An automated wound analysis method and system based on machine vision

Abstract

The present application provides an automated wound analysis method and system based on machine vision. The method comprises: obtaining a target wound image and wound record information captured by an image acquisition device, wherein the wound record information includes a wound type identifier and historical wound recognition results; based on the target wound image, identifying the wound using a deep learning model corresponding to the wound type identifier to obtain a target wound recognition result; based on the target wound recognition result and the historical wound recognition results, fitting the wound change trend by region and stage to obtain a wound fitting result for each subregion and stage; and determining a wound analysis result based on the wound fitting result for each subregion and stage. The technical solution provided by the present application improves the accuracy of wound analysis.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of image processing technology, and in particular to an automated wound analysis method and system based on machine vision. Background Art

[0002] With the advancement of medical technology, automated wound analysis is becoming increasingly important in clinical diagnosis and treatment. In particular, in scenarios such as chronic wound management, trauma emergency care, and postoperative care, physicians need to regularly assess wound healing so they can adjust treatment plans. Traditional wound assessment methods rely on the physician's subjective judgment, are susceptible to human factors, and are time-consuming and labor-intensive. Therefore, there is an urgent need for a wound analysis method that can objectively and accurately assess wound healing, improve medical efficiency, and accelerate patient recovery.

[0003] In recent years, deep learning-based image recognition technology has been widely used. For example, convolutional neural networks have been used for wound recognition and classification, achieving good results. However, existing deep learning methods mostly focus on extracting global features, resulting in low wound analysis accuracy. Summary of the Invention

[0004] The embodiments of the present application provide an automated wound analysis method based on machine vision to solve the problem of low accuracy of wound analysis in the prior art.

[0005] In a first aspect, an embodiment of the present application provides an automated wound analysis method based on machine vision, comprising:

[0006] Acquiring a target wound image and wound record information captured by an image acquisition device, wherein the wound record information includes a wound type identifier and historical wound recognition results;

[0007] Based on the target wound image, identifying the wound using a deep learning model corresponding to the wound type identifier to obtain a target wound identification result;

[0008] Based on the target wound recognition result and the historical wound recognition result, the wound change trend is fitted in different regions and stages to obtain the wound fitting results of each sub-region and each stage;

[0009] The wound analysis result is determined based on the wound fitting results of each sub-region at each stage.

[0010] Optionally, based on the target wound recognition result and the historical wound recognition result, fitting the wound change trend by region and stage to obtain the wound fitting result of each sub-region and each stage includes:

[0011] performing semantic segmentation on the target wound image to segment the wound region in the target wound image into at least two sub-regions, wherein different sub-regions correspond to different healing stage labels;

[0012] When there are multiple sub-regions, the target wound recognition result and the historical wound recognition result are divided into the first local wound recognition result and the second local wound recognition result of each sub-region respectively;

[0013] For each sub-region, based on the first local wound recognition result and the second local wound recognition result of the sub-region, the wound change trend is fitted in stages to obtain the wound fitting results of each stage of the sub-region.

[0014] Optionally, the step of fitting the wound change trend in stages based on the first local wound recognition result and the second local wound recognition result of the sub-region to obtain the wound fitting results of each stage of the sub-region includes:

[0015] Obtaining segmentation point information of the sub-region, and dividing the first local wound recognition result and the second local wound recognition result into corresponding stages according to the segmentation point information;

[0016] Based on all local wound recognition results of each stage in the sub-region, the fitting method corresponding to each stage is used to fit the wound change trend of the corresponding stage to obtain the wound fitting results of each stage in the sub-region.

[0017] Optionally, the stages include: untreated stage, pre-treatment stage, mid-treatment stage, and post-treatment stage;

[0018] Fitting the wound change trend of the corresponding stage using a fitting method corresponding to each stage, including at least one of the following:

[0019] Linear regression was used to fit the trend of wound changes during the untreated period;

[0020] Polynomial regression was used to fit the wound change trend during the early treatment period;

[0021] Exponential regression was used to fit the wound change trend during the mid-treatment period;

[0022] A saturated growth model was used to fit the wound change trend in the later stage of treatment.

[0023] Optionally, identifying the wound based on the target wound image by using a deep learning model corresponding to the wound type identifier to obtain a target wound identification result includes:

[0024] Preprocessing the target wound image to obtain a preprocessed image;

[0025] performing pre-segmentation on the pre-processed image to distinguish wound areas from non-wound areas;

[0026] A deep learning model corresponding to the wound type identifier is selected from the preset model library, and the features of the wound area are extracted through the deep learning model corresponding to the wound type identifier to obtain the target wound recognition result.

[0027] Optionally, the deep learning model corresponding to the wound type identifier includes any of the following:

[0028] a burn recognition model corresponding to the burn identifier, wherein the burn recognition model is obtained by jump-connecting an encoder and a decoder;

[0029] A cut wound recognition model corresponding to the cut wound identifier, wherein the cut wound recognition model adopts a masked region convolutional neural network structure;

[0030] A scratch recognition model corresponding to the scratch mark, wherein the scratch recognition model adopts a lightweight convolutional neural network structure;

[0031] An infected wound recognition model corresponding to the infection identifier, wherein the infected wound recognition model is a deep learning model based on a densely connected network;

[0032] A composite wound recognition model corresponding to the composite identifier, wherein the composite wound recognition model is a multi-task learning model.

[0033] Optionally, the target wound identification result includes: wound area, wound area color features, and wound area edge features;

[0034] The determining of the wound analysis result based on the wound fitting result of each sub-region at each stage includes:

[0035] determining the severity of the wound based on the area of ​​the wound region, the color characteristics of the wound region, and the edge characteristics of the wound region;

[0036] The wound analysis result is determined based on the wound severity and the wound fitting results of each sub-region and each stage.

[0037] In a second aspect, an embodiment of the present application provides an automated wound analysis system based on machine vision, comprising:

[0038] An acquisition module, configured to acquire a target wound image and wound record information acquired by an image acquisition device, wherein the wound record information includes a wound type identifier and historical wound recognition results;

[0039] an identification module, configured to identify the wound based on the target wound image using a deep learning model corresponding to the wound type identifier, and obtain a target wound identification result;

[0040] A fitting module is used to fit the wound change trend in different regions and stages based on the target wound recognition result and the historical wound recognition results, so as to obtain the wound fitting results in each sub-region and each stage;

[0041] A determination module is used to determine the wound analysis result based on the wound fitting results of each sub-region at each stage.

[0042] In a third aspect, an embodiment of the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an automated wound analysis method based on machine vision as described in any one of the first aspects.

[0043] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements an automated wound analysis method based on machine vision as described in any one of the first aspects.

[0044] In an embodiment of the present application, an automated wound analysis method based on machine vision is provided. The method comprises: obtaining a target wound image and wound record information collected by an image acquisition device, wherein the wound record information includes a wound type identifier and a historical wound recognition result; based on the target wound image, identifying the wound through a deep learning model corresponding to the wound type identifier to obtain a target wound recognition result; based on the target wound recognition result and the historical wound recognition result, fitting the wound change trend by region and stage to obtain the wound fitting result of each sub-region and each stage; determining the wound analysis result based on the wound fitting result of each sub-region and each stage. The present application identifies the wound through a deep learning model corresponding to the wound type identifier and fits the wound change trend by region and stage, which can effectively overcome the defects of the existing scheme, realize the refined and automated analysis of the wound, improve the accuracy of the wound analysis, and also improve the adaptability to the analysis of wounds of different types and different stages. Furthermore, the embodiment of the present application can more accurately predict and evaluate the healing speed and final state of the wound by using specific fitting methods for different stages.

[0045] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0047] Figure 1 A flowchart of an automated wound analysis method based on machine vision provided in an embodiment of the present application;

[0048] Figure 2 A schematic diagram of the structure of an automated wound analysis system based on machine vision provided in an embodiment of the present application;

[0049] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0051] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 11, 12, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0052] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0053] Figure 1 A flowchart of an automated wound analysis method based on machine vision is provided in an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0054] S11. Acquire a target wound image and wound record information captured by an image capture device, wherein the wound record information includes a wound type identifier and historical wound recognition results.

[0055] It should be understood that the image acquisition device can be a device for capturing the target wound image, such as a digital camera, a smartphone camera, etc., which can produce high-quality digital images for subsequent analysis. The wound type identification is a burn identification, a cut identification, an abrasion identification, an infection identification, a composite identification, etc. The historical recognition results are based on the specific information obtained by using a deep learning model to identify historical wound images, such as wound location, wound area, wound area color characteristics and wound area edge characteristics, etc., wherein the wound area refers to the wound area in the image, and the wound area can be converted into the actual wound area through a coordinate system conversion method. The actual wound area is a quantitative representation of the wound size. The wound size not only includes the actual area of ​​the wound, but may also involve dimensions such as the depth, length, and width of the wound. The acquisition time of the historical wound image is earlier than the acquisition time of the target wound image.

[0056] S12. Based on the target wound image, identify the wound using a deep learning model corresponding to the wound type identifier to obtain a target wound identification result.

[0057] In this embodiment, burns, cuts, abrasions, infections, and complex wounds have different characteristics, so designing different deep learning models can better capture and identify these characteristics.

[0058] S13. Based on the target wound recognition result and the historical wound recognition result, the wound change trend is fitted in different regions and stages to obtain the wound fitting results of each sub-region and each stage.

[0059] Among them, regional stage fitting refers to dividing the wound area in the target wound image into multiple sub-regions, with different sub-regions corresponding to different healing stages, and then fitting the wound change trend in different stages for each region. Among them, the wound change trend can refer to the change in wound area, wound color, wound edge, and redness and swelling within the region; the healing stage includes: hemostasis stage, inflammation stage, proliferation stage, remodeling stage, etc., and different healing stages can be represented by wound characteristics, for example: light red area, dark red area, yellow area, black area, etc. are segmented separately. Alternatively, the basis for regionalization can be anatomical structure: specifically, because the skin structure and healing characteristics of different anatomical locations may vary, regionalization based on anatomical structure helps to more accurately assess the healing status of each part.

[0060] Illustratively, in this embodiment, a wound area A may be divided into two sub-areas, where sub-area 1 is in the inflammatory stage and sub-area 2 is in the proliferative stage.

[0061] S14. Determine the wound analysis result based on the wound fitting results of each sub-region and each stage.

[0062] It should be understood that the wound fitting results for each sub-region at each stage provide a detailed description of the wound, and the wound analysis results can be a wound analysis report. For the same wound, areas may be in different healing stages at the same time. Therefore, in order to provide targeted treatment for each part, this embodiment comprehensively considers factors such as the healing trends of all sub-regions at each stage and the current wound status to form a detailed wound analysis report. The wound analysis results may include the current severity of the wound, the current severe area, the expected healing time for each sub-region, and recommended treatment measures for each sub-region.

[0063] The embodiment of the present application identifies wounds through a deep learning model corresponding to the wound type identifier, and fits the wound change trend by region and stage. It can effectively overcome the defects of existing solutions, realize refined and automated analysis of wounds, improve the accuracy of wound analysis, and also improve the adaptability of wound analysis of different types and stages.

[0064] It should be noted that the same deep learning model has different recognition accuracy for different types of wounds. In order to improve the recognition accuracy of each type of wound, the embodiments of the present application provide different types of deep learning models. In some optional embodiments, S12, based on the target wound image, identifies the wound using the deep learning model corresponding to the wound type identifier to obtain the target wound recognition result, including:

[0065] Step 121: pre-process the target wound image to obtain a pre-processed image.

[0066] Step 122 : Pre-segment the pre-processed image to distinguish the wound area from the non-wound area.

[0067] Step 123: Select a deep learning model corresponding to the wound type identifier from a preset model library, and extract features of the wound area through the deep learning model corresponding to the wound type identifier to obtain a target wound recognition result.

[0068] It should be understood that preprocessing includes binarization, denoising, grayscale processing, and edge processing. After pre-segmentation, only the wound area is input into the deep learning model corresponding to the wound type identifier. This can reduce the impact of non-wound areas on the deep learning model's recognition, thereby improving the recognition accuracy of the deep learning model.

[0069] It is also important to understand that different types of wounds have different characteristics that determine differences in their treatment and management. Understanding these characteristics can help select appropriate treatments and monitoring tools. The following are the characteristics of several common wound types and the reasons for their design:

[0070] Burn-type wounds have the following characteristics: Burns typically cause skin discoloration, blisters, eschars, etc. The degree of burn pain varies depending on the depth of the burn, with superficial burns being more painful and deep burns potentially causing numbness. Superficial burns are often accompanied by blisters, while deep burns are less likely to cause blisters. Severe burns may cause tissue necrosis and the formation of eschars. To address the above characteristics, this embodiment can adopt an encoder-decoder architecture to effectively capture and reconstruct the complex structure of the wound, especially for features such as blisters and eschars caused by burns. Through skip connections, more detailed information can be retained, which helps to more accurately identify and segment the burn area.

[0071] Cut-type wounds have the following characteristics: Cuts usually appear as straight or curved wounds. Cuts are often accompanied by obvious bleeding, especially when arteries are damaged. The edges of cuts are usually neat and easy to identify. The depth of cuts varies and may be limited to the epidermis, or may penetrate deep into the muscle or even the bone. In view of the above characteristics, this embodiment can use a masked region convolutional neural network. This structure can perform object detection and segmentation simultaneously and is suitable for identifying linear and edge features of cuts. In addition, the masked region convolutional neural network can accurately locate the position and edges of the wound, which helps to assess the depth and length of the wound.

[0072] Abrasions have the following characteristics: Abrasions mainly affect the epidermis and usually appear as wear and tear on the skin surface. A small amount of blood and tissue fluid often seeps out of the abrasion site. The shape of the abrasion is usually irregular and may be flaky or strip-shaped. The abrasion site is often accompanied by redness, swelling and mild pain. In view of the above characteristics, this embodiment can use a lightweight convolutional neural network. Since the characteristics of abrasions are relatively simple, the lightweight convolutional neural network is sufficient to complete the recognition task while maintaining high computational efficiency. The lightweight model can run quickly in a resource-limited environment and is suitable for real-time monitoring and evaluation.

[0073] Infected wounds have the following characteristics: Infected wounds are often accompanied by symptoms such as redness, swelling, fever, and pain. Infected wounds may exude pus, typically yellow or green in color. Infected wounds may emit a foul odor. The skin temperature at the infected site may be higher than that of the surrounding healthy skin. To address these characteristics, this embodiment utilizes a densely connected network to effectively extract complex features, making it suitable for identifying the various symptoms of infected wounds.

[0074] Complex wounds have the following characteristics: They may simultaneously include multiple types of injuries, such as burns, cuts, and abrasions. Complex wounds are structurally complex and may involve multiple layers of tissue damage. Symptoms such as redness, swelling, exudation, and pain may also occur simultaneously. To address these characteristics, this embodiment employs a multi-task learning model to simultaneously process multiple tasks. This model is suitable for identifying and evaluating the various characteristics of complex wounds. This embodiment also improves the overall performance of the model by jointly optimizing multiple tasks, ensuring accurate identification of different types of injuries.

[0075] Therefore, the deep learning model corresponding to the wound type identifier includes any of the following: (1) a burn recognition model corresponding to the burn identifier, which is obtained by jump-connecting the encoder and the decoder; (2) a cut wound recognition model corresponding to the cut wound identifier, which adopts a masked area convolutional neural network structure; (3) an abrasion recognition model corresponding to the abrasion identifier, which adopts a lightweight convolutional neural network structure; (4) an infected wound recognition model corresponding to the infection identifier, which is a deep learning model based on a densely connected network; (5) a composite wound recognition model corresponding to the composite identifier, which is a multi-task learning model.

[0076] In summary, each type of wound has its own unique characteristics. Different deep learning models are designed to better capture and identify these characteristics, thereby improving the accuracy and efficiency of wound identification and assessment. These models are selected and designed to meet the needs of specific wound types in clinical diagnosis and treatment.

[0077] It should be noted that this embodiment can segment the wound area according to different healing stages, and can also divide the same wound area into stages in time based on factors such as whether the same wound area has been treated, how many times it has been treated, and what type of treatment method it has undergone, thereby achieving regional stage fitting to improve the accuracy of wound analysis. In some optional embodiments, S13, based on the target wound identification results and historical wound identification results, the wound change trend is fitted by region and stage to obtain the wound fitting results for each stage of each sub-region, including:

[0078] Step 131: Perform semantic segmentation on the target wound image to divide the wound region into at least two subregions, with different subregions corresponding to different healing stage labels. Exemplary healing stage labels include those corresponding to the hemostatic stage, the inflammatory stage, the proliferative stage, and the remodeling stage. Using a deep learning model for semantic segmentation can more accurately identify wound regions and reduce misidentification.

[0079] Step 132: If there are multiple sub-regions, the target wound recognition result and the historical wound recognition results are divided into a first local wound recognition result and a second local wound recognition result for each sub-region. By breaking down the overall recognition result into each sub-region, the changing trend of each region can be analyzed more accurately.

[0080] For example, if the number of sub-regions is 3, then this embodiment may divide the target wound recognition result into the first local wound recognition result of sub-region 1, the first local wound recognition result of sub-region 2, and the first local wound recognition result of sub-region 3. Similarly, this embodiment may divide the historical wound recognition results into the second local wound recognition result of sub-region 1, the second local wound recognition result of sub-region 2, and the second local wound recognition result of sub-region 3.

[0081] Step 133 : For each sub-region, based on the first local wound recognition result and the second local wound recognition result of the sub-region, the wound change trend is fitted in stages to obtain the wound fitting results of each stage of the sub-region.

[0082] It should be understood that the second local wound recognition result may be local wound recognition results corresponding to multiple historical moments. Therefore, for each sub-region, the corresponding first local wound recognition result and second local wound recognition result may cover a single phase or multiple phases. When multiple phases are involved, this embodiment can fit the wound change trend in stages.

[0083] Among them, staging refers to dividing the healing process of local wounds into different stages in order to more systematically evaluate and manage the healing process of local wounds. The staging criteria are usually based on the following aspects: time, treatment intervention, etc. For example, sub-region 1 is currently in the growth stage, so sub-region 1 has previously experienced the hemostatic stage and the inflammatory stage. The embodiment of the present application can analyze its wound fitting results in the hemostatic stage, the inflammatory stage and the growth stage through the first local wound recognition result and the second local wound recognition result of the region, while sub-region 2 is currently in the inflammatory stage, so sub-region 2 has previously experienced the hemostatic stage. The embodiment of the present application can analyze its wound fitting results in the hemostatic stage and the inflammatory stage through the first local wound recognition result and the second local wound recognition result of the region.

[0084] By dividing the wound area into multiple sub-areas, the present embodiment can more precisely manage the healing status of each area and provide more targeted treatment recommendations. By dividing the wound in the same area into different stages over time, the wound healing process can be more systematically evaluated and managed, and the treatment plan can be adjusted in a timely manner.

[0085] The stage division performed in this embodiment is of great significance for generating accurate wound analysis results. Optionally, this embodiment can perform stage division based on segmentation point information. As an optional implementation method, step 133, based on the first local wound recognition result and the second local wound recognition result of the sub-region, fits the wound change trend in stages to obtain the wound fitting results of each stage in the sub-region, including:

[0086] Step a1: Obtain the segmentation point information of the sub-area, and divide the first local wound recognition result and the second local wound recognition result into corresponding stages according to the segmentation point information; Step a2: Based on all local wound recognition results of each stage of the sub-area, use the fitting method corresponding to each stage to fit the wound change trend of the corresponding stage to obtain the wound fitting results of each stage of the sub-area.

[0087] Among them, the segmentation point information includes the scratching time point, the wound treatment time point, etc. The wound treatment time point can be the treatment time point of using a specified wound treatment method, the treatment time point of using any wound treatment method, or a periodic segmentation time point. According to the segmentation point information, for example, the first stage is: the period between the scratching time point and the time point of the first wound treatment, the second stage is from the 1st day to the 10th day after the first wound treatment, and the third stage is from the 1st day to the 10th day after the second wound treatment.

[0088] It should be noted that by using different fitting methods to fit wound trends at different stages, the wound healing rate and final state can be more accurately predicted, providing a scientific basis for medical personnel. The stages include: untreated stage, pre-treatment stage, mid-treatment stage, and late-treatment stage. Corresponding fitting methods corresponding to each stage are used to fit the wound trends at the corresponding stage, including at least one of the following: linear regression to fit the wound trends at the untreated stage; polynomial regression to fit the wound trends at the pre-treatment stage; exponential regression to fit the wound trends at the mid-treatment stage; and saturated growth model to fit the wound trends at the late-treatment stage.

[0089] The wound change trends at each stage may include the change trends of wound indicators such as area, depth, degree of redness and swelling, and healing speed. The above fitting strategy is consistent with the change trend of any wound indicator.

[0090] The linear regression used in the untreated stage adopts the following formula:

[0091]

[0092] Among them, y1 represents the wound index value in the untreated stage, t represents time, a 01The wound index value at the initial moment of the untreated stage can be determined by the first local wound identification result of the sub-region in the untreated stage. 11 This represents the linear rate of change of wound indicators over time. It can be estimated by the changing trend of the local wound identification results in the subregion during the untreated phase. λ1 represents the regularization parameter in linear regression, which is used to control model complexity and prevent overfitting. Its value can be adjusted based on the amount of data from the subregion during the untreated phase. A larger amount of data can be used to appropriately reduce the regularization parameter.

[0093] For example, a 11 When it is equal to -0.5, it means that the wound area decreases by 0.5 square centimeters every day.

[0094] The polynomial regression used in the pre-treatment phase was as follows:

[0095]

[0096] Among them, y2 represents the wound index value in the early stage of treatment, t represents time, a 02 Indicates the wound index value at the initial moment of the early stage of treatment, a 13 、a 22 、a 32 ,…,a n2 The polynomial coefficients for the early treatment phase describe the nonlinear changes in wound metrics over time and can be determined by least squares fitting all local wound identification results for the subregion during the early treatment phase. λ2 is the regularization parameter for the early treatment phase, which controls model complexity.

[0097] For example, n=2, a 12 =-0.5, a 22 = 0.1, it means that the wound area decreases by 0.5 square centimeters every day, but the rate of decrease increases gradually with time, because a 22 The value of is positive.

[0098] The exponential regression used in the mid-treatment period was calculated using the following formula:

[0099]

[0100] Among them, y3 represents the wound index value in the middle stage of treatment, t represents time, a 03 It represents the initial value of the mid-treatment stage, which can be determined by the first local wound identification result of the sub-area in the mid-treatment stage. 13 Indicates the wound healing growth rate in the mid-stage of treatment, which can be estimated by the changing trend of the local wound identification results in the sub-region in the mid-stage of treatment. 23The quadratic term coefficient in the mid-treatment stage is used to adjust the curvature of the growth curve and can be determined by performing least square fitting on the local wound identification results of the sub-region in the mid-treatment stage.

[0101] The saturation growth model used in the late treatment phase uses the following formula:

[0102]

[0103] Among them, y4 represents the wound index value in the later stage of treatment, t represents time, a 04 is the maximum value in the late stage of treatment, indicating the final state of wound healing, and its value can be estimated by the last local wound identification result of the sub-region in the late stage of treatment. 14 The growth rate of wound healing in the later stage of treatment. The larger the value, the faster the healing speed. Its value can be estimated by the changing trend of the local wound identification results of the sub-region in the later stage of treatment. 24 is the time constant in the late stage of treatment, indicating the time point at which half of the maximum value is reached. It can be used to evaluate the intermediate stage of the healing process. Its value can be determined by least square fitting of the local wound identification results of the sub-region in the late stage of treatment. 34 It is the offset in the later stage of treatment, used to adjust the baseline position of the curve, and can reflect the basic state of the wound healing process. Its value can be determined by least square fitting of the local wound identification results of the sub-region in the later stage of treatment.

[0104] In other words, in the untreated stage, wound changes are typically linear. Without external intervention, the wound's state is relatively stable, and the change trend is relatively simple. Therefore, in this embodiment, linear regression can be used to fit the wound's change trend in the untreated stage. In the early stages of treatment, wound changes may be more complex because the wound's state changes rapidly after treatment begins. Polynomial regression can better capture this nonlinear change trend. Therefore, in this embodiment, polynomial regression can be used to fit the wound's change trend in the early stages of treatment. In the mid-stage of treatment, the wound's healing rate may accelerate, showing an exponential growth trend. Therefore, exponential regression can better fit this accelerated healing process. In the late stage of treatment, the wound is generally closed, a scar has formed, exudate has largely disappeared, and pain has also largely subsided. The main characteristic of this stage is that the wound's healing rate gradually slows down, entering a stage of stability or slow remodeling. Therefore, the saturated growth model is suitable for describing a trend that gradually approaches a maximum value. In the late stage of treatment, the wound healing process gradually approaches a stable state.

[0105] Through these steps, the wound healing process can be more accurately assessed and managed, providing medical staff with scientific treatment recommendations and improving treatment outcomes.

[0106] In some optional embodiments, the target wound identification results include: wound area, wound area color features, and wound area edge features; wherein the wound area may refer to the area occupied by the wound in the image, typically in square centimeters. The wound area color features may refer to the color distribution of the wound, which may include areas of different colors such as red, yellow, and black. The wound area edge features may refer to the shape of the wound edge, such as whether it is neat or has irregular edges.

[0107] Accordingly, S14, determining wound analysis results based on the wound fitting results of each sub-region at each stage, includes:

[0108] Step 141: Determine the severity of the wound based on the wound area, the wound color characteristics, and the wound edge characteristics.

[0109] Among them, the wound severity of the corresponding sub-region is comprehensively evaluated based on the wound area, color characteristics and edge characteristics of each sub-region, which is usually used to judge the urgency and treatment needs of the partial area of ​​the wound. The reason for the comprehensive evaluation of multiple features: the wound area of ​​the sub-region corresponding to the hemostatic stage or the inflammatory stage is an important indicator for evaluating the severity of the wound. The larger the wound area of ​​these two sub-regions, the higher the severity. The color characteristics of the wound area in each region can reflect the state of the local wound, such as red may indicate inflammation, and black may indicate necrosis. The edge characteristics of the wound area can reflect the healing condition of the local wound, such as neat edges may indicate good healing, and irregular edges may indicate poor healing. The severity of the wound can be the severity of the wound as a whole, which can be comprehensively determined based on the severity of the wound corresponding to each sub-region.

[0110] As a possible implementation, this embodiment may assess the severity of the wound by setting a threshold or using a scoring system.

[0111] Step 142: Determine a wound analysis result based on the wound severity and the wound fitting results of each sub-region and each stage.

[0112] It should be understood that a comprehensive assessment report of the wound status is formed by comprehensively considering the severity of the wound and the wound fitting results of each sub-area and stage. The wound analysis results include the current state of the wound, healing progress, potential risks, treatment recommendations, etc.

[0113] In this embodiment, by comprehensively evaluating the severity of the wound and the wound change trends in each sub-region at each stage, the healing status of each sub-region of the wound can be comprehensively evaluated, and more comprehensive treatment recommendations can be provided for each sub-region.

[0114] Figure 2 A schematic diagram of the structure of an automated wound analysis system based on machine vision provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the system includes:

[0115] The acquisition module 21 is configured to acquire the target wound image and wound record information acquired by the image acquisition device, wherein the wound record information includes a wound type identifier and historical wound recognition results.

[0116] The identification module 22 is used to identify the wound based on the target wound image through a deep learning model corresponding to the wound type identifier to obtain a target wound identification result.

[0117] The fitting module 23 is used to fit the wound change trend in different regions and stages based on the target wound recognition result and the historical wound recognition result, and obtain the wound fitting results in each sub-region and each stage.

[0118] The determination module 24 is configured to determine the wound analysis result based on the wound fitting result of each sub-region at each stage.

[0119] Figure 2 The automated wound analysis system based on machine vision can perform Figure 1 The implementation principles and technical effects of the machine vision-based automated wound analysis method described in the illustrated embodiment will not be elaborated upon. The specific manner in which the various modules and units of the machine vision-based automated wound analysis system in the aforementioned embodiment perform their operations has been described in detail in the relevant embodiments of the method and will not be elaborated upon here.

[0120] In one possible design, Figure 2 The machine vision-based automated wound analysis system of the illustrated embodiment can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32 .

[0121] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0122] The processing component 32 is used to: obtain the target wound image and wound record information captured by the image acquisition device, wherein the wound record information includes a wound type identifier and historical wound recognition results; based on the target wound image, identify the wound through a deep learning model corresponding to the wound type identifier to obtain a target wound recognition result; based on the target wound recognition result and the historical wound recognition results, fit the wound change trend in different regions and stages to obtain the wound fitting results for each sub-region and each stage; and determine the wound analysis results based on the wound fitting results for each sub-region and each stage.

[0123] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0124] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as random access memory (RAM), static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0125] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0126] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0127] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0128] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0129] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment shows an automated wound analysis method based on machine vision.

[0130] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0131] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0132] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An automated wound analysis method based on machine vision, characterized in that: include: Acquire a target wound image and wound record information captured by an image acquisition device, wherein the wound record information includes a wound type identifier and historical wound recognition results, wherein the wound type identifier includes a burn identifier, a cut identifier, an abrasion identifier, an infection identifier, and a combined identifier; Based on the target wound image, identifying the wound using a deep learning model corresponding to the wound type identifier to obtain a target wound identification result, wherein the target wound identification result includes: wound area, wound area color characteristics, and wound area edge characteristics; Based on the target wound recognition result and the historical wound recognition results, the wound change trend is fitted in different regions and stages to obtain the wound fitting results of each stage in each sub-region. The wound change trend includes: the change of wound area, wound color, wound edge, and redness and swelling degree in the corresponding region; Determining a wound analysis result based on the wound fitting results of each sub-region at each stage; The step of fitting the wound change trend by region and stage based on the target wound recognition result and the historical wound recognition result to obtain the wound fitting results of each stage in each sub-region includes: Performing semantic segmentation on the target wound image using a deep learning model to segment the wound area in the target wound image into at least two sub-areas, where different sub-areas correspond to different healing stage labels; When there are multiple sub-regions, the target wound recognition result and the historical wound recognition result are divided into the first local wound recognition result and the second local wound recognition result of each sub-region respectively; For each sub-region, based on the first local wound recognition result and the second local wound recognition result of the sub-region, the wound change trend is fitted in stages to obtain the wound fitting results of each stage of the sub-region.

2. The method according to claim 1, characterized in that The wound change trend is fitted in stages based on the first local wound recognition result and the second local wound recognition result of the sub-region to obtain the wound fitting results of each stage of the sub-region, including: Obtaining segmentation point information of the sub-region, and dividing the first local wound recognition result and the second local wound recognition result into corresponding stages according to the segmentation point information; Based on all local wound recognition results of each stage in the sub-region, the fitting method corresponding to each stage is used to fit the wound change trend of the corresponding stage to obtain the wound fitting results of each stage in the sub-region.

3. The method according to claim 2, characterized in that The stages include: untreated stage, pre-treatment stage, mid-treatment stage and post-treatment stage; Fitting the wound change trend of the corresponding stage using a fitting method corresponding to each stage, including at least one of the following: Linear regression was used to fit the trend of wound changes during the untreated period; Polynomial regression was used to fit the wound change trend during the early treatment period; Exponential regression was used to fit the wound change trend during the mid-treatment period; A saturated growth model was used to fit the wound change trend in the later stage of treatment.

4. The method according to claim 1, wherein The step of identifying the wound based on the target wound image by using a deep learning model corresponding to the wound type identifier to obtain a target wound identification result includes: Preprocessing the target wound image to obtain a preprocessed image; performing pre-segmentation on the pre-processed image to distinguish wound areas from non-wound areas; A deep learning model corresponding to the wound type identifier is selected from the preset model library, and the features of the wound area are extracted through the deep learning model corresponding to the wound type identifier to obtain the target wound recognition result.

5. The method according to claim 4, characterized in that A deep learning model corresponding to the wound type identifier includes any of the following: a burn recognition model corresponding to the burn identifier, wherein the burn recognition model is obtained by jump-connecting an encoder and a decoder; A cut wound recognition model corresponding to the cut wound identifier, wherein the cut wound recognition model adopts a masked region convolutional neural network structure; A scratch recognition model corresponding to the scratch mark, wherein the scratch recognition model adopts a lightweight convolutional neural network structure; An infected wound recognition model corresponding to the infection identifier, wherein the infected wound recognition model is a deep learning model based on a densely connected network; A composite wound recognition model corresponding to the composite identifier, wherein the composite wound recognition model is a multi-task learning model.

6. The method according to claim 1, characterized in that The determining of the wound analysis result based on the wound fitting result of each sub-region at each stage includes: determining the severity of the wound based on the area of ​​the wound region, the color characteristics of the wound region, and the edge characteristics of the wound region; The wound analysis result is determined based on the wound severity and the wound fitting results of each sub-region and each stage.

7. An automated wound analysis system based on machine vision, characterized in that: include: an acquisition module, configured to acquire a target wound image and wound record information acquired by an image acquisition device, wherein the wound record information includes a wound type identifier and historical wound recognition results, wherein the wound type identifier includes a burn identifier, a cut identifier, an abrasion identifier, an infection identifier, and a combined identifier; an identification module, configured to identify the wound based on the target wound image using a deep learning model corresponding to the wound type identifier, and obtain a target wound identification result, wherein the target wound identification result includes: wound area, wound area color characteristics, and wound area edge characteristics; A fitting module is used to fit the wound change trend by region and stage based on the target wound recognition result and the historical wound recognition results, and obtain the wound fitting results of each sub-region and each stage. The wound change trend includes: the change of wound area, wound color, wound edge, and redness and swelling degree in the corresponding region; a determination module, configured to determine a wound analysis result based on the wound fitting results of each sub-region at each stage; The step of fitting the wound change trend by region and stage based on the target wound recognition result and the historical wound recognition result to obtain the wound fitting results of each stage in each sub-region includes: Performing semantic segmentation on the target wound image using a deep learning model to segment the wound area in the target wound image into at least two sub-areas, where different sub-areas correspond to different healing stage labels; When there are multiple sub-regions, the target wound recognition result and the historical wound recognition result are divided into the first local wound recognition result and the second local wound recognition result of each sub-region respectively; For each sub-region, based on the first local wound recognition result and the second local wound recognition result of the sub-region, the wound change trend is fitted in stages to obtain the wound fitting results of each stage of the sub-region.

8. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an automated wound analysis method based on machine vision as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the automated wound analysis method based on machine vision as claimed in any one of claims 1 to 6 is implemented.

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