Fallopian tube patency analysis method and system, electronic equipment and storage medium

Abstract

The invention provides a fallopian tube patency analysis method and system, electronic equipment and a storage medium, and relates to the technical field of medical image analysis, and the method comprises the steps: obtaining a fallopian tube angiography image, obtaining a fallopian tube patency analysis model, sequentially carrying out the denoising, contrast enhancement and target region extraction processing of the fallopian tube angiography image, and obtaining a fallopian tube patency analysis model. The target area comprises a fallopian tube area and a contrast agent area, inputting the target area into the fallopian tube patency analysis model, and generating a patency result for representing the fallopian tube patency according to the fallopian tube patency analysis model. And a patency result is output through the fallopian tube patency analysis model, and the method has the advantages of good stability and high accuracy of the analysis result.

Description

Background Art

[0002] Hysterosalpingography is an important method for evaluating the patency of the fallopian tubes in women and is of great significance for diagnosing diseases such as infertility and fallopian tube obstruction.

[0003] In the related art, the analysis of the patency of the fallopian tubes is mainly carried out by doctors observing the contrast images and judging the patency according to the doctors' experience. The analysis results of this method have the problems of strong subjectivity and insufficient accuracy.

[0004] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The present application provides a method, a system, an electronic device and a storage medium for analyzing the patency of the fallopian tubes, which at least overcome the problems of strong subjectivity and insufficient accuracy in the analysis results in the related art to a certain extent.

[0006] Other features and advantages of the present application will become apparent through the following detailed description, or will be learned in part through the practice of the present application.

[0007] According to one aspect of the present application, there is provided a method for analyzing the patency of the fallopian tubes, including: obtaining a hysterosalpingography image; obtaining a fallopian tube patency analysis model; performing denoising, contrast enhancement, and target region extraction processing on the hysterosalpingography image in sequence, where the target region includes the fallopian tube region and the contrast agent region; inputting the target region into the fallopian tube patency analysis model, and generating a patency result for representing the patency of the fallopian tubes according to the fallopian tube patency analysis model.

[0008] In some embodiments, the obtaining of the fallopian tube patency analysis model includes: obtaining a sample of hysterosalpingography images; obtaining an initial fallopian tube patency analysis model, where the initial fallopian tube patency analysis model is any neural network model for classification; performing denoising, contrast enhancement, and target region extraction processing on the sample of hysterosalpingography images in sequence to obtain a target region sample; calculating fallopian tube parameters according to the target region sample, and judging the patency grade of the fallopian tubes according to the fallopian tube parameters; training the initial fallopian tube patency analysis model with the sample of hysterosalpingography images, the fallopian tube parameters, and the patency grade to obtain the fallopian tube patency analysis model.

[0009] In some embodiments, the processing of denoising, enhancing contrast, and extracting the target region from the fallopian tube angiography image sequentially includes: denoising the fallopian tube angiography image using a Gaussian filtering algorithm, where the size of the filtering kernel used in the Gaussian filtering algorithm is 5×5 and the standard deviation is 1.0; enhancing the contrast of the fallopian tube angiography image; and using a threshold segmentation method to segment the target region from the fallopian tube angiography image, and the threshold segmentation method adopts one of a global threshold, a local average gray value, and a Gaussian weighted average gray value.

[0010] In some embodiments, enhancing the contrast of the fallopian tube angiography image includes: calculating the probability of each gray level appearing in the fallopian tube angiography image according to the formula where n i is the number of occurrences of each gray level i, and N is the total number of pixels; calculating the cumulative probability of the gray levels in the fallopian tube angiography image according to the cumulative probability function, and the cumulative probability function is where c(i) is the cumulative probability and p(j) is the probability of the gray level j; mapping the original gray value to a new gray value, and the new gray value is y i = round[(L - 1)×c(i)], where L is the total number of newly set gray levels in the fallopian tube angiography image, and the round function is a function that rounds to the nearest integer; replacing all gray levels i of each pixel in the fallopian tube angiography image with the corresponding y i to obtain a fallopian tube angiography image with enhanced contrast.

[0011] In some embodiments, the threshold segmentation method adopts a global threshold, and the global threshold is 150.

[0012] In some embodiments, the Adam (Adaptive Moment Estimation) optimizer is used as the update strategy for the initial fallopian tube patency analysis model. The initial learning rate of the Adam optimizer is 0.001, the single training sample size for the initial fallopian tube patency analysis model is 32, and the number of iterations for training the initial fallopian tube patency analysis model is 100 times.

[0013] According to another aspect of the present application, there is also provided a fallopian tube patency analysis system, which is applied to a fallopian tube patency analysis device and includes: a first acquisition module for acquiring a fallopian tube angiography image; a second acquisition module for acquiring a fallopian tube patency analysis model; a preprocessing module for sequentially performing denoising, enhancing contrast, and extracting the target region on the fallopian tube angiography image; and a generation module for inputting the target region into the fallopian tube patency analysis model and generating a patency result representing the fallopian tube patency according to the fallopian tube patency analysis model.

[0014] According to another aspect of the present application, an electronic device is further provided. The electronic device includes: a processor; and a memory for storing executable instructions of the processor; wherein, the processor is configured to execute the fallopian tube patency analysis method described in any one of the above by executing the executable instructions.

[0015] According to yet another aspect of the present application, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the fallopian tube patency analysis method described in any one of the above is implemented.

[0016] According to yet another aspect of the present application, a computer program product is further provided, including a computer program. When the computer program is executed by a processor, the fallopian tube patency analysis method described in any one of the above is implemented.

[0017] The technical solutions provided in the embodiments of the present application at least include the following beneficial effects:

[0018] For the technical solutions provided in the embodiments of the present application, by outputting the patency result through the fallopian tube patency analysis model, the problem that in the related art, the analysis of the fallopian tube patency mainly relies on doctors observing the angiography images and judging the patency according to the doctors' experience, resulting in strong subjectivity and insufficient accuracy of the analysis results, is solved. It has the advantages of good stability and high accuracy of the analysis results.

[0019] Further, by sequentially performing denoising, enhancing contrast, and extracting the target area on the fallopian tube angiography image, a clear and accurate target area can be extracted, thereby improving the accuracy of the analysis result. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings here are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 Showing the flowchart of the fallopian tube patency analysis method in an embodiment of the present application;

[0022] Figure 2 Showing the flowchart of the method for obtaining the fallopian tube patency analysis model in an embodiment of the present application;

[0023] Figure 3 Showing the schematic diagram of the structure of the fallopian tube patency analysis system in an embodiment of the present application;

[0024] Figure 4 A structural block diagram of an electronic device in an embodiment of the present application is shown. Detailed implementation manners

[0025] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present application and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0027] It should be understood that the various steps recited in the method embodiments of the present application can be executed in a different order and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the steps shown. The scope of the present application is not limited in this regard.

[0028] It should be noted that the concepts such as "first" and "second" mentioned in the present application are only used to distinguish different devices, modules, or units, and are not used to limit the order of the functions executed by these devices, modules, or units or their interdependent relationships.

[0029] The following will, with reference to the accompanying drawings, detail the specific implementation manners of the embodiments of the present application.

[0030] Figure 1 A flowchart of a fallopian tube patency analysis method in an embodiment of the present application is shown. As Figure 1 shown, the method includes the following S110 to S140.

[0031] S110, obtain a hysterosalpingography image.

[0032] In one embodiment, a hysterosalpingography image is obtained from a hysterosalpingography device. The embodiments of the present application do not limit the hysterosalpingography device. For example, the hysterosalpingography device can be an ultrasound device or a magnetic resonance device. Among them, the ultrasound device has high resolution and high frame rate, and the magnetic resonance device uses a T1-weighted imaging or contrast-enhanced scanning imaging sequence. The method of obtaining the hysterosalpingography image can be obtained through the network. The embodiments of the present application do not limit the resolution and gray level of the hysterosalpingography image. For example, the resolution is 512×512 pixels and the gray level is 256 levels.

[0033] S120, obtain a fallopian tube patency analysis model.

[0034] Figure 2 The flowchart of the method for obtaining a fallopian tube patency analysis model in an embodiment of the present application is shown. As Figure 2 shown, in one embodiment, the method for obtaining a fallopian tube patency analysis model includes the following S121 to S125.

[0035] S121, obtain a hysterosalpingography image sample.

[0036] In one embodiment, the hysterosalpingography image sample is a hysterosalpingography image sample of a historical clinical case. The embodiments of the present application do not limit the method of obtaining the hysterosalpingography image sample. For example, it can be obtained from the historical clinical cases stored in the hysterosalpingography device through the network or directly obtained from a storage device that has stored hysterosalpingography image samples. The embodiments of the present application do not limit how many hysterosalpingography images the hysterosalpingography image sample specifically includes. For example, the number of hysterosalpingography image samples is not less than 10,000.

[0037] S122, obtain an initial fallopian tube patency analysis model, and the initial fallopian tube patency analysis model is any neural network model for classification.

[0038] In one embodiment, the embodiments of the present application do not limit which specific model the neural network model for classification is. For example, the neural network model for classification is a convolutional neural network model (CNN, Convolutional Neural Network), a multi-layer perceptron (MLP, Multi-Layer Perceptron), or a recurrent neural network (RNN, Recurrent Neural Network). The embodiments of the present application do not limit the method of obtaining the initial fallopian tube patency analysis model. For example, the initial fallopian tube patency analysis model can be obtained through the network or directly constructed.

[0039] S123. Perform denoising, contrast enhancement, and target region extraction on the fallopian tube angiography image samples in sequence to obtain target region samples.

[0040] In one embodiment, performing denoising, contrast enhancement, and target region extraction on the fallopian tube angiography image samples includes the following steps S1231 to S1233.

[0041] S1231. Denoise the fallopian tube angiography image samples.

[0042] In one embodiment, the method for denoising the fallopian tube angiography image samples is not limited in the embodiments of the present application. For example, use the Gaussian filtering algorithm to denoise the fallopian tube angiography image samples, where the size of the filter kernel used in the Gaussian filtering algorithm is 5×5 and the standard deviation is 1.0; or use one of Fourier transform, wavelet transform, non-local means filtering (NLM), and morphological filtering to denoise the fallopian tube angiography image samples. Denoising the fallopian tube angiography image samples can effectively reduce the granular noise in the image.

[0043] S1232. Enhance the contrast of the fallopian tube angiography image samples.

[0044] In one embodiment, enhancing the contrast of the fallopian tube angiography image samples includes the following steps S12321 to S12324.

[0045] S12321. Let n i be the number of occurrences of each gray level i, N be the total number of pixels, and L be the total number of gray levels in the fallopian tube angiography image samples. Regarding the specific value of the total number of gray levels, the embodiments of the present application are not limited. For example, for an 8-bit image, that is, each pixel value ranges from 0 to 255, then L = 256 gray levels. The probability of occurrence of each gray level i is where i = 0, 1,..., L - 1.

[0046] S12322. Calculate the cumulative probability of the gray levels in the fallopian tube angiography image samples according to the cumulative probability function, and the cumulative probability function is where c(i) is the cumulative probability and p(j) is the probability of gray level j.

[0047] S12323. Map the original gray value to a new gray value, and the new gray value is y i = round[(L - 1)×c(i)], where L is the newly set total number of gray levels in the fallopian tube angiography image, and the round function is a function that rounds to the nearest integer and retains the integer.

[0048] S12324. Replace all gray levels i of each pixel in the fallopian tube angiography image samples with the corresponding y i, an image sample of hysterosalpingography with enhanced contrast is obtained.

[0049] Enhancing the contrast of the image sample of hysterosalpingography can make the fallopian tubes and the contrast agent area in the image sample of hysterosalpingography clearer.

[0050] S1233, using a threshold segmentation method to segment the target region sample from the image sample of hysterosalpingography, and obtaining the target region sample.

[0051] In one embodiment, a global threshold is used to segment the target region sample from the image sample of hysterosalpingography. Specifically, for each pixel point p, if its gray value I(p) is greater than or equal to the global threshold T, it is set as the foreground (white), otherwise it is set as the background (black), and the formula is expressed as: where O(p) is the corresponding pixel value in the segmented image.

[0052] In one embodiment, the global threshold is determined according to the gray characteristics of the contrast agent. When the gray level range is 0-255, the global threshold is 150. That is, pixels with gray values greater than 150 are regarded as the contrast agent area.

[0053] In another embodiment, the local average gray value is used to segment the target region sample from the image sample of hysterosalpingography. Specifically, calculate the average gray value within the rectangular area around each pixel, and use the local average gray value as the threshold for this pixel. The size of the rectangular area is not limited in the embodiments of the present application. For example, the size of the rectangular area can be 3×3, 5×5, 7×7.

[0054] In another embodiment, the target region sample is segmented from the image sample of hysterosalpingography by using the Gaussian weighted average gray value. Specifically, the weight of the pixel is defined by Gaussian filtering, so that the pixels around the central pixel have a greater influence on the threshold. The formula is: T(x,y) = m(x,y) + C, where m(x,y) is the local average gray value near the point (x,y), C is a constant offset, and T(x,y) is the Gaussian weighted average gray value.

[0055] S124, calculating the fallopian tube parameters according to the target region sample, and judging the patency grade of the fallopian tubes according to the fallopian tube parameters.

[0056] In one embodiment, the present application does not limit what the fallopian tube parameters include. For example, the fallopian tube parameters are the fallopian tube filling ratio, filling speed, and diameter change. Among them, the filling ratio is the ratio of the length of the fallopian tube filled with the contrast agent to the total length of the fallopian tube, the filling speed is the time interval from the contrast agent entering the fallopian tube to filling a specific area (such as from the isthmus to the ampulla), and the diameter change range of different parts of the fallopian tube (such as the interstitial part, isthmus, ampulla).

[0057] In one embodiment, the present application places no restrictions on the patency levels included. For example, the patency levels can be patent, moderately patent, slightly patent, extremely slightly patent, and blocked.

[0058] In one embodiment, the present application places no restrictions on the method for determining the patency level of the fallopian tube based on fallopian tube parameters. For example, the clinical gold standard for grading fallopian tube patency is used to determine the patency level of the fallopian tube. If the total length of the fallopian tube is 10 cm and the filling length of the contrast agent is more than 8 cm, the filling ratio reaches more than 80%, and the patency level is determined to be patent.

[0059] S125. Use the fallopian tube imaging samples, fallopian tube parameters, and patency levels to train the initial fallopian tube patency analysis model to obtain the fallopian tube patency analysis model.

[0060] In one embodiment, the present application places no restrictions on the specific parameters for training. For example, ResNet-50 is used as the basic network structure, and the adaptive moment estimation optimizer is used as the update strategy for the initial fallopian tube patency analysis model. The initial learning rate of the adaptive moment estimation optimizer is 0.001, the single training sample size for the initial fallopian tube patency analysis model is 32, and the number of iterations for training the initial fallopian tube patency analysis model is 100 times.

[0061] By training the initial fallopian tube patency analysis model using samples with known fallopian tube parameters and patency levels, the professionalism of the fallopian tube patency analysis model for fallopian tube patency analysis is improved, and the accuracy of the analysis results is enhanced.

[0062] S130. Perform denoising, contrast enhancement, and target area extraction processing on the fallopian tube imaging in sequence. The target area includes the fallopian tube area and the contrast agent area.

[0063] In one embodiment, the method for performing denoising, contrast enhancement, and target area extraction processing on the fallopian tube imaging in sequence is the same as the method in S1231 to S1233. Specifically, replace "fallopian tube imaging sample" with "fallopian tube imaging" and "target area sample" with "target area" in S1231 to S1233. The detailed steps are not elaborated here.

[0064] S140. Input the target area into the fallopian tube patency analysis model, and generate a patency result representing the fallopian tube patency according to the fallopian tube patency analysis model.

[0065] In one embodiment, input the target area into the fallopian tube patency analysis model through the network. The patency result includes fallopian tube parameters and patency levels.

[0066] Based on the same inventive concept, an embodiment of the present application also provides a fallopian tube patency analysis system, as described in the following embodiments. Since the principle of problem-solving of this system embodiment is similar to that of the above method embodiment, the implementation of this system embodiment can refer to the implementation of the above method embodiment, and repeated parts will not be elaborated.

[0067] Figure 3 A schematic diagram showing the structure of the fallopian tube patency analysis system in an embodiment of the present application, as Figure 3 shown in the figure, the system includes a first acquisition module 31 for acquiring a fallopian tube angiography image; a second acquisition module 32 for acquiring a fallopian tube patency analysis model; a preprocessing module 33 for successively performing denoising, contrast enhancement, and target region extraction processing on the fallopian tube angiography image; and a generation module 34 for inputting the target region into the fallopian tube patency analysis model and generating a patency result representing the fallopian tube patency according to the fallopian tube patency analysis model.

[0068] In one embodiment, the first acquisition module 31 acquires the fallopian tube angiography image from a fallopian tube angiography device through a network and saves the acquired fallopian tube angiography image locally in the first acquisition module 31; the second acquisition module 32 acquires it from a server carrying the fallopian tube patency analysis model through a network; the preprocessing module 33 acquires the fallopian tube angiography image from the first acquisition module 31 through a network and saves it locally, and performs denoising, contrast enhancement, and target region extraction processing on the fallopian tube angiography image; the generation module 34 can acquire the target region from the preprocessing module 33 through a network and acquire the fallopian tube patency analysis model from the second acquisition module 32 through a network, input the target region into the fallopian tube patency analysis model, and generate a patency result representing the fallopian tube patency.

[0069] In one embodiment, the second acquisition module 32 is further configured to acquire a fallopian tube angiography image sample of a historical clinical case from a fallopian tube angiography device through a network, or acquire a fallopian tube angiography image sample from a storage device storing fallopian tube angiography image samples through a network, and save the acquired fallopian tube angiography image sample locally in the second acquisition module 32; the second acquisition module 32 is further configured to acquire an initial fallopian tube patency analysis model; the second acquisition module 32 is further configured to successively perform denoising, contrast enhancement, and target region extraction processing on the fallopian tube angiography image sample; the second acquisition module 32 is further configured to calculate fallopian tube parameters according to the target region sample and determine the patency grade of the fallopian tube according to the fallopian tube parameters; the second acquisition module 32 is further configured to train the initial fallopian tube patency analysis model using the fallopian tube angiography image sample, fallopian tube parameters, and patency grade to obtain the fallopian tube patency analysis model.

[0070] The above network can be a wired network or a wireless network. Optionally, the above wireless network or wired network uses standard communication technologies and / or protocols. The network is usually the Internet, but can also be any network, including but not limited to any combination of a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network or a virtual private network). In some embodiments, technologies and / or formats including HyperText Mark-up Language (HTML), Extensible Markup Language (XML), etc. are used to represent data exchanged through the network. In addition, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet Protocol Security (IPSec), etc. can be used to encrypt all or some of the links. In other embodiments, customized and / or proprietary data communication technologies can also be used to replace or supplement the above data communication technologies.

[0071] It should be noted here that the above first acquisition module 31, second acquisition module 32, preprocessing module 33, and generation module 34 correspond to S110 to S140 in the method embodiment. The examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content claimed in the above method embodiment. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0072] Those skilled in the art can understand that various aspects of the present application can be implemented as a method, a system, an electronic device, and a storage medium. Therefore, various aspects of the present application can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "system" here.

[0073] Next, refer to Figure 4 to describe the electronic device 400 according to this embodiment of the present application. Figure 4 The displayed electronic device 400 is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0074] AsFigure 4 As shown, the electronic device 400 is presented in the form of a general-purpose computing device. The components of the electronic device 400 may include, but are not limited to: at least one of the above-mentioned processing units 410, at least one of the above-mentioned storage units 420, and a bus 430 connecting different system components (including the storage unit 420 and the processing unit 410).

[0075] Among them, the storage unit stores program code, and the program code can be executed by the processing unit 410, so that the processing unit 410 executes the steps according to various exemplary embodiments of the present application described in the "Exemplary Method" section of this specification above. For example, the processing unit 410 may execute steps S110 to S140 of the above method embodiment.

[0076] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 4201 and / or a cache storage unit 4202, and may further include a read-only storage unit (ROM) 4203.

[0077] The storage unit 420 may also include a program / utilities 4204 having a set (at least one) of program modules 4205. Such program modules 4205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment.

[0078] The bus 430 may represent one or more of several types of bus structures, including a storage unit bus or a storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any bus structure in a variety of bus structures.

[0079] The electronic device 400 may also communicate with one or more external devices 440 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be performed through an input / output (I / O) interface 450. And, the electronic device 400 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 460. As shown in the figure, the network adapter 460 communicates with other modules of the electronic device 400 through the bus 430. It should be understood that although not shown in the figure, other hardware and / or software modules may be used in combination with the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0080] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0081] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer program product, which includes: a computer program that, when executed by a processor, implements the above-mentioned fallopian tube patency analysis method.

[0082] In an exemplary embodiment of the present application, there is also provided a computer-readable storage medium, which can be a readable signal medium or a readable storage medium. A program product capable of implementing the above method of the present application is stored on the computer-readable storage medium.

[0083] In some possible implementation manners, various aspects of the present application can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0084] More specific examples of the computer-readable storage medium in the present application may include, but are not limited to: an electrical connection having one or more wires, 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), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0085] In the present application, the computer-readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium can also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program used by or in combination with an instruction execution system, apparatus, or device.

[0086] Optionally, the program code contained on the computer-readable storage medium can be transmitted with any suitable medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the above.

[0087] In specific implementation, the program code for performing the operations of this application can be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as an independent software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0088] It should be noted that although several modules or units of the system for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more of the above-described modules or units can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0089] In addition, although the steps of the method in this application are described in a specific order in the drawings, this does not require or imply that these steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution, etc.

[0090] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described here can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.) or on the network, including several instructions to enable a computing device (which can be a personal computer, server, mobile terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0091] Other embodiments of the present application will be readily contemplated by those skilled in the art after considering the specification and practicing the invention herein. This application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common general knowledge or conventional technical means in the technical field not claimed in the present application. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present application are pointed out by the appended claims.

Claims

1. A method for analyzing fallopian tube patency, characterized in that, Comprising: Obtaining a hysterosalpingography image; Obtaining a fallopian tube patency analysis model; Successively performing denoising, contrast enhancement, and target region extraction processing on the hysterosalpingography image, where the target region includes the fallopian tube region and the contrast agent region; Inputting the target region into the fallopian tube patency analysis model, and generating a patency result representing the fallopian tube patency according to the fallopian tube patency analysis model.

2. The method according to claim 1, wherein The obtaining of the fallopian tube patency analysis model includes: Obtaining a sample of hysterosalpingography images; Obtaining an initial fallopian tube patency analysis model, where the initial fallopian tube patency analysis model is any neural network model for classification; Successively performing denoising, contrast enhancement, and target region extraction processing on the sample of hysterosalpingography images to obtain a target region sample; Calculating fallopian tube parameters according to the target region sample, and judging the patency grade of the fallopian tubes according to the fallopian tube parameters; Training the initial fallopian tube patency analysis model using the sample of hysterosalpingography images, the fallopian tube parameters, and the patency grade to obtain the fallopian tube patency analysis model.

3. The method according to claim 1, wherein The successively performing denoising, contrast enhancement, and target region extraction processing on the hysterosalpingography image includes: Denoising the hysterosalpingography image using a Gaussian filtering algorithm, where the filter kernel size used in the Gaussian filtering algorithm is 5×5 and the standard deviation is 1.0; Enhancing the contrast of the hysterosalpingography image; Segmenting the target region from the hysterosalpingography image using a threshold segmentation method, where the threshold segmentation method uses one of a global threshold, a local average gray value, and a Gaussian weighted average gray value.

4. The method according to claim 3, wherein The enhancing the contrast of the hysterosalpingography image includes: According to the formula calculate the probability of occurrence of each gray level in the hysterosalpingography image, where n i is the number of occurrences of each gray level i, and N is the total number of pixels; Calculate the cumulative probability of the gray levels in the hysterosalpingography image according to the cumulative probability function, where the cumulative probability function is where c(i) is the cumulative probability and p(j) is the probability of gray level j; Map the original grayscale value to a new grayscale value, where the new grayscale value is y i = round[(L - 1)×c(i)], where L is the total number of newly set gray levels in the hysterosalpingography image, and the round function is a function that rounds to the nearest integer; Replace all gray levels \(i\) of each pixel in the hysterosalpingography image with the corresponding \(y\). i to obtain a hysterosalpingography image with enhanced contrast.

5. The method according to claim 3, wherein The threshold segmentation method uses a global threshold, and the global threshold is 150.

6. The method according to claim 2, characterized in that, Using an adaptive moment estimation Adam optimizer as the update strategy of the initial fallopian tube patency analysis model, where the initial learning rate of the Adam optimizer is 0.001, the single training sample size for the initial fallopian tube patency analysis model is 32, and the number of iterations for training the initial fallopian tube patency analysis model is 100 times.

7. A fallopian tube patency analysis system, characterized in that, Comprising: A first obtaining module for obtaining a hysterosalpingography image; A second obtaining module for obtaining a fallopian tube patency analysis model; A preprocessing module for successively performing denoising, contrast enhancement, and target region extraction processing on the hysterosalpingography image; A generating module for inputting the target region into the fallopian tube patency analysis model and generating a patency result representing the fallopian tube patency according to the fallopian tube patency analysis model.

8. An electronic device, characterized in that, Comprising: A processor; And, A memory for storing executable instructions of the processor; Wherein, the processor is configured to execute the fallopian tube patency analysis method according to any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the fallopian tube patency analysis method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program which, when executed by a processor, implements the fallopian tube patency analysis method according to any one of claims 1 to 6.

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