Pulmonary airway nodule identification method and apparatus, device, and computer-readable storage medium

Abstract

The present application relates to the technical field of image processing. Disclosed are a pulmonary airway nodule identification method and apparatus, a device, and a computer-readable storage medium. The method comprises: acquiring a pulmonary airway medical image, and extracting a pulmonary airway contour image in the pulmonary airway medical image; on the basis of coordinates corresponding to pixel points in the pulmonary airway contour image, determining a target variation magnitude of a target field corresponding to the pulmonary airway contour image, wherein the target field is a direction field or a gradient field; and comparing the target variation magnitude with a preset first variation magnitude threshold to obtain a comparison result, and identifying a pulmonary airway nodule in the pulmonary airway contour image on the basis of the comparison result. By means of determining the target variation magnitude of the target field corresponding to the pulmonary airway contour image, and using the target variation magnitude of the target field on the surface of an airway contour, the position of a protrusion on a pulmonary airway in the medical image is determined, and thus the position of a small nodule is identified, thereby improving the accuracy of identifying an early-stage pulmonary airway nodule.

Description

Methods, apparatus, devices and computer-readable storage media for identifying pulmonary airway nodules

[0001] This application claims priority to Chinese Patent Application No. 202410960174.6, filed on July 17, 2024, entitled “Method, Apparatus, Device and Computer-Readable Storage Medium for Identifying Pulmonary Airway Nodules”, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of image processing technology, specifically to a method, apparatus, device, and computer-readable storage medium for identifying pulmonary airway nodules. Background Technology

[0003] The lungs and airways are vital organs responsible for respiration. Influenced by the environment, nodules can form on the airway walls. These nodules can grow rapidly, and once they reach a certain size, they can impair breathing and even pose a risk of death. Currently, during lung and airway examinations, doctors typically analyze medical images of the airways to determine the presence of nodules. However, because early-stage nodules are very small and difficult to detect with the naked eye, they are easily overlooked, leading to missed diagnoses.

[0004] Therefore, improving the accuracy of early identification of pulmonary airway nodules is an urgent problem to be solved. Summary of the Invention

[0005] This application provides a method, apparatus, device, and computer-readable storage medium for identifying pulmonary airway nodules, which can improve the accuracy of early identification of pulmonary airway nodules.

[0006] In a first aspect, embodiments of this application provide a method for identifying pulmonary airway nodules, the method comprising:

[0007] Acquire medical images of the lung airways and extract the lung airway contour images from the medical images of the lung airways;

[0008] Based on the coordinates of the pixels in the lung airway contour image, the target change of the target field corresponding to the lung airway contour image is determined, where the target field is a direction field or a gradient field.

[0009] The target change amount is compared with a preset first change amount threshold to obtain a comparison result, and lung airway nodules in the lung airway contour image are identified based on the comparison result.

[0010] Secondly, embodiments of this application provide a lung airway nodule identification device, the device comprising:

[0011] An acquisition unit is used to acquire medical images of the lung airways and extract lung airway contour images from the medical images of the lung airways;

[0012] The determining unit is used to determine the target change amount of the target field corresponding to the lung airway contour image based on the coordinates of the pixels in the lung airway contour image, wherein the target field is a direction field or a gradient field.

[0013] The identification unit is used to compare the target change amount with a preset first change amount threshold, obtain a comparison result, and identify lung airway nodules in the lung airway contour image based on the comparison result.

[0014] Thirdly, embodiments of this application also provide a lung airway nodule identification device, including a memory storing a computer program; a processor loads the computer program from the memory to execute the steps of any lung airway nodule identification method provided in embodiments of this application.

[0015] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program adapted for loading by a processor to execute the steps of any of the lung airway nodule identification methods provided in embodiments of this application.

[0016] Fifthly, embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the steps in any of the lung airway nodule identification methods provided in embodiments of this application.

[0017] The method described in the application embodiment acquires a medical image of the lung airway and extracts the lung airway contour image from the medical image. Based on the coordinates of the pixels in the lung airway contour image, the target change amount of the target field corresponding to the lung airway contour image is determined. The target field is a direction field or a gradient field. The target change amount is compared with a preset first change amount threshold to obtain a comparison result, and lung airway nodules in the lung airway contour image are identified based on the comparison result. By determining the target change amount of the target field corresponding to the lung airway contour image, lung airway nodules in the lung airway contour image are identified. Since the target change amount of the target field on the airway contour surface is used to determine the location of protrusions on the lung airway in the medical image, the location of small nodules is identified, thereby improving the accuracy of early lung airway nodule identification. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 is a schematic flowchart of the first embodiment of the lung airway nodule identification method provided in this application;

[0020] Figure 2 is a flowchart of a second embodiment of the lung airway nodule identification method provided in this application.

[0021] Figure 3 is a schematic flowchart of the third embodiment of the lung airway nodule identification method provided in this application;

[0022] Figure 4 is a schematic flowchart of the fourth embodiment of the lung airway nodule identification method provided in this application;

[0023] Figure 5 is a schematic diagram of the structure of the lung airway nodule identification device provided in the embodiment of this application;

[0024] Figure 6 is a schematic diagram of the structure of the lung airway nodule identification device provided in the embodiments of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. At the same time, in the description of the embodiments of this application, the terms "first," "second," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0026] This application provides a method, apparatus, device, and computer-readable storage medium for identifying pulmonary airway nodules.

[0027] Specifically, this embodiment will be described from the perspective of a lung airway nodule identification device, which can be integrated into a lung airway nodule identification apparatus. That is, the lung airway nodule identification method of this application embodiment can be executed by the lung airway nodule identification apparatus.

[0028] The lung airway nodule identification method provided in this application embodiment can be applied to lung airway nodule identification devices, which can be smart terminals, PC terminals, mobile terminals, etc.

[0029] The following detailed description, in conjunction with the accompanying drawings, illustrates the process. This embodiment uses a lung airway nodule identification device as an example. It should be noted that the order of description in the following embodiments is not intended to limit the preferred order of the embodiments. Although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be performed in a different order than that shown in the accompanying drawings.

[0030] Please refer to Figure 1 for a first embodiment of the lung airway nodule identification method. The first embodiment includes the following steps:

[0031] Step 101: Obtain a medical image of the lung airway and extract the lung airway contour image from the medical image of the lung airway.

[0032] Step 102: Based on the coordinates of the pixels in the lung airway contour image, determine the target change amount of the target field corresponding to the lung airway contour image, wherein the target field is a direction field or a gradient field.

[0033] Step 103: Compare the target change amount with a preset first change amount threshold to obtain a comparison result, and identify lung airway nodules in the lung airway contour image based on the comparison result.

[0034] In this embodiment, when it is necessary to identify pulmonary airway nodules, the relevant user inputs a pulmonary airway medical image into the pulmonary airway nodule recognition device. After acquiring the pulmonary airway medical image, the pulmonary airway nodule recognition device extracts the pulmonary airway contour image from the pulmonary airway medical image, creates a coordinate system based on the pulmonary airway contour image, and determines the coordinates of each pixel on the pulmonary airway contour image based on the coordinate system. Based on the coordinates corresponding to each pixel in the pulmonary airway contour image, the pulmonary airway nodule recognition device determines the target field corresponding to each pixel, and then determines the target change amount of the target field corresponding to the pulmonary airway contour image based on the target fields corresponding to all pixels. The target field is a direction field or a gradient field. The pulmonary airway nodule recognition device compares the target change amount with a preset first change amount threshold to obtain a comparison result, and identifies pulmonary airway nodules in the pulmonary airway contour image based on the comparison result.

[0035] It should be noted that both the lung airway medical image and the lung airway contour image are three-dimensional medical images, and the coordinate system created based on the lung airway contour image is a three-dimensional coordinate system.

[0036] The lung airway nodule recognition device of this embodiment acquires medical images of the lung airways and extracts lung airway contour images from these images. Based on the coordinates of the pixels in the lung airway contour image, it determines the target change amount of the target field corresponding to the lung airway contour image. The target field is a direction field or gradient field. The target change amount is compared with a preset first change amount threshold to obtain a comparison result, and lung airway nodules in the lung airway contour image are identified based on the comparison result. By determining the target change amount of the target field corresponding to the lung airway contour image, and using the target change amount of the target field on the airway contour surface, the device determines the location of protrusions on the lung airways in the medical image, thereby identifying the location of small nodules and improving the accuracy of early lung airway nodule recognition.

[0037] Specifically, the following provides a detailed explanation of each step:

[0038] Step 101: Obtain a medical image of the lung airway and extract the lung airway contour image from the medical image of the lung airway.

[0039] In this step, the pulmonary airway nodule recognition device has an interactive module for user interaction. The user can input the pulmonary airway medical image requiring nodule recognition into the device via this module. The device then acquires the pulmonary airway medical image and extracts the pulmonary airway contour image from it. It is understood that the pulmonary airway contour image only contains the pulmonary airway contour from the pulmonary airway medical image.

[0040] For example, the lung airway nodule recognition device uses scanning methods such as CT, MR, and 4D ultrasound to scan the human body and obtain medical images of the lung airway. Then, it uses a pre-selected segmentation model to segment the physiological tissues in the lung airway medical images to obtain segmented images of the physiological tissues in the lung airway. Finally, it extracts the medical images of the lung airway edge contours from the segmented images of the physiological tissues in the lung airway to obtain lung airway contour images, which serve as the image basis for subsequent lung airway nodule recognition.

[0041] Step 102: Based on the coordinates of the pixels in the lung airway contour image, determine the target change amount of the target field corresponding to the lung airway contour image, wherein the target field is a direction field or a gradient field.

[0042] In this step, after the pulmonary airway nodule recognition device acquires the pulmonary airway contour image, it creates a coordinate system based on the pulmonary airway contour image and determines the coordinates of each pixel on the pulmonary airway contour image based on the coordinate system. Based on the coordinates of each pixel in the pulmonary airway contour image, the pulmonary airway nodule recognition device determines the target vector corresponding to each pixel, and then determines the target change of the target field corresponding to the pulmonary airway contour image based on the target vectors corresponding to all pixels. The target field is a direction field or a gradient field.

[0043] In an exemplary embodiment, the lung airway nodule recognition device includes a calculation model for calculating the target change of the target field. After acquiring a lung airway contour image, the lung airway nodule recognition device inputs the lung airway contour image into the calculation model. The calculation model creates a coordinate system based on the lung airway contour image and determines the coordinates of each pixel on the lung airway contour image based on the coordinate system. Based on the coordinates of each pixel in the lung airway contour image, the calculation model determines the target vector corresponding to each pixel, and then determines the target change of the target field corresponding to the lung airway contour image based on the target vectors corresponding to all pixels.

[0044] It's important to note that a direction field describes the direction of a vector at each point, but not necessarily its magnitude. In a direction field, vectors can have different lengths, but at any given point, all vectors point in the same direction. A gradient field, on the other hand, is a special type of vector field where the magnitude and direction of a vector are determined by the gradient of a scalar function. In other words, a gradient field describes the rate of change and direction of a scalar field at each point.

[0045] Step 103: Compare the target change amount with a preset first change amount threshold to obtain a comparison result, and identify lung airway nodules in the lung airway contour image based on the comparison result.

[0046] In this step, the lung airway nodule identification compares the target field change amount corresponding to the lung airway contour image with a preset first change amount threshold to obtain the comparison result, and identifies lung airway nodules in the lung airway contour image based on the comparison result. It can be understood that when the target field change amount corresponding to the lung airway contour image is greater than the preset first change amount threshold, it indicates that there is a sudden change in the target field of the lung airway contour in the lung airway contour image. This sudden change is usually caused by bulges in the lung airway contour, and these bulges are usually lung airway nodules. When the target field change amount corresponding to the lung airway contour image is not greater than the preset first change amount threshold, it indicates that there is no sudden change in the target field of the lung airway contour in the lung airway contour image, that is, there are no lung airway nodules in the lung airway contour.

[0047] The lung airway nodule recognition device of this embodiment acquires medical images of the lung airways and extracts lung airway contour images from these images. Based on the coordinates of the pixels in the lung airway contour image, it determines the target change amount of the target field corresponding to the lung airway contour image. The target field is a direction field or gradient field. The target change amount is compared with a preset first change amount threshold to obtain a comparison result, and lung airway nodules in the lung airway contour image are identified based on the comparison result. By determining the target change amount of the target field corresponding to the lung airway contour image, and using the target change amount of the target field on the airway contour surface, the device determines the location of protrusions on the lung airways in the medical image, thereby identifying the location of small nodules and improving the accuracy of early lung airway nodule recognition.

[0048] Further, referring to Figure 2, a second embodiment of the lung airway nodule identification method is proposed. The difference between the second embodiment and the first embodiment is that the target field is a direction field. The step of determining the target change amount of the target field corresponding to the lung airway contour image based on the coordinates of the pixels in the lung airway contour image includes:

[0049] Step 1021: Obtain the central line of the lung airway in the lung airway contour image, and divide the lung airway contour into multiple circles with each central line point on the central line as the center.

[0050] In this step, the lung airway nodule recognition device acquires the lung airway centerline in the lung airway contour image, and divides the lung airway contour into multiple circles with each centerline point on the lung airway centerline as the center. It can be understood that the lung airway contour can be regarded as a cylindrical structure, and the centerline of the cylindrical structure is composed of multiple centerline point sets. With each centerline point as the center, multiple circles can be drawn on the lung airway contour. The multiple circles stacked together constitute the cylindrical structure of the lung airway.

[0051] In an exemplary embodiment, the lung airway nodule recognition device includes a calculation model for calculating the target change in the orientation field. The calculation model includes a segmentation model, which acquires the lung airway centerline in the lung airway contour image and divides the lung airway contour into multiple circles with each centerline point on the lung airway centerline as the center.

[0052] It is understandable that the lung airways usually have branches. Dividing the lung airway contour into multiple circles with each centerline point on the central line of the lung airway as the center is equivalent to removing the branches of the lung airway. Treating the lung airway as a cylindrical structure without considering its cylindrical structure can avoid misjudging the target field changes at the lung airway branches as target field changes caused by lung airway nodules during subsequent processing, which can help improve the accuracy of lung airway nodule identification.

[0053] Furthermore, the lung airway nodule recognition device numbers the pixels on the corresponding lung airways around the centerline points in sequence. For example, for centerline point 1, the pixels on the corresponding lung airways are numbered 11, 12, 13, etc.; for centerline point 2, the pixels on the corresponding lung airways are numbered 21, 22, 23, etc., and so on, until all pixels on the lung airway outline are sorted and numbered.

[0054] Step 1022: Calculate the direction vector of each pixel on the lung airway contour based on the coordinates of the two nearest adjacent pixels on each pair of adjacent circumferences.

[0055] In this step, the lung airway nodule identification device calculates the direction vector of each pixel on the lung airway contour based on the coordinates of the two nearest adjacent pixels on each pair of adjacent circumferences.

[0056] In an exemplary embodiment, the calculation model for calculating the target change in the orientation field in the lung airway nodule recognition device includes a vector calculation model. The lung airway nodule recognition device obtains the coordinates of the two nearest adjacent pixels on every two adjacent circumferences, and inputs the coordinates of the two adjacent pixels into the vector calculation model to calculate the orientation vector of each pixel on the lung airway contour.

[0057] In an exemplary embodiment, in the above steps, the lung airway nodule recognition device sorts and numbers all the pixels on the lung airway contour. At this time, the lung airway nodule recognition device can determine that the two closest adjacent pixels on each pair of adjacent circumferences can be, for example, two pixels numbered 11 and 21, two pixels numbered 12 and 22, two pixels numbered 21 and 31, etc. Based on the coordinates of pixels numbered 11 and 21, the direction vector of pixel numbered 11 is calculated; based on the coordinates of pixels numbered 12 and 22, the direction vector of pixel numbered 12 is calculated; based on the coordinates of pixels numbered 21 and 31, the direction vector of pixel numbered 21 is calculated, and so on, to determine the direction vector of each pixel on the lung airway contour.

[0058] In an exemplary embodiment, in the above steps, the lung airway nodule recognition device sorts and numbers all the pixels on the lung airway contour, calculates the distance between each pixel and each pixel on the adjacent circumference, and then determines the two nearest adjacent pixels on each pair of adjacent circumferences. Based on the two nearest adjacent pixels on each pair of adjacent circumferences, the device calculates the direction vector of the corresponding pixel, until the direction vector of each pixel on the lung airway contour is determined.

[0059] Step 1023: Based on the direction vector of each pixel and the preset direction vector, determine the target change amount of the direction field corresponding to the lung airway contour image.

[0060] In this step, the lung airway nodule recognition device determines the target change in the orientation field corresponding to the lung airway contour image based on the orientation vector of each pixel and a preset orientation vector. It should be noted that the preset orientation vector is the orientation vector of the pixel when no lung airway nodules are present in the lung airway contour.

[0061] In an exemplary embodiment, the direction vector of each pixel in the lung airway nodule recognition device is compared with a preset direction vector, and a calculation model for calculating the target change of the direction field is input to obtain the target change of the direction field corresponding to the lung airway contour image.

[0062] Specifically, step 1023 includes:

[0063] Step 10231: Calculate the reference change of the direction vector of each pixel based on the direction vector of each pixel and the preset direction vector.

[0064] Step 10232: Accumulate the reference change of the direction vector of each pixel to determine the target change of the direction field corresponding to the lung airway contour image.

[0065] In steps 10231 to 10232, for each pixel on the lung airway contour, the lung airway nodule recognition device calculates the reference change of the direction vector of each pixel based on the direction vector of each pixel and the preset direction vector, and then accumulates the reference change of the direction vector of each pixel to determine the target change of the direction field corresponding to the lung airway contour image.

[0066] In an exemplary embodiment, the lung airway nodule recognition device inputs the direction vector of each pixel and a preset direction vector into a calculation model for calculating the target change of the direction field. The calculation model calculates the reference change of the direction vector of each pixel based on the direction vector of each pixel and the preset direction vector, and then accumulates the reference change of the direction vector of each pixel to determine the target change of the direction field corresponding to the lung airway contour image.

[0067] The lung airway nodule recognition device in this embodiment divides the lung airway contour into multiple circles, using each centerline point on the lung airway centerline as the center. Based on the coordinates of the two nearest adjacent pixels on each pair of adjacent circles, the direction vector of each pixel on the lung airway contour is calculated. Based on the direction vector of each pixel and a preset direction vector, the target change in the direction field corresponding to the lung airway contour image is determined. This avoids misjudging the target change in the target field at lung airway branches as the target change in the target field caused by lung airway nodules during subsequent processing, thus helping to improve the accuracy of lung airway nodule recognition.

[0068] Furthermore, referring to Figure 3, a third embodiment of the lung airway nodule identification method is proposed. The difference between the third embodiment and the first and second embodiments is that the target field is a gradient field. The step of determining the target change amount of the target field corresponding to the lung airway contour image based on the coordinates of the pixels in the lung airway contour image includes:

[0069] Step 1024: Divide the lung airway contour image into regions to obtain lung airway contour sub-images.

[0070] In this step, the lung airway nodule recognition device divides the lung airway contour image into regions based on the smallest nodule diameter to obtain lung airway contour sub-images. It can be understood that the lung airway contour can be regarded as a cylindrical structure, and the cylindrical structure is divided into multiple cylindrical sub-structures with a height equal to the smallest nodule diameter. Each cylindrical sub-structure corresponds to a lung airway contour sub-image.

[0071] In an exemplary embodiment, the lung airway nodule recognition device includes a calculation model for calculating the target change of the gradient field. The calculation model includes a partitioning model, which acquires a lung airway contour image and divides the lung airway contour image into regions based on the minimum nodule diameter to obtain a lung airway contour sub-image.

[0072] Step 1025: For each of the lung airway contour sub-images, determine the gradient field reference change amount corresponding to the lung airway contour sub-image based on the coordinates corresponding to each pixel in the lung airway contour sub-image.

[0073] In this step, for each lung airway contour sub-image, the lung airway nodule recognition device calculates the gradient vector corresponding to each pixel based on the coordinates of each pixel in the lung airway contour sub-image, determines the reference change of the gradient vector corresponding to each pixel based on the gradient vector corresponding to each pixel, and determines the reference change of the gradient field corresponding to the lung airway contour sub-image based on the reference change of the gradient vector corresponding to each pixel.

[0074] In an exemplary embodiment, for each lung airway contour sub-image, the lung airway nodule recognition device inputs the lung airway contour sub-image into a calculation model for calculating the target change of the gradient field. The calculation model calculates the gradient vector corresponding to each pixel based on the coordinates of each pixel in the lung airway contour sub-image, determines the reference change of the gradient vector corresponding to each pixel based on the gradient vector corresponding to each pixel, and determines the reference change of the gradient field corresponding to the lung airway contour sub-image based on the reference change of the gradient vector corresponding to each pixel.

[0075] Specifically, step 1025 includes:

[0076] Step 10251: For each pixel in the lung airway contour sub-image, calculate the normal vector corresponding to the pixel based on the coordinates of the pixel.

[0077] In this step, the lung airway nodule recognition device calculates the normal vector corresponding to each pixel in the lung airway contour sub-image based on the coordinates of the pixel. It can be understood that the coordinates of each pixel are three-dimensional coordinates. For the three-dimensional coordinates of each pixel, the normal vector of the pixel can be estimated based on the coordinates of the neighboring pixels by means of least squares fitting, average normal vector calculation based on the neighboring points, etc.

[0078] In an exemplary embodiment, for each lung airway contour sub-image, the lung airway nodule recognition device inputs the lung airway contour sub-image into a calculation model for calculating the target change of the gradient field. The calculation model calculates the normal vector corresponding to each pixel in the lung airway contour sub-image based on the coordinates of the pixel.

[0079] Step 10252: Calculate the gradient vector corresponding to the pixel based on the normal vector and coordinates of the pixel.

[0080] In this step, for each pixel, the airway nodule identification device calculates the gradient vector corresponding to the pixel based on the pixel's normal vector and coordinates. Specifically, in, This is the partial derivative operator, where x, y, and z are the x, y, and z coordinates of the pixel, respectively.

[0081] In one exemplary embodiment, after the computational model determines the normal vector of each pixel in the lung airway contour sub-image, it calculates the gradient vector corresponding to each pixel in the lung airway contour sub-image based on the normal vector and coordinates of the pixel.

[0082] Step 10253: Based on the gradient vector corresponding to the pixel and the gradient vector corresponding to each pixel in the adjacent lung airway contour sub-image, determine the reference change amount of the gradient vector corresponding to the pixel.

[0083] In this step, for each pixel, the lung airway nodule recognition device selects the lung airway contour sub-image that is closest to and adjacent to the pixel, and obtains the gradient vectors of multiple pixels that are closest to and adjacent to the pixel in the lung airway contour sub-image. Based on the gradient vector of the pixel and the obtained gradient vectors of multiple pixels, the reference change of the gradient vector of the pixel is calculated.

[0084] In an exemplary embodiment, after the computational model determines the gradient vector of each pixel in the lung airway contour sub-image, it selects the lung airway contour sub-image that is closest to and adjacent to the pixel, and obtains the gradient vectors of multiple pixels that are closest to and adjacent to the pixel in the lung airway contour sub-image. Based on the gradient vector of the pixel and the obtained gradient vectors of the multiple pixels, the reference change of the gradient vector of the pixel is calculated.

[0085] It should be noted that, based on the gradient vector of the pixel and the gradient vectors of multiple pixels obtained, the reference change of the gradient vector of the pixel is approximated by using differentiation methods, such as partial derivatives or directional derivatives.

[0086] Step 10254: Accumulate the gradient vector reference change for each pixel in the lung airway contour sub-image to determine the gradient field reference change for the lung airway contour sub-image.

[0087] In this step, after determining the gradient vector reference change corresponding to each pixel in the lung airway contour sub-image, the lung airway nodule recognition device accumulates the gradient vector reference change corresponding to each pixel in the lung airway contour sub-image to determine the gradient field reference change corresponding to the lung airway contour sub-image.

[0088] In an exemplary embodiment, after the computational model determines the gradient vector reference change of each pixel in the lung airway contour sub-image, it accumulates the gradient vector reference change corresponding to each pixel in the lung airway contour sub-image to determine the gradient field reference change corresponding to the lung airway contour sub-image.

[0089] Step 1026: Based on the number of the lung airway contour sub-images and the gradient field reference change for each of the lung airway contour images, determine the gradient field target change corresponding to the lung airway contour image.

[0090] In this step, after determining the gradient field reference change amount corresponding to each lung airway contour sub-image, the lung airway nodule recognition device sums up the gradient field reference change amount corresponding to each lung airway contour sub-image and divides it by the number of lung airway contour sub-images to determine the gradient field target change amount corresponding to the lung airway contour image.

[0091] In an exemplary embodiment, after the computational model determines the gradient vector reference change of each pixel in the lung airway contour sub-image, it accumulates the gradient field reference change corresponding to each lung airway contour sub-image and divides it by the number of lung airway contour sub-images to determine the gradient field target change corresponding to the lung airway contour image.

[0092] The lung airway nodule recognition device in this embodiment calculates the gradient field target change corresponding to the lung airway contour image, so that subsequent judgment and recognition of lung airway nodules in the lung airway contour image are based on the gradient field target change. Since the lung airway can be regarded as a cylindrical structure, the calculation of the gradient field is relatively direct for simple cylindrical surface, and it may have high accuracy in detecting protrusions. Therefore, calculating the gradient field target change corresponding to the lung airway contour image can help improve the accuracy of lung airway nodule recognition.

[0093] Further, referring to Figure 4, a fourth embodiment of the lung airway nodule identification method is proposed. The difference between the third embodiment and the first to third embodiments is that the step of comparing the target change amount with a preset first change amount threshold to obtain a comparison result, and identifying lung airway nodules in the lung airway contour image based on the comparison result, includes:

[0094] Step 1031: Compare the target change amount with a preset first change amount threshold to obtain a comparison result. The preset first change amount threshold is the target field change amount corresponding to the lung airway contour image without lung airway nodules.

[0095] In this step, the lung airway nodule recognition device compares the target change amount of the target field of the lung airway contour image with a preset first change amount threshold to obtain a comparison result. The preset first change amount threshold is the target field change amount corresponding to the lung airway contour image without lung airway nodules. When the target field is a directional field, the preset first change amount threshold is the directional field change amount corresponding to the lung airway contour image without lung airway nodules. When the target field is a gradient field, the preset first change amount threshold is the gradient field change amount corresponding to the lung airway contour image without lung airway nodules.

[0096] Step 1032: If the comparison result is that the target change is not greater than a preset first change threshold, then it is determined that there are no lung airway nodules in the lung airway contour image.

[0097] In this step, if the lung airway nodule identification device obtains a comparison result indicating that the target change amount is not greater than a preset first change amount threshold, it determines that there are no lung airway nodules in the lung airway contour image.

[0098] Step 1033: If the comparison result is that the target change is greater than the preset first change threshold, then based on the preset second change threshold and the target field reference change corresponding to each pixel, identify the lung airway nodules in the lung airway contour image.

[0099] In this step, if the lung airway nodule recognition device obtains a comparison result that the target change is greater than the preset first change threshold, it determines that there is a lung airway nodule in the lung airway contour image. Based on the preset second change threshold and the target field reference change corresponding to each pixel, the lung airway nodule in the lung airway contour image is identified.

[0100] Specifically, step 1033 includes:

[0101] Step 10331: Compare the preset second change threshold with the target field reference change corresponding to each pixel. The preset second change threshold is the average value of the target field change corresponding to all pixels in the lung airway contour image without lung airway nodules.

[0102] In this step, the lung airway nodule recognition device compares a preset second change threshold with the target field reference change corresponding to each pixel. The preset second change threshold is the average value of the target field change corresponding to all pixels in the lung airway contour image without lung airway nodules. When the target field is a directional field, the preset second change threshold is the average value of the directional field change corresponding to the lung airway contour image without lung airway nodules. When the target field is a gradient field, the preset second change threshold is the average value of the gradient field change corresponding to the lung airway contour image without lung airway nodules.

[0103] Step 10332: The contour region composed of pixels whose target field reference change is greater than a preset second change threshold is determined as the region of the lung airway nodule in the lung airway contour image.

[0104] In this step, the lung airway nodule recognition device identifies the contour region composed of pixels whose target field reference change is greater than a preset second change threshold as the region of lung airway nodules in the lung airway contour image.

[0105] Further, after determining the contour region composed of pixels whose target field reference change is greater than a preset second change threshold as the region of the pulmonary airway nodules in the pulmonary airway contour image, the process includes:

[0106] Step a: Based on preset anatomical information, the region of the pulmonary airway nodules, and the pulmonary airway contour image, determine the anatomical location information corresponding to the pulmonary airway nodules.

[0107] In this step, the pulmonary airway nodule identification device determines the anatomical location information corresponding to the pulmonary airway nodules based on preset anatomical information, the region of the pulmonary airway nodules, and the pulmonary airway contour image. Specifically, the preset anatomical information includes the location and name of each segment of the pulmonary airway, including: the main airway, also known as the trachea, which is the initial part of the airway connecting the larynx and the bronchi, and includes the larynx, cervical trachea, and thoracic trachea; the bronchi, which branch from the main airway into two main bronchi, namely the left and right main bronchi, and includes the main bronchi, secondary bronchi, segmental bronchi, and subsegmental bronchi; the bronchioles, which are the terminal branches of the bronchi, leading to the alveoli; and the terminal bronchioles, which are the terminal ends of the bronchioles, leading to the alveoli. The pulmonary airway nodule recognition device determines the anatomical location of the pulmonary airway nodule based on the region of the pulmonary airway nodule and the outline image of the pulmonary airway, combined with preset anatomical information. For example, the region of the pulmonary airway nodule is located in the cervical tracheal segment of the main airway; or the region of the pulmonary airway nodule is located in the segmental bronchial segment of the bronchus.

[0108] Step b: Calculate the size information corresponding to the pulmonary airway nodules based on the coordinates of each pixel in the region of the pulmonary airway nodules in the pulmonary airway contour image.

[0109] In this step, the pulmonary airway nodule recognition device calculates the size information corresponding to the pulmonary airway nodule based on the coordinates of each pixel in the region of the pulmonary airway nodule in the pulmonary airway contour image. Further, the pulmonary airway nodule recognition device obtains the scaling ratio of the pulmonary airway contour image, and multiplies the pulmonary airway nodule size information calculated based on the coordinates of each pixel with the scaling ratio to obtain the true size information of the pulmonary airway nodule.

[0110] Step c: Mark the anatomical location information and the size information at the corresponding marking positions of the lung airway nodules in the lung airway contour image.

[0111] In this step, the lung airway nodule identification device marks the anatomical location and size information on the corresponding marking positions of the lung airway nodules in the lung airway contour image, making it easier for relevant users to determine the location of the lung airway nodules.

[0112] The lung airway nodule recognition device in this embodiment compares the target change amount with a preset first change amount threshold to obtain a comparison result. The preset first change amount threshold is the target field change amount corresponding to the lung airway contour image where no lung airway nodules exist. If the comparison result shows that the target change amount is not greater than the preset first change amount threshold, it is determined that no lung airway nodules exist in the lung airway contour image. If the comparison result shows that the target change amount is greater than the preset first change amount threshold, lung airway nodules in the lung airway contour image are identified based on a preset second change amount threshold and the target field reference change amount corresponding to each pixel. By determining the target change amount of the target field corresponding to the lung airway contour image, and using the target change amount of the target field on the airway contour surface, the location of protrusions on the lung airway in the medical image is determined, thereby identifying the location of small nodules and improving the accuracy of early lung airway nodule recognition.

[0113] This embodiment also provides a lung airway nodule identification device, which can be integrated into lung airway nodule identification devices such as smart terminals, PC terminals, and mobile terminals, as shown in Figure 5. The lung airway nodule identification device may include:

[0114] The acquisition unit 1001 is used to acquire medical images of the lung airways and extract the lung airway contour images from the medical images of the lung airways.

[0115] The determining unit 1002 is used to determine the target change amount of the target field corresponding to the lung airway contour image based on the coordinates of the pixels in the lung airway contour image, wherein the target field is a direction field or a gradient field.

[0116] The identification unit 1003 is used to compare the target change amount with a preset first change amount threshold, obtain a comparison result, and identify lung airway nodules in the lung airway contour image based on the comparison result.

[0117] In an optional example, determining the cell is also used for:

[0118] Obtain the central line of the lung airway in the lung airway contour image, and divide the lung airway contour into multiple circles with each central line point on the central line as the center.

[0119] The direction vector of each pixel on the lung airway contour is calculated based on the coordinates of the two nearest adjacent pixels on each pair of adjacent circumferences.

[0120] Based on the direction vector of each pixel and the preset direction vector, the target change in the direction field corresponding to the lung airway contour image is determined.

[0121] In an optional example, determining the cell is also used for:

[0122] Based on the direction vector of each pixel and the preset direction vector, the reference change of the direction vector of each pixel is calculated.

[0123] The change in the direction vector reference of each pixel is accumulated to determine the target change in the direction field corresponding to the lung airway contour image.

[0124] In an optional example, determining the cell is also used for:

[0125] The lung airway contour image is divided into regions to obtain lung airway contour sub-images.

[0126] For each of the aforementioned lung airway contour sub-images, the gradient field reference change corresponding to the lung airway contour sub-image is determined based on the coordinates corresponding to each pixel in the lung airway contour sub-image.

[0127] Based on the number of the lung airway contour sub-images and the gradient field reference change for each of the lung airway contour images, the gradient field target change corresponding to the lung airway contour image is determined.

[0128] In an optional example, determining the cell is also used for:

[0129] For each pixel in the lung airway contour sub-image, the normal vector corresponding to the pixel is calculated based on the coordinates of the pixel.

[0130] Based on the normal vector and coordinates of the pixel, the gradient vector corresponding to the pixel is calculated.

[0131] Calculate the spatial length corresponding to the gradient vector, and based on the spatial length and vector direction of the gradient vector, determine the reference change amount of the gradient vector corresponding to the pixel.

[0132] The gradient vector reference change corresponding to each pixel in the lung airway contour sub-image is accumulated to determine the gradient field reference change corresponding to the lung airway contour sub-image.

[0133] In an optional example, the identification unit is also used for:

[0134] The target change amount is compared with a preset first change amount threshold to obtain a comparison result. The preset first change amount threshold is the target field change amount corresponding to the lung airway contour image without lung airway nodules.

[0135] If the comparison result shows that the target change is not greater than a preset first change threshold, then it is determined that there are no pulmonary airway nodules in the pulmonary airway contour image.

[0136] If the comparison result shows that the target change is greater than a preset first change threshold, then based on a preset second change threshold and the target field reference change corresponding to each pixel, lung airway nodules in the lung airway contour image are identified.

[0137] In an optional example, the identification unit is also used for:

[0138] The preset second change threshold is compared with the target field reference change corresponding to each pixel. The preset second change threshold is the average value of the target field change corresponding to all pixels in the lung airway contour image without lung airway nodules.

[0139] The contour region composed of pixels whose target field reference change is greater than a preset second change threshold is determined as the region of the lung airway nodule in the lung airway contour image.

[0140] In an optional example, the identification unit is also used for:

[0141] Based on preset anatomical information, the region of the pulmonary airway nodules, and the pulmonary airway contour image, the anatomical location information corresponding to the pulmonary airway nodules is determined.

[0142] The size information corresponding to the pulmonary airway nodule is calculated based on the coordinates of each pixel in the region of the pulmonary airway contour image.

[0143] The anatomical location information and the size information are marked at the corresponding annotation positions of the lung airway nodules in the lung airway contour image.

[0144] The method in this embodiment acquires medical images of the lung airways and extracts lung airway contour images from these images. Based on the coordinates of the pixels in the lung airway contour images, the target change amount of the target field corresponding to the lung airway contour images is determined. The target field is either a direction field or a gradient field. The target change amount is compared with a preset first change amount threshold to obtain a comparison result. Based on the comparison result, lung airway nodules in the lung airway contour images are identified. By determining the target change amount of the target field corresponding to the lung airway contour images, and using the target change amount of the target field on the airway contour surface, the location of protrusions on the lung airways in the medical images is determined, thereby identifying the location of small nodules and improving the accuracy of early lung airway nodule identification.

[0145] Accordingly, this application also provides a lung airway nodule identification device, as shown in FIG6, which is a schematic diagram of the structure of the lung airway nodule identification device provided in this application embodiment. The lung airway nodule identification device 1100 includes a processor 1101 with one or more processing cores, a memory 1102 with one or more computer-readable storage media, and a computer program stored on the memory 1102 and executable on the processor. The processor 1101 and the memory 1102 are electrically connected. Those skilled in the art will understand that the structure of the lung airway nodule identification device shown in the figure does not constitute a limitation on the lung airway nodule identification device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0146] The processor 1101 is the control center of the lung airway nodule identification device 1100. It connects various parts of the device via various interfaces and lines, and executes various functions and processes data by running or loading software programs and / or units stored in the memory 1102, and by calling data stored in the memory 1102, thereby providing overall monitoring of the lung airway nodule identification device 1100. The processor 1101 can be a CPU, GPU, network processor (NP), etc., and can implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application.

[0147] In this embodiment of the application, the processor 1101 in the lung airway nodule identification device 1100 will load the instructions corresponding to the process of one or more applications into the memory 1102 according to the following steps, and the processor 1101 will run the applications stored in the memory 1102 to realize various functions. For specific implementation, please refer to the previous embodiments, which will not be repeated here.

[0148] Optionally, as shown in Figure 5, the lung airway nodule recognition device 1100 further includes: a touch screen display 1103, a radio frequency circuit 1104, an audio circuit 1105, an input unit 1106, and a power supply 1107. The processor 1101 is electrically connected to the touch screen display 1103, the radio frequency circuit 1104, the audio circuit 1105, the input unit 1106, and the power supply 1107. Those skilled in the art will understand that the structure of the lung airway nodule recognition device shown in Figure 6 does not constitute a limitation on the lung airway nodule recognition device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0149] The touch display screen 1103 can be used to display a graphical user interface (GUI) and receive operation commands generated by the user interacting with the GUI. The touch display screen 1103 may include a display panel and a touch panel. The display panel can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the airway nodule recognition device. These graphical user interfaces can be composed of graphics, text, icons, video, and any combination thereof. Optionally, the display panel can be configured using a liquid crystal display (LCD), organic light-emitting diode (OLED), or other similar technologies. The touch panel can be used to collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel), and generate corresponding operation commands, which then execute the corresponding program. Optionally, the touch panel may include a touch detection device and a touch controller. The touch detection device detects the user's touch location and the signal generated by the touch operation, transmitting the signal to the touch controller. The touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends it to the processor 1101. It can also receive and execute commands from the processor 1101. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it transmits the information to the processor 1101 to determine the type of touch event. Subsequently, the processor 1101 provides corresponding visual output on the display panel based on the type of touch event. In this embodiment, the touch panel and the display panel can be integrated into the touch display screen 1103 to achieve input and output functions. However, in some embodiments, the touch panel and the touch display screen 1103 can be implemented as two independent components to achieve input and output functions. That is, the touch display screen 1103 can also be used as part of the input unit 1106 to achieve input functions.

[0150] The radio frequency circuit 1104 can be used to transmit and receive radio frequency signals to establish wireless communication with network devices or other lung airway nodule identification devices via wireless communication, and to transmit and receive signals with network devices or other lung airway nodule identification devices.

[0151] Audio circuitry 1105 can be used to provide an audio interface between the user and the lung airway nodule identification device via a speaker and microphone. Audio circuitry 1105 can convert received audio data into electrical signals and transmit them to the speaker, where the speaker converts them into sound signals for output. Conversely, the microphone converts collected sound signals into electrical signals, which are then received by audio circuitry 1105, converted back into audio data, and processed by processor 1101 before being transmitted via radio frequency circuitry 1104 to, for example, another lung airway nodule identification device, or output to memory 1102 for further processing. Audio circuitry 1105 may also include an earphone jack to provide communication between external headphones and the lung airway nodule identification device.

[0152] The input unit 1106 can be used to receive input numbers, characters, or user characteristic information (such as fingerprints, iris, facial information, etc.), and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control.

[0153] Power supply 1107 is used to power the various components of the lung airway nodule identification device 1100. Optionally, power supply 1107 can be logically connected to processor 1101 through a power management device, thereby enabling functions such as charging, discharging, and power consumption management through the power management device. Power supply 1107 may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0154] Although not shown in Figure 6, the lung airway nodule recognition device 1100 may also include a camera, sensor, wireless fidelity module, Bluetooth module, etc., which will not be described in detail here.

[0155] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0156] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0157] Therefore, embodiments of this application provide a computer-readable storage medium storing multiple computer programs that can be loaded by a processor to execute any of the lung airway nodule identification methods provided in this application. The computer program can execute the lung airway nodule identification method; specific implementations can be found in the preceding embodiments and will not be repeated here.

[0158] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0159] Since the computer program stored in the computer-readable storage medium can execute any of the lung airway nodule identification methods provided in the embodiments of this application, the beneficial effects that any of the lung airway nodule identification methods provided in the embodiments of this application can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0160] According to one aspect of this application, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a lung airway nodule identification device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the lung airway nodule identification device to perform the methods provided in the various optional implementations of the above embodiments.

[0161] In the above embodiments of the lung airway nodule identification device, computer-readable storage medium, lung airway nodule identification equipment, and computer program product, the descriptions of each embodiment have different focuses. Parts not described in detail in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes and beneficial effects of the lung airway nodule identification device, computer-readable storage medium, computer program product, lung airway nodule identification equipment, and their corresponding units described above can be referred to the description of the lung airway nodule identification method in the above embodiments, and will not be repeated here.

[0162] The foregoing has provided a detailed description of a method, apparatus, device, computer-readable storage medium, and computer program product for identifying pulmonary airway nodules according to embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and its core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A lung airway nodule identification method, characterized in that, The lung airway nodule identification method comprises: acquiring a lung airway medical image and extracting a lung airway contour image in the lung airway medical image; determining a target variation of a target field corresponding to the lung airway contour image based on coordinates corresponding to pixel points in the lung airway contour image, the target field being a direction field or a gradient field; comparing the target variation with a preset first variation threshold to obtain a comparison result, and identifying lung airway nodules in the lung airway contour image based on the comparison result.

2. The lung airway nodule identification method of claim 1, wherein, When the target field is a direction field, determining a target variation of a target field corresponding to the lung airway contour image based on coordinates corresponding to pixel points in the lung airway contour image comprises: acquiring a lung airway center line in the lung airway contour image, and dividing the lung airway contour into a plurality of circumferences with each center line point on the lung airway center line as a center; calculating a direction vector of each pixel point on the lung airway contour based on coordinates corresponding to two adjacent pixel points on each two adjacent circumferences that are closest to each other; determining a target variation of a direction field corresponding to the lung airway contour image based on the direction vector of each pixel point and a preset direction vector.

3. The lung airway nodule identification method of claim 2, wherein, When the target field is a direction field, determining a target variation of a target field corresponding to the lung airway contour image based on coordinates corresponding to pixel points in the lung airway contour image comprises: calculating a direction vector reference variation of each pixel point based on the direction vector of each pixel point and a preset direction vector; accumulating the direction vector reference variation of each pixel point to determine the target variation of the direction field corresponding to the lung airway contour image.

4. The lung airway nodule identification method of claim 1, wherein, When the target field is a gradient field, determining a target variation of a target field corresponding to the lung airway contour image based on coordinates corresponding to pixel points in the lung airway contour image comprises: dividing the lung airway contour image into lung airway contour sub-images; for each lung airway contour sub-image, determining a gradient field reference variation corresponding to the lung airway contour sub-image based on coordinates corresponding to each pixel point in the lung airway contour sub-image; determining a gradient field target variation corresponding to the lung airway contour image based on the number of lung airway contour sub-images and each gradient field reference variation.

5. The lung airway nodule identification method of claim 4, wherein, When the target field is a gradient field, determining a target variation of a target field corresponding to the lung airway contour image based on coordinates corresponding to pixel points in the lung airway contour image comprises: for each pixel point in the lung airway contour sub-image, calculating a normal vector corresponding to the pixel point based on the coordinates corresponding to the pixel point; calculating a gradient vector corresponding to the pixel point based on the normal vector and the coordinates corresponding to the pixel point; determining a gradient vector reference variation corresponding to the pixel point based on the gradient vector corresponding to the pixel point and the gradient vector corresponding to each pixel point in an adjacent lung airway contour sub-image; accumulating the gradient vector reference variation corresponding to each pixel point in the lung airway contour sub-image to determine the gradient field reference variation corresponding to the lung airway contour sub-image.

6. The lung airway nodule identification method of claim 1, wherein, The target change amount is compared with a preset first change amount threshold to obtain a comparison result, and lung airway nodules in the lung airway contour image are identified based on the comparison result, including: The target change amount is compared with a preset first change amount threshold to obtain a comparison result. The preset first change amount threshold is the target field change amount corresponding to the lung airway contour image without lung airway nodules. If the comparison result is that the target change is not greater than a preset first change threshold, then it is determined that there are no lung airway nodules in the lung airway contour image. If the comparison result shows that the target change is greater than a preset first change threshold, then based on a preset second change threshold and the target field reference change corresponding to each pixel, lung airway nodules in the lung airway contour image are identified.

7. The lung airway nodule identification method of claim 6, wherein, Based on a preset second change threshold and the orientation field reference change corresponding to each pixel, lung airway nodules in the lung airway contour image are identified, including: The preset second change threshold is compared with the target field reference change corresponding to each pixel. The preset second change threshold is the average value of the target field change corresponding to all pixels in the lung airway contour image without lung airway nodules. The contour region composed of pixels whose target field reference change is greater than a preset second change threshold is determined as the region of the lung airway nodule in the lung airway contour image.

8. The lung airway nodule identification method of claim 7, wherein, After determining the contour region composed of pixels whose target field reference change is greater than a preset second change threshold as the region of lung airway nodules in the lung airway contour image, the process includes: Based on the preset anatomical information, the region of the pulmonary airway nodules, and the pulmonary airway contour image, the anatomical location information corresponding to the pulmonary airway nodules is determined; The size information corresponding to the pulmonary airway nodule is calculated based on the coordinates of each pixel in the region of the pulmonary airway nodule in the pulmonary airway contour image. The anatomical location information and the size information are marked at the corresponding annotation positions of the lung airway nodules in the lung airway contour image.

9. A lung airway nodule identification apparatus, characterized by, The pulmonary airway nodule identification device includes: An acquisition unit is used to acquire medical images of the lung airways and extract lung airway contour images from the medical images of the lung airways; The determining unit is used to determine the target change amount of the target field corresponding to the lung airway contour image based on the coordinates of the pixels in the lung airway contour image, wherein the target field is a direction field or a gradient field. The identification unit is used to compare the target change amount with a preset first change amount threshold, obtain a comparison result, and identify lung airway nodules in the lung airway contour image based on the comparison result.

10. An electronic device, comprising: It includes a processor and a memory, the memory storing a computer program; the processor loads the computer program from the memory to perform the steps of the lung airway nodule identification method as described in any one of claims 1-8.

11. A non-transitory computer readable storage medium, comprising: The computer-readable storage medium stores a computer program adapted for loading by a processor to perform the steps of the lung airway nodule identification method as described in any one of claims 1-8.

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