual-energy abdominal image is configured as a three-dimensional dual-energy abdominal CT image; based on each two-dimensional slice dual-energy abdominal image and the two-dimensional slice stone mask image of the stone mask image of its corresponding slice in the dual-energy abdominal image, extracting the stone image with the original grayscale value from the dual-energy abdominal image; calculating the average grayscale value corresponding to each stone type in the stone mask image, and determining the stone grayscale value corresponding to each stone type.

[0068] In the embodiments of the present disclosure and other possible embodiments, the method for extracting a stone image with original grayscale values from the dual-energy abdominal image based on the two-dimensional slice stone mask image of each two-dimensional slice dual-energy abdominal image and the stone mask image of its corresponding slice in the dual-energy abdominal image includes: performing a multiplication operation on the two-dimensional slice stone mask image of each two-dimensional slice dual-energy abdominal image and the stone mask image of its corresponding slice in the dual-energy abdominal image to obtain a stone grayscale value image; performing grayscale value restoration on the stone grayscale value 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 image of the stone grayscale value image and the stone mask image of its corresponding slice to obtain a stone image with original grayscale values.

[0069] In the embodiments of the present disclosure and other possible embodiments, the method of performing grayscale value restoration on the stone grayscale value image based on the mask values corresponding to each stone type and the stone spatial position in the two-dimensional slice stone mask image of the stone mask image of its corresponding slice to obtain a stone image with original grayscale values includes: respectively extracting the mask values and stone spatial positions corresponding to each stone type in the two-dimensional slice stone mask image of the stone mask image; based on the stone spatial positions corresponding to each stone type, respectively performing stone type spatial position positioning on the stone grayscale value image to obtain spatial position positioning images corresponding to each stone type in the two-dimensional slice stone grayscale value image in the stone grayscale value image; dividing the spatial position positioning images corresponding to each stone type in the two-dimensional slice stone grayscale value image in the stone grayscale value image by the mask values corresponding to the stone spatial positions to obtain a stone image with original grayscale values.

[0070] In the embodiments of the present disclosure and other possible embodiments, if the dual-energy abdominal image is configured as a two-dimensional dual-energy abdominal X-ray image, a stone image with original grayscale values is extracted from the dual-energy abdominal image based on the dual-energy abdominal image and its corresponding stone mask image; the average grayscale value corresponding to each stone type in the stone mask image is calculated to determine the stone grayscale value corresponding to each stone type.

[0071] In an embodiment of the present disclosure, based on the stone mask image, one or more of the stone shape corresponding to the stone information, the major diameter and minor diameter corresponding to the stone, and the stone volume are determined.

[0072] In the embodiments of the present disclosure and other possible embodiments, the method for determining one or more of the stone shape, the major diameter and minor diameter corresponding to the stone, and the stone volume corresponding to the stone information based on the stone mask image includes: respectively calculating the stone area corresponding to each stone type in each two-dimensional slice stone mask image in the stone mask image corresponding to the dual-energy abdominal image; obtaining multiple stone areas; respectively calculating the major diameter and minor diameter corresponding to the maximum stone area among the multiple stone areas corresponding to each stone type, to obtain the major diameter and minor diameter corresponding to each stone type; and / or, respectively performing edge detection on the stones corresponding to each stone type on the stone mask image corresponding to the dual-energy abdominal image, to obtain stone edge mask lines; respectively fitting the stone edge mask lines corresponding to each stone type, to obtain the stone shape corresponding to each stone type; and / or, respectively reconstructing the mask images corresponding to each stone type in the stone mask image corresponding to the dual-energy abdominal image, to obtain the stone volume corresponding to each stone type.

[0073] In an embodiment of the present disclosure, the abdominal medical image processing method further includes: if the dual-energy abdominal image is configured as a three-dimensional dual-energy abdominal CT image, identifying the stone type corresponding to the stone information based on the urinary stone atomic number image corresponding to the dual-energy abdominal image. This addresses the technical problem of difficulty in distinguishing or identifying mixed urinary stones of different components, thereby failing to meet the clinical diagnosis requirements for mixed-component stones, which are more common than single-component stones.

[0074] In the embodiments of the present 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).

[0075] In the embodiments of the present disclosure and other possible embodiments, a spectral CT device (dual-energy CT device, spectral CT device), namely a dual-source CT device with fast tube voltage switching, employs instantaneous kVp switching technology to switch between high and low energy within an extremely short time (<0.25ms), achieving the three similarities of dual energy (simultaneous, same-direction, same-source). This overcomes the shortcomings of dual-tube dual-source CT, avoiding subtle angular discrepancies between the two tube scanning planes, improving data acquisition accuracy, and providing superior CT scan images.

[0076] In the embodiments disclosed herein and other possible embodiments, spectral CT scanning and 3D reconstruction can not only clearly display the density and morphology of the renal parenchyma, the orientation and contours of the renal pelvis and calyces, the course and lumen of the ureters, and the bladder wall and lumen, but can also show the morphological location of kidney stones and the renal pelvis and calyces, and further measure the volumes of stones and the renal pelvis and calyces, with imaging quality far superior to that of conventional CT 3D multi-planar reconstruction. In particular, the effective atomic number (Zeff value) of spectral CT can be used to analyze the compositional characteristics of kidney stones, revealing numerical differences between different regions, and based on these differences, clarifying the specific spatial distribution of different components in mixed stones.

[0077] In the embodiments of the present disclosure and other possible embodiments, the method for identifying the stone type corresponding to the stone information based on the urinary stone atomic number image corresponding to the dual-energy abdominal image includes: if the dual-energy abdominal image is configured as a three-dimensional dual-energy abdominal CT image; obtaining the urinary stone atomic number image corresponding to the dual-energy abdominal image; and identifying the stone type corresponding to the dual-energy abdominal image based on the urinary stone atomic number image.

[0078] In the embodiments of the present disclosure and other possible embodiments, the method for identifying the stone type corresponding to the dual-energy abdominal image based on the urinary stone atomic number image includes: extracting multiple component percentages of peak values corresponding to multiple bar graphs corresponding to the effective atomic numbers in the urinary stone atomic number image; identifying the stone type corresponding to the dual-energy abdominal image based on multiple component percentages corresponding to the multiple bar graphs and multiple set stone type component percentage intervals.

[0079] In the embodiments of the present disclosure and other possible embodiments, the method for extracting multiple component percentages of peak values corresponding to multiple bar graphs corresponding to effective atomic numbers in the atomic number image of urinary stones includes: obtaining a set bar graph color and / or a set bar graph width; extracting multiple bar graphs corresponding to effective atomic numbers that meet the set bar graph color and / or the set bar graph width in the atomic number image of urinary stones based on the set bar graph color and / or the set bar graph width; determining multiple component percentages of peak values corresponding to multiple bar graphs based on the multiple bar graphs that meet the set bar graph color and / or the set bar graph width in the atomic number image of urinary stones.

[0080] In the embodiment of the present disclosure and other possible embodiments, those skilled in the art may configure the bar graph color configuration and / or the bar graph width according to actual needs. For example, the bar graph color configuration is set to yellow.

[0081] In the embodiments of the present disclosure and other possible embodiments, before extracting the multiple bar graphs corresponding to the effective atomic numbers in the urinary tract stone atomic number image or extracting the multiple bar graphs corresponding to the effective atomic numbers that meet the set bar graph color and / or set bar graph width in the urinary tract stone atomic number image, the urinary tract stone atomic number image is corrected to obtain a corrected urinary tract stone atomic number image; and multiple component percentages of the peak values corresponding to the multiple bar graphs corresponding to the effective atomic numbers in the corrected urinary tract stone atomic number image are extracted or multiple component percentages of the peak values corresponding to the multiple bar graphs corresponding to the effective atomic numbers that meet the set bar graph color and / or set bar graph width in the corrected urinary tract stone atomic number image are extracted.

[0082] In the embodiments of the present disclosure and other possible embodiments, the method of correcting the urinary tract stone atomic number image to obtain the corrected urinary tract stone atomic number image includes: performing at least one correction processing operation such as angle adjustment, contrast enhancement, and scaling on the urinary tract stone atomic number image to obtain the corrected urinary tract stone atomic number image, so as to facilitate the subsequent extraction of multiple bar graphs corresponding to the effective atomic numbers.

[0083] In the embodiments of the present disclosure and other possible embodiments, optical character recognition technology is used to extract multiple component percentages of peak values corresponding to multiple bar graphs corresponding to effective atomic numbers in the urinary stone atomic number image; or, optical character recognition technology is used to extract multiple component percentages of peak values corresponding to multiple bar graphs corresponding to effective atomic numbers that meet the set bar graph color and / or set bar graph width in the corrected urinary stone atomic number image.

[0084] In the embodiments of the present disclosure and other possible embodiments, optical character recognition (OCR) technology is a computer vision technology that uses image processing and machine learning algorithms to identify and extract text content in images and convert it into a machine-readable and editable text format.

[0085] In an embodiment of the present disclosure, the method for identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and the multiple set stone type component percentage intervals includes: obtaining the set component percentage corresponding to the interval that is less than the minimum value of the set stone type component percentage interval; if the component percentage corresponding to the multiple bar graphs is less than the set component percentage, deleting the component percentage corresponding to the multiple component percentages corresponding to the multiple bar graphs that is less than the set component percentage; otherwise, retaining the component percentage corresponding to the set component percentage that is greater than or equal to the set component percentage; identifying the stone type corresponding to the dual-energy abdominal image based on the retained multiple component percentages and the multiple set stone type component percentage intervals.

[0086] In the embodiments of the present disclosure and other possible embodiments, those skilled in the art may configure the set component percentage corresponding to the interval less than the minimum value in the set stone type component percentage interval according to actual needs. For example, the set component percentage corresponding to the interval less than the minimum value in the set stone type component percentage interval is configured to be 5%.

[0087] In the embodiments of the present disclosure and other possible embodiments, the implementation of OCR requires calling the API of the GOT_OCR2 tool and setting its parameters such as language and text box positioning; atomic number peak recognition and morphological calculation require using Python's NumPy, SciPy or OpenCV library to execute the extraction of multiple component percentages of peaks corresponding to multiple bar graphs corresponding to the effective atomic numbers in the urinary stone atomic number image or multiple component percentages of peaks corresponding to multiple bar graphs corresponding to the effective atomic numbers in the urinary stone atomic number image that meet the set bar graph color and / or set bar graph width.

[0088] In the embodiments of the present disclosure and other possible embodiments, the method for identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and the multiple set stone type component percentage intervals includes: obtaining the set component percentage corresponding to the interval that is less than the minimum value of the interval in the set stone type component percentage interval; if the component percentage corresponding to the multiple bar graphs is less than the set component percentage, deleting the component percentage corresponding to the multiple component percentages corresponding to the multiple bar graphs that is less than the set component percentage; otherwise, retaining the component percentage corresponding to the set component percentage that is greater than or equal to the set component percentage; identifying the stone type corresponding to the dual-energy abdominal image based on the retained multiple component percentages and the multiple set stone type component percentage intervals.

[0089] In the embodiments of the present disclosure and other possible embodiments, the method for identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and the multiple set stone type component percentage intervals also includes: if there is an overlapping interval in the set stone type component percentage intervals corresponding to at least two of the multiple component percentages corresponding to the multiple bar graphs, obtaining an iodine-based image corresponding to the dual-energy abdominal image; and identifying the stone type corresponding to the overlapping interval based on the iodine-based image corresponding to the dual-energy abdominal image.

[0090] In the embodiments of the present disclosure and other possible embodiments, the method for identifying the stone type corresponding to the overlapping interval based on the iodine-based image corresponding to the dual-energy abdominal image includes: determining the stone grayscale value corresponding to the iodine-based image corresponding to the dual-energy abdominal image; respectively calculating the average grayscale value and uniformity corresponding to the stone grayscale value; if the average grayscale value is greater than the set average grayscale value and the uniformity is greater than the set uniformity, configuring the stone type as calcium oxalate dihydrate stone; otherwise, configuring the stone type as struvite stone or carbonate apatite stone.

[0091] In the embodiments of the present disclosure and other possible embodiments, the multiple set stone type composition percentage intervals include: one or more of: uric acid stone composition percentage intervals, calcium oxalate monohydrate stone composition percentage intervals, calcium oxalate dihydrate stone composition percentage intervals, carbonate apatite stone composition percentage intervals, calcium carbonate phosphate stone composition percentage intervals, and struvite stone composition percentage intervals.

[0092] In the embodiments of the present disclosure and other possible embodiments, those skilled in the art may configure the set average grayscale value and the set uniformity according to actual needs.

[0093] In the embodiment of the present disclosure and other possible embodiments, uniformity = (maximum value - minimum value) / (2 * average value) × 100%, where the maximum value represents the maximum value corresponding to the grayscale value of the cementation, the minimum value represents the minimum value corresponding to the grayscale value of the cementation, and the average value represents the average grayscale value corresponding to the grayscale value of the cementation.

[0094] In the embodiments of the present disclosure and other possible embodiments, the percentage interval of the uric acid stone component is configured to be 6.5-10.5, the percentage interval of the calcium oxalate monohydrate stone component is configured to be 13.3-14.0, the percentage interval of the calcium oxalate dihydrate stone component is configured to be 12.0-13.3, the percentage interval of the carbonate apatite stone component is configured to be 14.0-15.0, the percentage interval of the calcium carbonate phosphate stone component is configured to be greater than 12.5, and the percentage interval of the struvite stone component is configured to be less than 12.5.

[0095] In the embodiments of the present disclosure and other possible embodiments, the Zeff peak value (component percentage interval) of uric acid stones is between 6.5-10.5; the Zeff peak value of calcium oxalate monohydrate stones is between 13.3-14.0; the Zeff peak value of calcium oxalate dihydrate stones is between 12.0-13.3; the Zeff peak value of carbonate apatite stones is between 14.0-15.0; the Zeff peak value of calcium carbonate phosphate stones is greater than 12.5 and all have CT value images with uneven density; the Zeff peak value of struvite stones is less than 12.5 and all have CT value images with uneven density.

[0096] In the embodiments of the present disclosure and other possible embodiments, the method for identifying the stone type corresponding to the dual-energy abdominal image based on the multiple component percentages corresponding to the multiple bar graphs and the multiple set stone type component percentage intervals includes: constructing a stone component comparison table according to the multiple set stone type component percentage intervals; based on the stone component comparison table, performing a table lookup and comparison operation on the multiple component percentages corresponding to the multiple bar graphs to identify the stone type corresponding to the dual-energy abdominal image.

[0097] The abdominal medical image processing method may be executed by an abdominal medical image processing device / system. For example, the abdominal medical image processing method may be executed by a terminal device, a server, or other processing device, wherein the terminal device may be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, an in-vehicle device, a wearable device, etc. In some possible implementations, the abdominal medical image processing method may be implemented by a processor calling computer-readable instructions stored in a memory.

[0098] Those skilled in the art will understand that in the above-mentioned abdominal medical image processing method of the specific embodiment, the writing order of each step does not mean 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.

[0099] According to one aspect of an embodiment of the present disclosure, a device / system for processing abdominal medical images is provided, including: an acquisition unit for acquiring a preset segmentation network, a dual-energy abdominal training image corresponding to the preset segmentation network for training, and a multi-mask label fusion image corresponding to the dual-energy abdominal training image representing the kidneys, ureters, bladder and stones; a training unit for training the preset segmentation network using the dual-energy abdominal training image and the corresponding multi-mask label fusion image to obtain a corresponding stone segmentation model; a processing unit for segmenting the stones in the kidneys, ureters and bladder of the dual-energy abdominal image based on the stone segmentation model to obtain a stone mask image.

[0100] According to one aspect of an embodiment of the present disclosure, a device / system for processing abdominal medical images is provided, comprising: an electronic device configured with a processor and a memory for storing processor-executable instructions; wherein the processor is configured to call instructions stored in the memory to execute the above-mentioned method for processing abdominal medical images.

[0101] According to one aspect of an embodiment of the present disclosure, a device / system for processing abdominal medical images is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to call the instructions stored in the memory to execute the above-mentioned method for processing abdominal medical images.

[0102] According to one aspect of an embodiment of the present disclosure, a device / system for processing abdominal medical images is provided, comprising: a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions implement the above-mentioned method for processing abdominal medical images when executed by a processor.

[0103] According to one aspect of an embodiment of the present disclosure, a device / system for processing abdominal medical images is provided, including: a computer program product, including a computer program / instruction, which implements the above-mentioned method for processing abdominal medical images when executed by a processor.

[0104] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above embodiment of the method for processing abdominal medical images. Its specific implementation can refer to the description of the above embodiment of the method for processing abdominal medical images. For the sake of brevity, it will not be repeated here.

[0105] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the above-mentioned abdominal medical image processing method. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0106] The present disclosure also provides an electronic device comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to implement the above-mentioned abdominal medical image processing method. The electronic device may be provided as a terminal, server, or other device.

[0107] Figure 2 8 is a block diagram of an electronic device 800 according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like.

[0108] 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 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 .

[0109] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 802 may include one or more modules to facilitate interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

[0110] The memory 804 is configured to store various types of data to support operations on the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 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 memory, flash memory, magnetic disk, or optical disk.

[0111] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0112] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0113] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), which is configured to receive external audio signals when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0114] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0115] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the electronic device 800, the relative positioning of components, such as the display and keypad of the electronic device 800. The sensor assembly 814 can also detect changes in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and temperature changes of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The 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, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0116] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0117] 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 above methods.

[0118] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions. The computer program instructions can be executed by the processor 820 of the electronic device 800 to perform the above method.

[0119] Figure 3 1 is a block diagram of an electronic device 1900 according to an exemplary embodiment. For example, the electronic device 1900 may be provided as a server. Figure 3The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute the instructions to perform the above-described method.

[0120] The electronic device 1900 may further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or the like.

[0121] 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 the processing component 1922 of the electronic device 1900 to perform the above method.

[0122] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0123] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0124] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0125] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, and conventional procedural programming languages such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer 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., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0126] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0127] 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 device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0128] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are 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 implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0129] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction contains one or more executable instructions for realizing the prescribed logical function. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the prescribed function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0130] While various embodiments of the present disclosure have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not 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 selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for processing abdominal medical images, characterized in that: include: Obtaining a preset segmentation network, a dual-energy abdominal training image corresponding to the preset segmentation network for training, and a multi-mask label fusion image representing the kidney, ureter, bladder, and stone corresponding to the dual-energy abdominal training image; Using the dual-energy abdominal training image and the corresponding multi-mask label fusion image, the preset segmentation network is trained to obtain a corresponding stone segmentation model; Based on the stone segmentation model, the stones in the kidney, ureter and bladder are segmented in the dual-energy abdominal image to obtain a stone mask image.

2. The method for processing abdominal medical images according to claim 1, characterized in that: The method for constructing the multi-mask label fusion image representing the kidney, bladder, ureter, ureter and stone respectively includes: obtaining an organ mask label image corresponding to at least one organ of the kidney, ureter, and bladder in the dual-energy abdominal training image and a stone mask label image in the organ; generating a multi-mask label fusion image corresponding to the dual-energy abdominal training image based on the organ mask label image and the stone mask label image; and / or, The method for generating a multi-mask label fusion image corresponding to the dual-energy abdomen training image based on the organ mask label image and the stone mask label image comprises: generating a multi-mask label fusion image corresponding to the dual-energy abdomen training image based on the organ mask value corresponding to the organ mask label image and the organ spatial position corresponding to the organ mask value, the stone mask value corresponding to the stone mask label image and the second spatial position corresponding to the stone mask value; and / or, Before obtaining the organ mask label image corresponding to at least one organ of the kidney, ureter, and bladder in the dual-energy abdominal training image and the stone mask label image in the organ, the stone mask label image is determined based on the iodine-based image / calcium image corresponding to the dual-energy abdominal training image, and the organ mask label image is determined based on the water-based image / soft tissue image corresponding to the dual-energy abdominal training image.

3. The method for processing abdominal medical images according to any one of claims 1 or 2, characterized in that: The method of training the preset segmentation network using the dual-energy abdominal training image and the corresponding multi-mask label fusion image to obtain the corresponding stone segmentation model includes: Obtaining a set loss value corresponding to the stone segmentation model; In the process of training the preset segmentation network using the dual-energy abdominal training image and its corresponding multi-mask label fusion image, if the loss value corresponding to the stone segmentation model is less than or equal to the set loss value, the training of the preset segmentation network is stopped to obtain the corresponding stone segmentation model.

4. The method for processing abdominal medical images according to any one of claims 1 to 3, characterized in that: Also includes: extracting corresponding stone information based on the dual-energy abdominal image and / or the stone mask image; The stone information is configured as one or more of the following: stone location, stone shape, stone calcification value, long diameter and short diameter corresponding to the stone, stone area or stone volume.

5. The method for processing abdominal medical images according to claim 4, characterized in that: Determining the stone position corresponding to the stone information based on the kidney and / or ureter and / or bladder mask images corresponding to the dual-energy abdomen image and the stone mask image; and / or, Determining a limescale value corresponding to the stone information based on the dual-energy abdominal image and the corresponding stone mask image; and / or, Based on the stone mask image, one or more of the stone shape corresponding to the stone information, the major diameter and minor diameter corresponding to the stone, and the stone volume are determined.

6. The method for processing abdominal medical images according to any one of claims 1 to 5, characterized in that: Also includes: If the dual-energy abdominal image is configured as a three-dimensional dual-energy abdominal CT image, the stone type corresponding to the stone information is identified based on the urinary stone atomic number image corresponding to the dual-energy abdominal image.

7. A system for processing abdominal medical images, characterized in that: include: an acquisition unit, configured to acquire a preset segmentation network, a dual-energy abdominal training image corresponding to the preset segmentation network for training, and a multi-mask label fusion image corresponding to the dual-energy abdominal training image representing the kidney, ureter, bladder, and stone; a training unit, configured to train the preset segmentation network using the dual-energy abdominal training image and the corresponding multi-mask label fusion image to obtain a corresponding stone segmentation model; The processing unit is used to segment the stones in the kidney, ureter and bladder of the dual-energy abdominal image based on the stone segmentation model to obtain a stone mask image.

8. A system for processing abdominal medical images, characterized in that: include: An electronic device, the electronic device being configured with a processor and a memory for storing instructions executable by the processor; The processor is configured to call the instructions stored in the memory to execute the abdominal medical image processing method according to any one of claims 1 to 6; or The method comprises: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to call the instructions stored in the memory to execute the abdominal medical image processing method according to any one of claims 1 to 6.

9. A system for processing abdominal medical images, characterized in that: include: A computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the abdominal medical image processing method according to any one of claims 1 to 6.

10. A system for processing abdominal medical images, characterized in that: include: A computer program product comprises a computer program / instruction, which implements the abdominal medical image processing method according to any one of claims 1 to 6 when executed by a processor.

Images