Method and system for processing abdominal medical images

Abstract

The present disclosure relates to a kind of abdominal medical image processing method and system, it is related to abdominal image processing technical field.Therein, the abdominal medical image processing method described, comprising: obtaining preset segmentation network, for training the double-energy abdominal training image corresponding to the preset segmentation network, the double-energy abdominal training image corresponding multi-mask label fusion image for indicating kidney, ureter, bladder and stone;Using the double-energy abdominal training image and its corresponding multi-mask label fusion image, the preset segmentation network is trained, and the corresponding stone segmentation model is obtained;Based on the stone segmentation model, kidney, ureter and bladder in double-energy abdominal image are segmented in stone, and stone mask image is obtained.The present disclosure embodiment can realize the processing of abdominal medical image.

Description

TECHNICAL FIELD

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

[0002] Urinary calculi is a common urological disease, urinary calculi can be found in any part of the kidney, bladder, ureter and urethra, but kidney, bladder and ureter calculi are common. The clinical manifestations are different due to the different parts of the calculi. Its early diagnosis and accurate measurement are crucial for developing effective treatment plans.

[0003] Urinary calculi can cause pain, dysuria, infection, hematuria and other clinical symptoms, if not treated in time, it can easily lead to kidney damage and even kidney failure, and also increase the incidence of many chronic diseases, including osteoporosis and cardiovascular disease. In addition, urinary calculi often recur, about 40% of patients have one or more times of stone recurrence.

[0004] At present, the composition diagnosis of postoperative urinary calculi mainly relies on infrared spectrum analysis of stone samples, which has certain significance for preventing urinary calculi recurrence, but is of no help to preoperative selection of appropriate stone treatment methods. Urinary calculi diagnosis methods rely on medical imaging techniques such as X-ray and CT scan. However, due to the corresponding spatial positions of the kidney, ureter and bladder including the spatial position of urinary calculi, there is a lack of a multi-mask label fusion image representing the kidney, ureter, bladder and calculi, so that the segmentation of the calculi does not fully utilize the anatomical structure information of the spatial positions of the kidney, ureter, bladder and calculi, and further lacks the synergistic effect of the anatomical structure information of the kidney, ureter, bladder and calculi.

[0005] Therefore, it is necessary to propose an abdominal image processing technical scheme based on a multi-mask label fusion image representing the kidney, ureter, bladder and calculi, to solve the technical problem that the anatomical structure information of the spatial positions of the kidney, ureter, bladder and calculi is not fully utilized, and further lacks the synergistic effect of the anatomical structure information of the kidney, ureter, bladder and calculi. SUMMARY

[0006] The present disclosure proposes a technical scheme of a processing method and system of abdominal medical images.

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

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

[0009] The pre-set segmentation network is trained by using the dual-energy abdominal training image and the corresponding multi-mask label fusion image, to obtain a corresponding stone segmentation model.

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

[0011] Preferably, the method for constructing the multi-mask label fusion image representing the kidneys, bladders, ureters, ureters and stones respectively comprises: obtaining organ mask label images corresponding to at least one organ in the kidneys, ureters and bladders in the dual-energy abdominal training image and stone mask label images in the organs; and generating a multi-mask label fusion image corresponding to the dual-energy abdominal training image based on the organ mask label images and the stone mask label images.

[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 images and the stone mask label images comprises: generating a multi-mask label fusion image corresponding to the dual-energy abdominal training image based on organ mask values corresponding to the organ mask label images and organ spatial positions corresponding to the organ mask values, and stone mask values corresponding to the stone mask label images and second spatial positions corresponding to the stone mask values.

[0013] Preferably, before obtaining the organ mask label images corresponding to at least one organ in the kidneys, ureters and bladders in the dual-energy abdominal training image and the stone mask label images in the organs, the stone mask label images are determined based on the iodine-based images / calcium images corresponding to the dual-energy abdominal training image, and the organ mask label images are determined based on the water-based images / soft tissue images corresponding to the dual-energy abdominal training image.

[0014] Preferably, the method for training the pre-set segmentation network by using the dual-energy abdominal training image and the corresponding multi-mask label fusion image to obtain a corresponding stone segmentation model comprises: obtaining a set loss value corresponding to the stone segmentation model; and during the process of training the pre-set segmentation network by 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, the training of the pre-set segmentation network is stopped to obtain a corresponding stone segmentation model.

[0015] Preferably, the processing method of the abdominal medical image further comprises: 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 stone position, stone shape, stone grayscale value, long diameter and short diameter corresponding to 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 image corresponding to the dual-energy abdominal image and the stone mask image.

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

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

[0019] Preferably, the processing method of the abdominal medical image further comprises: 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.

[0020] According to an aspect of the present disclosure, a processing device / system of an abdominal medical image is provided, comprising:

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

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

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

[0024] According to an aspect of the present disclosure, a processing device / system of an abdominal medical image is provided, comprising: an electronic device 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 perform the above-mentioned processing method of the abdominal medical image.

[0025] According to an aspect of the present disclosure, there is provided an apparatus / system for processing an abdominal medical image, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to perform the above-mentioned method for processing an abdominal medical image.

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

[0027] According to an aspect of the present disclosure, there is provided an apparatus / system for processing an abdominal medical image, comprising: a computer program product comprising computer programs / instructions which, when executed by a processor, implement the above-mentioned method for processing an abdominal medical image.

[0028] In the embodiments of the present disclosure, a method and system for processing an abdominal medical image are provided to solve the technical problem that anatomical structure information of a kidney, ureter, bladder and space position of a stone is not fully utilized, and anatomical structure information of the kidney, ureter, bladder and stone lacks synergistic effect.

[0029] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, but not limiting the present disclosure.

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

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

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

[0033] Figure 2 FIG. 8 is a block diagram of an electronic device 800 according to an exemplary embodiment;

[0034] Figure 3 FIG. 19 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 numbers in different drawings represent the same or similar elements. Although various aspects of embodiments are illustrated in the drawings, the drawings are not necessarily drawn to scale unless specifically noted.

[0036] The term "exemplary" is used herein to mean "serving as an example, instance, 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" used herein only means an association relationship of the associated objects, and means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, at least one of A, B, and C includes any one or more elements selected from the set consisting of A, B, and C.

[0038] In addition, in order to better illustrate the present disclosure, a large number of specific details are given in the specific embodiments below. Those skilled in the art should understand that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, elements and circuits that are well known to those skilled in the art are not described in detail, in order to highlight the main ideas of the present disclosure.

[0039] It can be understood that the above-mentioned various method embodiments of the present disclosure for processing abdominal medical images can be combined with each other to form combined embodiments without deviating from the principle logic. Due to the limited space, the present disclosure will not be described again.

[0040] In addition, the present disclosure also provides an abdominal medical image processing device / system, an electronic device, a computer readable storage medium, and a program, which can be used to implement any of the abdominal medical image processing methods provided by the present disclosure. The corresponding technical solutions and descriptions are described in the method part and will not be described again.

[0041] Figure 1 A flowchart of an abdominal medical image processing method according to an embodiment of the present disclosure is shown. As shown in FIG. 1, the method includes the following steps. Figure 1As shown, the abdominal medical image processing method comprises the following steps: S101: 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 corresponding to the dual-energy abdominal training image and representing a kidney, a ureter, a bladder, and a stone; S102: training the preset segmentation network by using the dual-energy abdominal training image and the multi-mask label fusion image corresponding thereto, to obtain a corresponding stone segmentation model; and S103: segmenting stones in the kidney, the ureter, and the bladder of a dual-energy abdominal image based on the stone segmentation model, to obtain a stone mask image. Thus, the technical problem that the anatomical structure information of the kidney, the ureter, the bladder, and the stone is not fully utilized, and the anatomical structure information of the kidney, the ureter, the bladder, and the stone lacks synergistic effect is solved.

[0042] S101: 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 corresponding to the dual-energy abdominal training image and representing a kidney, a ureter, a bladder, and a stone.

[0043] In the embodiments and other possible embodiments of the present disclosure, a patient's abdomen is scanned by using a spectral CT device (dual-energy CT device, spectral CT device), 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 embodiments and other possible embodiments of the present disclosure, a patient's abdomen is photographed by using a dual-energy digital X-ray machine, to obtain a two-dimensional dual-energy abdominal image (dual-energy abdominal image / dual-energy abdominal X-ray image).

[0045] In the embodiments and other possible embodiments of the present disclosure, 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 and other possible embodiments of the present disclosure, the preset segmentation network is configured as one or more of Unet, ResUnet, Unet++, ResUnet++, nnUnet, SegNet, PSPNet, DeepLab, RefineNet, Medformer, or an improved segmentation network thereof.

[0047] In embodiments of the present disclosure, the method for constructing the multi-mask label fusion image representing the kidney, bladder, ureter, ureter and stone respectively comprises: obtaining organ mask label images corresponding to at least one organ of the kidney, ureter and bladder in the dual-energy abdominal training image and stone mask label images 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 images and the stone mask label images.

[0048] In embodiments 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 images and the stone mask label images comprises: generating a multi-mask label fusion image corresponding to the dual-energy abdominal training image based on organ mask values corresponding to the organ mask label images and organ spatial positions corresponding to the organ mask values, stone mask values corresponding to the stone mask label images and second spatial positions corresponding to the stone mask values; and / or,

[0049] In embodiments of the present disclosure, before obtaining the organ mask label images corresponding to at least one organ of the kidney, ureter and bladder in the dual-energy abdominal training image and the stone mask label images 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 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 comprises: performing stone delineation on the iodine-based image / calcium image corresponding to the dual-energy abdominal training image to determine the stone mask label image.

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

[0052] In embodiments of the present disclosure and other possible embodiments, the method for generating the 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: 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 respectively; if the organ spatial position and the stone spatial position overlap, 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 the multi-mask label fusion image corresponding to the dual-energy abdominal training image.

[0053] In embodiments of the present disclosure and other possible embodiments, before the step of 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 respectively, the method further includes: if the number corresponding to the stone mask image is greater than 1, acquiring the stone spatial positions corresponding to the plurality of stone mask images respectively; determining whether there is spatial position overlap between the stone spatial positions corresponding to the plurality of stone mask images; if there is spatial position overlap, generating the multi-mask label fusion image of each stone and its corresponding organ corresponding to the spatial position overlap and the multi-mask label fusion image of the stone and its corresponding organ corresponding to the spatial position not overlapping together; otherwise, generating the multi-mask label fusion image corresponding to all stones and their corresponding organs together.

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

[0055] In embodiments of the present disclosure, the method for training the preset segmentation network by using the dual-energy abdominal training image and the multi-mask label fusion image corresponding thereto to obtain a corresponding stone segmentation model includes: acquiring a set loss value corresponding to the stone segmentation model; during the process of training the preset segmentation network by using the dual-energy abdominal training image and the multi-mask label fusion image corresponding thereto, 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 a corresponding stone segmentation model.

[0056] In embodiments and other possible embodiments of the present disclosure, in the process of training the preset segmentation network by using the dual-energy abdominal training image and the corresponding multi-mask label fusion image, the organ loss function corresponding to the organ and the organ loss weight value corresponding to the organ loss function, the stone loss function corresponding to the stone and the stone loss weight value corresponding to the stone loss function which is less than the organ loss weight value are obtained; in the process of training the preset segmentation network, if the organ loss value corresponding to the organ loss function is less than a first set loss value and / or the total loss value corresponding to the organ loss function and the stone loss function is less than a second set loss value, then in each training process of continuing to train the preset segmentation network, the stone loss weight value is increased by a set proportion to obtain a stone loss regulation weight value; the organ loss regulation weight value corresponding to the organ loss weight value is determined according to the stone loss regulation 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 based on the stone loss regulation weight value and the organ loss regulation weight value; 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, the training of the preset segmentation network is stopped.

[0057] In embodiments and other possible embodiments of the present disclosure, the method for determining the organ loss regulation weight value corresponding to the organ loss weight value according to the stone loss regulation weight value comprises: obtaining a set configuration value corresponding to the sum of the organ loss weight value and the stone loss weight value; determining the organ loss regulation weight value corresponding to the organ loss weight value by subtracting the stone loss regulation weight value from the set configuration value; and 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 embodiments and other possible embodiments of the present disclosure, the organ loss function and the stone loss function are respectively configured as one or more of a cross-entropy loss function, a Dice loss function, a Focal loss function, a Tversky loss function, a Tversky loss function, and an IoU loss function.

[0059] Step S103: based on the stone segmentation model, the stones in the kidneys, ureters and bladders of the dual-energy abdominal image are segmented to obtain a stone mask image.

[0060] In embodiments of the present disclosure, the method for processing the abdominal medical image 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 stone position, stone shape, stone grayscale value, stone corresponding long diameter and short diameter, stone area, or stone volume.

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

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

[0063] In 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 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 includes: if the dual-energy abdominal image is configured as a three-dimensional dual-energy abdominal CT image; extracting a plurality of two-dimensional slice stone mask images including the stone mask corresponding to each stone type from the stone mask image; if the stone mask spatial position corresponding to the plurality of two-dimensional slice stone mask images is within the left kidney and / or ureter and / or bladder mask spatial position of the two-dimensional slice stone mask image of the corresponding slice of the kidney and / or ureter and / or bladder mask image, 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 position corresponding to the plurality of two-dimensional slice stone mask images is within the right kidney and / or ureter and / or bladder mask spatial position of the two-dimensional slice stone mask image of the corresponding slice of the kidney and / or ureter and / or bladder mask image, the stone corresponding to the stone mask spatial position is configured as a right kidney and / or ureter and / or bladder stone.

[0064] In 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 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 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; configuring the organ mask in the two-dimensional organ mask image as a left organ mask and a right organ mask according to the two-dimensional organ mask image; determining the stone position corresponding to each stone type based on the spatial position relationship between the stone mask in the stone mask image and the left organ mask and the right organ mask in the two-dimensional organ mask image.

[0065] In 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 left 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, 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, the stone corresponding to the stone mask spatial position is configured as a right organ stone.

[0066] In embodiments of the present disclosure, based on the dual-energy abdominal image and the corresponding stone mask image thereof, a stone gray value corresponding to the stone information is determined.

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

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

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

[0070] In 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 gray values is extracted from the dual-energy abdominal image based on the dual-energy abdominal image and the corresponding stone mask image thereof; average gray values corresponding to each stone type in the stone mask image are calculated to determine stone gray values corresponding to each stone type.

[0071] In embodiments of the present disclosure, based on the stone mask image, one or more of stone shape, long diameter and short diameter corresponding to the stone, and stone volume corresponding to the stone information are determined.

[0072] In embodiments of the present disclosure and other possible embodiments, the method for determining one or more of stone shape, long diameter and short diameter corresponding to the stone, and stone volume corresponding to the stone based on the stone mask image, comprises: calculating stone areas 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 respectively; obtaining a plurality of stone areas; calculating long diameter and short diameter corresponding to the maximum stone area in the plurality of stone areas corresponding to each stone type respectively to obtain long diameter and short diameter corresponding to each stone type; and / or 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; fitting the stone edge mask lines corresponding to each stone type respectively to obtain stone shape corresponding to each stone type; and / or reconstructing the mask images corresponding to each stone type in the stone mask image corresponding to the dual-energy abdominal image to obtain stone volume corresponding to each stone type.

[0073] In embodiments of the present disclosure, the method for processing abdominal medical images further comprises: 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. Thus, the technical problem that it is difficult to identify or recognize mixed urinary stones of different components, so as to not meet the diagnostic needs of mixed component stones more than single component stones in the clinic is solved.

[0074] In embodiments of the present disclosure and other possible embodiments, a patient's abdomen is scanned by using a spectral CT device (dual-energy CT device, spectral CT device) 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 and other possible embodiments of the present disclosure, the spectral CT device (dual-energy CT device, spectral CT device), i.e., a fast tube voltage switching dual-source CT device, adopts a transient kVp switching technology to complete switching of high and low single energies in a very short time (<0.25 ms), and realizes three samenesses (simultaneity, same direction, and same source) of dual energies. It overcomes the defects of a dual-ball tube dual-source CT device, thereby avoiding a slight angle difference between two ball tube scanning planes, and improves the accuracy of acquired data to provide a more excellent CT scanning image.

[0076] In the embodiments and other possible embodiments of the present disclosure, the spectral CT scanning and three-dimensional reconstruction can not only clearly display renal parenchyma density and morphology, orientation and contour of a renal pelvis and calyx, ureter running and lumen, bladder wall and intracavity condition, but also display a kidney stone and a renal pelvis and calyx morphology position, and further measure a stone and a renal pelvis and calyx volume, and the imaging quality is far superior to that of a conventional CT three-dimensional multiplanar reconstruction. In particular, an effective atomic number (Zeff value) of the spectral CT can analyze a composition characteristic of a kidney stone, display a numerical difference of different regions, and clearly show a specific spatial distribution mode of different compositions in a mixed stone according to the difference.

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

[0078] In the embodiments and other possible embodiments of the present disclosure, the method for identifying a stone type corresponding to the dual-energy abdominal image based on the urinary stone atomic number image comprises: extracting a plurality of component percentages of a plurality of peak values of a plurality of strip charts corresponding to an effective atomic number in the urinary stone atomic number image; and identifying a stone type corresponding to the dual-energy abdominal image based on a plurality of component percentages corresponding to the plurality of strip charts and a plurality of set stone type component percentage intervals.

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

[0080] In embodiments of the present disclosure and other possible embodiments, the set bar graph color can be configured by a person skilled in the art according to actual needs. For example, the set bar graph color is yellow.

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

[0082] In embodiments of the present disclosure and other possible embodiments, the method for correcting the urinary stone atomic number image to obtain a corrected urinary stone atomic number image includes at least one correction processing operation of angle adjustment, contrast enhancement, scaling, etc. of the urinary stone atomic number image, so as to extract the plurality of bar graphs of the effective atomic numbers later.

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

[0084] In embodiments and other possible embodiments of the present disclosure, Optical Character Recognition (OCR) is a computer vision technology that identifies and extracts textual content from images through image processing and machine learning algorithms, and converts it into machine-readable and editable text format.

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

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

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

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

[0089] In embodiments of the present disclosure and other possible embodiments, the method for identifying the stone type corresponding to the dual-energy abdominal image based on the plurality of component percentages corresponding to the plurality of bar charts and the plurality of set stone type component percentage intervals further includes: if there is an overlapping interval in the set stone type component percentage intervals corresponding to at least two component percentages of the plurality of component percentages, 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 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 a stone grayscale value corresponding to the iodine-based image corresponding to the dual-energy abdominal image; calculating an average grayscale value and a uniformity corresponding to the stone grayscale value, respectively; if the average grayscale value is greater than a set average grayscale value and the uniformity is greater than a 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 embodiments of the present disclosure and other possible embodiments, the plurality of set stone type component percentage intervals includes one or more of a uric acid stone component percentage interval, a calcium oxalate monohydrate stone component percentage interval, a calcium oxalate dihydrate stone component percentage interval, a carbonate apatite stone component percentage interval, a calcium phosphate stone component percentage interval, and a struvite stone component percentage interval.

[0092] In embodiments of the present disclosure and other possible embodiments, the set average grayscale value and the set uniformity can be configured by a person skilled in the art according to actual needs.

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

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

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

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

[0097] The execution subject of the abdominal medical image processing method can be an abdominal medical image processing device / system. For example, the abdominal medical image processing method can be executed by a terminal device or a server or other processing device. The terminal device can be a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementation manners, the abdominal medical image processing method can be realized by a processor calling computer readable instructions stored in a memory.

[0098] Those skilled in the art can understand that, in the above-mentioned specific implementation of the processing method of the abdominal medical image, 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 an aspect of an embodiment of the present disclosure, a processing apparatus / system of an abdominal medical image is provided, comprising: an acquisition unit configured to acquire a preset segmentation network, a dual-energy abdominal training image used for training the preset segmentation network, and a multi-mask label fusion image corresponding to the dual-energy abdominal training image and representing a kidney, a ureter, a bladder and a stone; a training unit configured to train the preset segmentation network by using the dual-energy abdominal training image and the multi-mask label fusion image corresponding thereto, to obtain a corresponding stone segmentation model; and a processing unit configured to segment a stone in the kidney, the ureter and the bladder of a dual-energy abdominal image based on the stone segmentation model, to obtain a stone mask image.

[0100] According to an aspect of an embodiment of the present disclosure, a processing apparatus / system of an abdominal medical image is provided, comprising: an electronic device 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 above-mentioned processing method of the abdominal medical image.

[0101] According to an aspect of an embodiment of the present disclosure, a processing apparatus / system of an abdominal medical image 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-mentioned processing method of the abdominal medical image.

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

[0103] According to an aspect of an embodiment of the present disclosure, a processing apparatus / system of an abdominal medical image is provided, comprising: a computer program product comprising computer programs / instructions, which, when executed by a processor, implement the above-mentioned processing method of the abdominal medical image.

[0104] In some embodiments, the apparatus provided by the embodiments of the present disclosure has functions or includes modules that can be used to execute the methods described in the above embodiments of the processing method of the abdominal medical image. The specific implementation can refer to the description of the above embodiments of the processing method of the abdominal medical image, and for the sake of brevity, it will not be described here.

[0105] The embodiment of the present disclosure further provides a computer readable storage medium, which stores computer program instructions. The computer program instructions are executed by a processor to implement the method for processing an abdominal medical image. The computer readable storage medium can be a non-volatile computer readable storage medium.

[0106] The embodiment of the present disclosure further provides an electronic device, which comprises a processor and a memory for storing processor-executable instructions. The processor is configured to implement the method for processing an abdominal medical image. The electronic device can be provided as a terminal, a server or other forms of devices.

[0107] Figure 2 is a block diagram of an electronic device 800 according to an exemplary embodiment. The electronic device 800 can be a terminal such as 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, and the like, for example.

[0108] Referring to Figure 2 , the electronic device 800 can 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.

[0109] The processing component 802 usually controls overall operations of the electronic device 800, such as operations associated with displaying, making phone calls, data communications, camera operations and recording operations. The processing component 802 can include one or more processors 820 to execute instructions to complete all or part of steps of the methods described above. Further, the processing component 802 can include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate the 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 of the electronic device 800. Examples of the data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and the like. The memory 804 can be implemented by any type of volatile or non-volatile storage devices 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.

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

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

[0113] The audio component 810 is configured to output and / or input an audio signal. For example, the audio component 810 includes a microphone (MIC) configured to receive an external audio signal 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 an audio signal.

[0114] The I / O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which can be a keypad, a click wheel, buttons, and the like. The buttons can include, but are not limited to, a home button, a volume button, a start button, and a lock button.

[0115] The sensor component 814 includes one or more sensors for providing status assessments for various aspects of the electronic device 800. For example, the sensor component 814 can detect an open / closed position of the electronic device 800, relative positioning of components of the electronic device 800, such as a display and a keypad of the electronic device 800, a change in position of the electronic device 800 or a component of the electronic device 800, presence or absence of user contact with the electronic device 800, orientation or acceleration / deceleration / g-force and temperature changes of the electronic device 800. The sensor component 814 can include an optical sensor that is configured to detect ambient light, a proximity sensor configured to detect the presence of nearby objects without any physical touch, or a CMOS or CCD image sensor for use in imaging applications. In some embodiments, the sensor component 814 can further include an acceleration sensor, 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 corresponding communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In an example 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 example embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on Radio Frequency Identification (RFID) techniques, infrared data association (IrDA) techniques, ultra-wideband (UWB) techniques, Bluetooth (BT) techniques, and other techniques.

[0117] In an example embodiment, the electronic device 800 can be implemented using 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, micro-controllers, microprocessors, or other electronic elements to perform the above-described methods.

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

[0119] Figure 3 FIG. 19 is a block diagram of an electronic device 1900 according to an example embodiment. For example, the electronic device 1900 can be provided as a server. Referring to FIG. 19, the electronic device 1900 includes a bus 1901, a processor 1902, a memory 1903, a storage 1904, an input / output (I / O) interface 1905, a display 1906, and a communication interface 1907. Figure 3The electronic device 1900 includes a processing component 1922, which is further composed of one or more processors, and a memory resource represented by the memory 1932 for storing instructions, such as application programs, executable by the processing component 1922. The application programs stored in the memory 1932 can include one or more than one module each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute the instructions to perform the above method.

[0120] The electronic device 1900 can 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 can 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 exemplary embodiments, a non-transitory computer readable storage medium, such as the memory 1932 including computer program instructions, is also provided, which can be executed by the processing component 1922 of the electronic device 1900 to complete the above method.

[0122] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0123] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, 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 mechanically encoded device such as punch-cards or punched tape, a

[0124] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0125] Computer readable program instructions for carrying out operations of the present disclosure can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computing / processing device, partly on the user's computing / processing device, as a stand-alone software package, partly on the user's computing / processing device and partly on a remote computing / processing device or entirely on the remote computing / processing device or server. In the latter scenario, the remote computing / processing device can be connected to the user's computing / processing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing / processing device, for example, through the Internet using an Internet Service Provider. In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0126] The computer readable program instructions can also be loaded onto a computing / processing device, other programmable data processing apparatus, or other device to cause a series of operations to be performed on the computing / processing device, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computing / processing device, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0127] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include a non-transitory computer readable storage medium that can be a computer- readable storage medium having no data storage cycles that change state. The instructions can be executed by one or more processors of a computer, to cause a series of operational steps to be performed on the computer to produce a computer-implemented process. The instructions can also cause one or more processors of a computer or other programmable data processing apparatus to

[0128] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0129] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0130] Embodiments of the present disclosure have been described above, and the description is intended to be illustrative of the embodiments and not restrictive of the disclosure. Many modifications and variations of the described embodiments are possible in light of this disclosure. It is contemplated that the use of the terms "including," "comprising," "incorporating," "consisting of," "consisting essentially of," and the like, are intended to be open-ended. That is, the use of these terms is intended to encompass the presence of one or more elements or steps, but not exclude the presence of other elements or steps. It is also contemplated that the use of the terms "first," "second," "third," and the like, are intended to be descriptive and not to be limiting. That is, the use of these terms is intended to indicate the presence of one or more elements or steps, but not exclude the presence of other elements or steps.

Claims

1. A method for processing abdominal medical images, characterized in that, include: The process involves 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, bladder, and stones corresponding to the dual-energy abdominal training image. Constructing the multi-mask label fusion image representing the kidney, bladder, ureter, and stones includes: acquiring organ mask label images corresponding to at least one organ (kidney, ureter, or bladder) and stone mask label images within those organs from the dual-energy abdominal training image; and generating the multi-mask label fusion image corresponding to the dual-energy abdominal training image based on the organ mask value and the organ spatial position corresponding to the organ mask value, and the stone mask value and the second spatial position corresponding to the stone mask value from the stone mask label image. The preset segmentation network is trained using the dual-energy abdominal training image and its corresponding multi-mask label fusion image to obtain the corresponding stone segmentation model; Based on the stone segmentation model, stones in the kidneys, ureters, and bladder are segmented from dual-energy abdominal images to obtain stone mask images.

2. The method for processing abdominal medical images according to claim 1, characterized in that, Before acquiring the organ mask label image corresponding to at least one organ (kidney, ureter, bladder) and the stone mask label image within the organ in the dual-energy abdominal training image, 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, Using the dual-energy abdominal training image and its corresponding multi-mask label fused image, the preset segmentation network is trained to obtain the corresponding stone segmentation model, including: Obtain the set loss value corresponding to the stone segmentation model; During the training of 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, and the corresponding stone segmentation model is obtained.

4. The method for processing abdominal medical images according to any one of claims 1 or 2, characterized in that, Also includes: Based on the dual-energy abdominal image and / or the stone mask image, extract the corresponding stone information; The stone information is configured as one or more of the following: stone location, stone shape, stone calorific value, the major and minor diameters of the stone, stone area, or stone volume.

5. The method for processing abdominal medical images according to claim 3, characterized in that, Also includes: Based on the dual-energy abdominal image and / or stone mask image, extract the corresponding stone information; The stone information is configured as one or more of the following: stone location, stone shape, stone calorific value, the major and minor diameters of the stone, stone area, or stone volume.

6. The method for processing abdominal medical images according to claim 4, characterized in that, The step of extracting corresponding stone information based on the dual-energy abdominal image and / or the stone mask image includes: Based on the kidney and / or ureter and / or bladder mask images corresponding to the dual-energy abdominal image and the stone mask image, the location of the stone corresponding to the stone information is determined; and / or, Based on the dual-energy abdominal image and its corresponding stone mask image, determine the calorific value corresponding to the stone information; and / or, Based on the stone mask image, determine one or more of the following information: stone shape, major and minor diameters, and stone volume.

7. The method for processing abdominal medical images according to claim 5, characterized in that, The step of extracting corresponding stone information based on the dual-energy abdominal image and / or the stone mask image includes: Based on the kidney and / or ureter and / or bladder mask images corresponding to the dual-energy abdominal image and the stone mask image, the location of the stone corresponding to the stone information is determined; and / or, Based on the dual-energy abdominal image and its corresponding stone mask image, determine the calorific value corresponding to the stone information; and / or, Based on the stone mask image, determine one or more of the following information: stone shape, major and minor diameters, and stone volume.

8. The method for processing abdominal medical images according to claim 4, characterized in that, Also includes: If the dual-energy abdominal image is configured as a three-dimensional dual-energy abdominal CT image, then the stone type corresponding to the stone information is identified based on the atomic number image of the urinary tract stone corresponding to the dual-energy abdominal image.

9. The method for processing abdominal medical images according to any one of claims 5-7, characterized in that, Also includes: If the dual-energy abdominal image is configured as a three-dimensional dual-energy abdominal CT image, then the stone type corresponding to the stone information is identified based on the atomic number image of the urinary tract stone corresponding to the dual-energy abdominal image.

10. A system for processing abdominal medical images, characterized in that, include: An acquisition unit is configured to acquire 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, bladder, and stones corresponding to the dual-energy abdominal training image; wherein, constructing the multi-mask label fusion image representing the kidney, bladder, ureter, and stones includes: acquiring 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; and generating the multi-mask label fusion image corresponding to the dual-energy abdominal training image based on the organ mask value and the organ spatial position corresponding to the organ mask value, and the stone mask value and the second spatial position corresponding to the stone mask value of the stone mask label image. The training unit is used to train the preset segmentation network using the dual-energy abdominal training image and its corresponding multi-mask label fusion image to obtain the corresponding stone segmentation model. The processing unit is used to segment 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.

11. 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 processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the method for processing abdominal medical images according to any one of claims 1 to 9.

12. A system for processing abdominal medical images, characterized in that, include: processor; A memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the method for processing abdominal medical images according to any one of claims 1 to 9.

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

14. A system for processing abdominal medical images, characterized in that, include: A computer program product, comprising a computer program / instructions that, when executed by a processor, implement the method for processing abdominal medical images as described in any one of claims 1 to 9.

Images