Macrophage identification method and system based on IV-OCT image

Abstract

The invention discloses a macrophage recognition method and system based on an IV-OCT image. The method comprises the steps of multi-modal preprocessing, dynamic feature enhancement, mixed deep learning, adaptive threshold optimization and macrophage recognition. The invention relates to the technical field of macrophage recognition, in particular to a macrophage recognition method and system based on an IV-OCT image, which realize more stable and accurate automatic recognition and positioning of macrophages and improve the consistency and efficiency of recognition. Carrying out multi-modal preprocessing by adopting a multi-modal image enhancement method combining adaptive contrast enhancement and a dual-channel improved generative adversarial network; performing dynamic feature enhancement by adopting a normalized standard deviation ratio calculation method combined with partition sampling enhancement; and carrying out mixed deep learning macrophage image segmentation by adopting a branch feature extraction network of cross attention feature fusion in combination with an enhanced discriminant generative adversarial network.

Description

Technical Field

[0001] The present invention relates to the technical field of macrophage identification, and in particular to a macrophage identification method and system based on IV-OCT images. Background Art

[0002] The macrophage identification method and system based on IV-OCT images is a medical auxiliary tool that combines optical coherence tomography (IV-OCT) technology with artificial intelligence analysis. It automatically identifies macrophage infiltration areas in coronary atherosclerotic plaques through computers. As an important development in the field of intravascular imaging, this method can evaluate plaque vulnerability in real time and non-invasively during interventional surgery, provide clinicians with objective indicators of inflammatory activity, assist in formulating precise treatment plans, and can also be used for dynamic monitoring of the efficacy of anti-atherosclerotic drugs.

[0003] However, in the existing macrophage recognition methods based on IV-OCT images, there are technical problems that the recognition results often rely on manual experience and have poor repeatability due to uneven image quality, blurred cell structure and obvious differences in tissue performance between different individuals; in the existing basic processing of image data, there are technical problems that the IV-OCT images are affected by factors such as blood flow and tissue reflection during the acquisition process, resulting in low image contrast and unclear structural hierarchy, which affects the accuracy of subsequent analysis; in the existing image feature optimization process, there is a technical problem that due to the complexity of tissue structure and the local distribution characteristics of macrophages, traditional methods cannot effectively capture local change trends, affecting the sensitive recognition of tiny cell areas; in the existing macrophage segmentation process, there is a technical problem that it is often affected by unclear image hierarchy, blurred tissue boundaries and large heterogeneity, resulting in difficult to accurately segment cell boundaries and easy confusion of adjacent structures; in the existing segmentation threshold adjustment process, there is a technical problem that due to the large individual differences in images and the wide range of cell activity changes, the traditional fixed threshold segmentation method is prone to over- or under-recognition, and the cost of manual intervention is high and the subjectivity is strong. Summary of the invention

[0004] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a macrophage recognition method and system based on IV-OCT images. In view of the technical problems that the existing macrophage recognition methods based on IV-OCT images often rely on manual experience and have poor repeatability due to uneven image quality, blurred cell structure and obvious differences in tissue performance between different individuals, this solution creatively starts from the significant differences of the recognition objects themselves and creatively introduces a structure-guided feature separation mechanism, which can more clearly distinguish the difference between the macrophage area and the background tissue, realize more stable and accurate automatic recognition and positioning of macrophages, significantly reduce the necessity of manual intervention, and improve the recognition efficiency. In view of the technical problems in the existing basic processing of image data, such as low image contrast and unclear structural hierarchy due to the influence of factors such as blood flow and tissue reflection during the acquisition process of IV-OCT images, which affect the accuracy of subsequent analysis, this solution creatively adopts a multimodal image enhancement method combining adaptive contrast enhancement and dual-channel improved generative adversarial network for multimodal preprocessing, which realizes the intelligent improvement of image quality and the true restoration of regional details, providing a clearer and more biologically meaningful image basis for subsequent recognition and analysis; in view of the technical problems in the existing image feature optimization process ... Local change trends affect the technical problem of sensitive identification of tiny cell areas. This solution creatively starts from the characteristics of obvious local structural changes and uneven distribution of macrophages, and adopts the normalized standard deviation ratio calculation method combined with partition sampling enhancement to perform dynamic feature enhancement, thereby achieving accurate capture of tiny cell changes and improved recognition sensitivity. In view of the technical problems that the existing macrophage segmentation process is often affected by unclear image levels, blurred tissue boundaries and large heterogeneity, resulting in difficulty in accurate segmentation of cell boundaries and easy confusion of adjacent structures, this solution creatively starts from the dual needs of image structure feature expression and cell tissue discrimination ability improvement, and adopts a branch feature extraction network with cross-attention feature fusion. Combined with the enhanced discriminative generative adversarial network, hybrid deep learning macrophage image segmentation is performed, which achieves high-resolution segmentation of the macrophage area and stronger boundary discrimination ability, effectively improving the recognition accuracy and segmentation integrity under complex backgrounds; in the existing segmentation threshold adjustment process, there are technical problems such as large individual differences in images and wide range of cell activity variations, the traditional fixed threshold segmentation method is prone to over- or under-recognition, and the cost of manual intervention is high and subjectivity is strong. This scheme creatively adopts the differentiable maximum inter-class variance threshold algorithm combined with morphological correction to perform adaptive segmentation threshold optimization, realizing a more flexible, efficient and robust image segmentation process, effectively improving the stability and practicality of automatic recognition.

[0005] The technical solution adopted by the present invention is as follows: The present invention provides a macrophage identification method based on IV-OCT images, the method comprising the following steps:

[0006] Step S1: multimodal preprocessing;

[0007] Step S2: dynamic feature enhancement;

[0008] Step S3: Hybrid deep learning;

[0009] Step S4: adaptive threshold optimization;

[0010] Step S5: Macrophage identification.

[0011] Further, in step S1, the multimodal preprocessing is used to collect data and enhance macrophage-related signal features in the image processing stage, specifically collecting IV-OCT images and catheter position information, and using a multimodal image enhancement method combining adaptive contrast enhancement and a dual-channel improved generative adversarial network to perform multimodal preprocessing to obtain macrophage feature enhanced image data;

[0012] The step of using a multimodal image enhancement method combining adaptive contrast enhancement and a dual-channel improved generative adversarial network to perform multimodal preprocessing to obtain macrophage feature enhanced image data includes:

[0013] Step S11: constructing a dual-channel improved generative adversarial network, specifically by constructing a dual-channel U-shaped network as the generator structure of the adversarial generative network, and performing dual-channel setting and discriminator setting of the generator structure to obtain the dual-channel improved generative adversarial network;

[0014] The generator structure has a dual-channel setting, including a dual-channel input setting and an output setting; the dual-channel input setting specifically uses the IV-OCT image as a first input channel and the catheter position mask information as a second input channel; the output setting specifically uses the artifact-removed image as the generator data output;

[0015] Step S12: multi-scale loss setting, specifically, combining pixel-level L1 loss, adversarial loss and frequency domain consistency loss, performing multi-scale loss weighting setting to obtain a multi-scale loss function;

[0016] Step S13: adaptive contrast enhancement, specifically, performing generative adversarial training through the dual-channel improved generative adversarial network and the multi-scale loss setting and outputting a macrophage enhanced image, dividing the image in the macrophage enhanced image into small blocks of 16×16, and performing a contrast-limited adaptive histogram equalization operation on each small block to obtain a contrast-optimized image;

[0017] Step S14: global intensity normalization, specifically, by controlling the gray value distribution of the entire image of the macrophage enhanced image using a dynamic range compression method during the contrast limited adaptive histogram equalization operation to obtain adaptive contrast enhanced image data;

[0018] Step S15: multimodal preprocessing, specifically, obtaining a macrophage enhanced image through the dual-channel improved generative adversarial network and the multi-scale loss setting, and performing multimodal data preprocessing by combining the adaptive contrast enhancement and the global intensity normalization to obtain macrophage feature enhanced image data;

[0019] The macrophage feature enhanced image data refers to macrophage image data after catheter artifact removal, structure clarification and contrast optimization.

[0020] Further, in step S2, the dynamic feature enhancement is used to construct a biomarker feature that is sensitive to the macrophage infiltration area, specifically, based on the macrophage feature enhanced image data, a normalized standard deviation ratio calculation method combined with partition sampling enhancement is used to perform dynamic feature enhancement to obtain biomarker mapping data, including the following steps:

[0021] Step S21: regional sampling, specifically, extracting the vertical distance from the surface of the vascular endothelium to the pixel point from the macrophage feature enhanced image data, and dividing the image in the macrophage feature enhanced image data into a shallow region and a deep region according to the vertical distance, and setting the sampling region window size for the shallow region and the deep region respectively, and obtaining the macrophage heterogeneous distribution characteristics through radial sampling and axial sampling;

[0022] Step S22: normalized standard deviation ratio calculation definition, specifically, by performing normalized standard deviation calculation based on the first shallow window, the second shallow window, the first deep window and the second deep window, to obtain a reference normalized standard deviation value of the macrophage region;

[0023] The macrophage region reference normalized standard deviation value is used to reflect the local texture fluctuation degree of the region and to indicate the probability of being a macrophage region;

[0024] Step S23: Dynamically weighting the normalized standard deviation ratio, specifically, introducing a dynamic attenuation coefficient on the basis of the normalized standard deviation calculation, performing dynamic weighted calculation, and obtaining a noise-resistant optimized macrophage region reference value;

[0025] Step S24: biomarker map fusion, specifically, fusing the macrophage region reference normalized standard deviation value and the anti-noise optimized macrophage region reference value with pixel values ​​to obtain biomarker map fusion feature data;

[0026] Step S25: Dynamic feature enhancement, specifically, perform dynamic sampling based on the blood vessel position through the sub-region sampling, and enhance the boundary structure and texture changes of the cell image by combining the definition of the normalized standard deviation ratio calculation, the dynamic weighting of the normalized standard deviation ratio, and the fusion of the biomarker mapping map, to obtain biomarker mapping map data;

[0027] Each pixel value of the biomarker mapping map data is used to represent the dynamically weighted fusion feature value of the normalized standard deviation, and the dynamically weighted fusion feature value of the normalized standard deviation is used to reflect the activity degree of macrophage infiltration.

[0028] Furthermore, in step S3, the hybrid deep learning is used to fuse local texture and global features and achieve macrophage segmentation. Specifically, based on the macrophage feature enhanced image data and the biomarker mapping map data, a branch feature extraction network with cross-attention feature fusion is adopted, combined with an enhanced discriminative generative adversarial network, to perform hybrid deep learning macrophage image segmentation, and obtain macrophage segmentation feature data, including the following steps:

[0029] Step S31: Construct a dual-branch feature extraction network. Specifically, cut the feature maps of the macrophage feature enhanced image data and the biomarker mapping map data respectively, and construct a dual-branch feature extraction network to extract macrophage basic feature data;

[0030] The dual-branch feature extraction network includes a local texture extraction branch and a global semantic feature extraction branch;

[0031] Step S32: Cross-attention feature fusion. Specifically, fuse the local texture feature map data and the global feature map data through a gated weighting mechanism to obtain cross-attention fusion feature data;

[0032] Step S33: Discriminative enhancement adversarial training optimization. Specifically, through the dual-branch feature extraction network and the cross-attention feature fusion, construct a cross-dual-branch image segmentation subnet, and use the cross-dual-branch image segmentation subnet as a generator, construct a three-scale enhanced discriminator, and perform adversarial generation training to obtain a discriminative enhanced generative adversarial subnet;

[0033] The three-scale enhanced discriminator includes a local discriminative scale, a medium-scale discriminative scale, and a global discriminative scale;

[0034] Step S34: Hybrid deep learning model training, specifically, constructing a cross-branch image segmentation subnet by constructing a dual-branch feature extraction network and the cross-attention feature fusion, and constructing a discriminant enhancement generation adversarial subnet through the discriminant enhancement adversarial training optimization, and performing subnet integration and hybrid deep learning model training based on the cross-branch image segmentation subnet and the discriminant enhancement generation adversarial subnet to obtain a macrophage segmentation model Model SEG ;

[0035] Step S35: macrophage segmentation, specifically, using the macrophage segmentation model Model according to the biomarker mapping data SEG , perform macrophage segmentation and obtain macrophage segmentation feature data.

[0036] Further, in step S4, the adaptive threshold optimization is used to dynamically improve the optimal segmentation threshold of macrophages, specifically, based on the macrophage segmentation feature data, a differentiable maximum inter-class variance threshold algorithm combined with morphological correction is used to perform adaptive segmentation threshold optimization to obtain adaptively corrected recognition data, including the following steps:

[0037] Step S41: construct a differentiable maximum inter-class variance threshold algorithm, specifically, convert the standard maximum inter-class variance threshold algorithm into a differentiable form to obtain a differentiable maximum inter-class variance threshold algorithm, and embed the differentiable maximum inter-class variance threshold algorithm into the macrophage segmentation model Model in step S3. SEG At the end of , the optimal initial segmentation threshold is calculated;

[0038] Step S42: constructing a morphological correction network, specifically, constructing a lightweight U-shaped network including a 3-layer codec pair as the morphological correction network, predicting the segmentation mask gradient value according to the optimal initial segmentation threshold, and performing morphological correction by constructing an optimized correction loss function to obtain morphologically corrected segmentation threshold data;

[0039] Step S43: constructing a dynamic threshold feedback mechanism, specifically, by counting the number of connected domains and the average area of ​​the segmented area and setting a dynamic detection threshold for isolated areas, making an over-segmentation judgment, and dynamically adjusting the morphologically corrected segmentation threshold data to obtain dynamic feedback optimized segmentation threshold data;

[0040] Step S44: Adaptive threshold optimization, specifically, dynamically adjusting the segmentation threshold of the macrophage segmentation feature data according to the dynamic feedback optimization segmentation threshold data to obtain adaptively corrected recognition data.

[0041] Furthermore, in step S5, the macrophage recognition is used to comprehensively output multiple features to output the final macrophage infiltration area to realize macrophage artificial intelligence recognition, specifically combining the macrophage segmentation feature data and the adaptive correction recognition data to perform macrophage multi-feature adaptive recognition to obtain macrophage comprehensive recognition data.

[0042] The present invention provides a macrophage recognition system based on IV-OCT images, comprising an input processing module, a dynamic enhancement module, a hybrid learning module, a threshold optimization module and a cell recognition module;

[0043] The input processing module is used for multimodal preprocessing, and obtains macrophage feature enhanced image data through multimodal preprocessing, and sends the macrophage feature enhanced image data to the dynamic enhancement module and the hybrid learning module;

[0044] The dynamic enhancement module is used for dynamic feature enhancement, obtains biomarker mapping data through dynamic feature enhancement, and sends the biomarker mapping data to the hybrid learning module;

[0045] The hybrid learning module is used for hybrid deep learning, obtains macrophage segmentation feature data through hybrid deep learning, and sends the macrophage segmentation feature data to the threshold optimization module and the cell recognition module;

[0046] The threshold optimization module is used for adaptive threshold optimization, obtains adaptive correction recognition data through adaptive threshold optimization, and sends the adaptive correction recognition data to the cell recognition module;

[0047] The cell identification module is used for macrophage identification, and through macrophage identification, macrophage comprehensive identification data is obtained.

[0048] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0049] (1) In view of the technical problems that the existing macrophage recognition methods based on IV-OCT images often rely on manual experience and have poor repeatability due to uneven image quality, blurred cell structure and obvious differences in tissue performance between different individuals, this scheme creatively introduces a structure-guided feature separation mechanism based on the significant differences of the recognition object itself, which can more clearly distinguish the difference between the macrophage area and the background tissue, realize more stable and accurate automatic recognition and positioning of macrophages, significantly reduce the necessity of manual intervention, and improve the consistency and efficiency of recognition;

[0050] (2) In view of the technical problems in the existing basic processing of image data, there are problems such as low image contrast and unclear structural hierarchy due to the influence of factors such as blood flow and tissue reflection during the acquisition process of IV-OCT images, which affect the accuracy of subsequent analysis. This solution creatively adopts a multimodal image enhancement method that combines adaptive contrast enhancement and dual-channel improved generative adversarial network for multimodal preprocessing, which achieves intelligent improvement of image quality and true restoration of regional details, providing a clearer and more biologically meaningful image basis for subsequent recognition and analysis;

[0051] (3) In view of the technical problem that in the existing image feature optimization process, due to the complexity of tissue structure and the local distribution characteristics of macrophages, traditional methods cannot effectively capture local change trends, which affects the sensitive identification of micro-cell areas. This solution creatively starts from the characteristics of obvious changes and uneven distribution of local structure of macrophages, and adopts the normalized standard deviation ratio calculation method combined with partition sampling enhancement to perform dynamic feature enhancement, thereby achieving accurate capture of micro-cell changes and improving recognition sensitivity;

[0052] (4) In view of the technical problems in the existing macrophage segmentation process, which are often affected by unclear image layers, blurred tissue boundaries and large heterogeneity, resulting in difficulty in accurately segmenting cell boundaries and easy confusion of adjacent structures, this solution creatively starts from the dual needs of image structure feature expression and cell tissue discrimination ability improvement, adopts a branch feature extraction network with cross-attention feature fusion, combined with an enhanced discriminant generative adversarial network, and performs hybrid deep learning macrophage image segmentation, achieving high-resolution segmentation of macrophage areas and stronger boundary discrimination capabilities, effectively improving the recognition accuracy and segmentation integrity under complex backgrounds;

[0053] (5) In view of the technical problems in the existing segmentation threshold adjustment process, the traditional fixed threshold segmentation method is prone to over- or under-recognition due to the large individual differences in images and the wide range of cell activity variations, and the high cost and strong subjectivity of manual intervention. This scheme creatively adopts the differentiable maximum inter-class variance threshold algorithm combined with morphological correction to perform adaptive segmentation threshold optimization, realizing a more flexible, efficient and robust image segmentation process, and effectively improving the stability and practicality of automatic recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of a process of macrophage identification method based on IV-OCT images provided by the present invention;

[0055] Figure 2 A schematic diagram of a macrophage recognition system based on IV-OCT images provided by the present invention;

[0056] Figure 3 It is a schematic diagram of the process of multimodal preprocessing in step S1;

[0057] Figure 4 A schematic diagram of the process of dynamic feature enhancement in step S2;

[0058] Figure 5 It is a schematic diagram of the process of hybrid deep learning in step S3;

[0059] Figure 6 Schematic diagram of the process of adaptive threshold optimization in step S4.

[0060] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0062] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0063] Example 1, see Figure 1 The present invention provides a macrophage identification method based on IV-OCT images, the method comprising the following steps:

[0064] Step S1: multimodal preprocessing;

[0065] Step S2: dynamic feature enhancement;

[0066] Step S3: Hybrid deep learning;

[0067] Step S4: adaptive threshold optimization;

[0068] Step S5: Macrophage identification.

[0069] By performing the above operations, in view of the technical problems in the existing macrophage identification methods based on IV-OCT images, which exist due to uneven image quality, blurred cell structure and obvious differences in tissue performance between different individuals, the identification results often rely on manual experience and have poor repeatability. This scheme creatively starts from the significant differences in the identification objects themselves and creatively introduces a structure-guided feature separation mechanism, which can more clearly distinguish the difference between the macrophage area and the background tissue, realize more stable and accurate automatic identification and positioning of macrophages, significantly reduce the necessity of manual intervention, and improve the consistency and efficiency of identification.

[0070] Example 2, see Figure 1 , Figure 2 and Figure 3 In step S1, the multimodal preprocessing is used to collect data and enhance macrophage-related signal features in the image processing stage, specifically collecting IV-OCT images and catheter position information, and using a multimodal image enhancement method combining adaptive contrast enhancement and a dual-channel improved generative adversarial network to perform multimodal preprocessing to obtain macrophage feature enhanced image data;

[0071] The IV-OCT image refers to a single-channel grayscale image with a size of 512×512;

[0072] The step of using a multimodal image enhancement method combining adaptive contrast enhancement and a dual-channel improved generative adversarial network to perform multimodal preprocessing to obtain macrophage feature enhanced image data includes:

[0073] Step S11: constructing a dual-channel improved generative adversarial network, specifically by constructing a dual-channel U-shaped network as the generator structure of the adversarial generative network, and performing dual-channel setting and discriminator setting of the generator structure to obtain the dual-channel improved generative adversarial network;

[0074] The generator structure has a dual-channel setting, including a dual-channel input setting and an output setting; the dual-channel input setting specifically uses the IV-OCT image as a first input channel and the catheter position mask information as a second input channel; the output setting specifically uses the artifact-removed image as the generator data output;

[0075] The discriminator setting specifically adopts the standard PatchGAN architecture;

[0076] Step S12: multi-scale loss setting, specifically, combining pixel-level L1 loss, adversarial loss and frequency domain consistency loss, performing multi-scale loss weighting setting to obtain a multi-scale loss function;

[0077] The pixel-level L1 loss is used to preserve image details;

[0078] The adversarial loss is used to improve the realism of the generated image;

[0079] The frequency domain consistency loss is used to perform frequency domain constraints on the frequency domain extracted after the generated image and the real image are processed by wavelet transform, and to retain the strong scattered points related to macrophages and the smooth features of the blood vessel wall;

[0080] The calculation formula of the multi-scale loss function is:

[0081] ;

[0082] In the formula, is the multi-scale loss function, is the pixel-level L1 loss weight, is the pixel-level L1 loss function, is the adversarial loss weight, is the adversarial loss function, is the frequency domain consistency loss weight, K is the frequency domain decomposition layer number of wavelet transform processing, k is the frequency domain decomposition level index, is the frequency domain transformation function of the kth layer, I gen is the generated image, I real is a real image, ||·|| 2 is the L2 norm operator;

[0083] Step S13: adaptive contrast enhancement, specifically, performing generative adversarial training through the dual-channel improved generative adversarial network and the multi-scale loss setting and outputting a macrophage enhanced image, dividing the image in the macrophage enhanced image into small blocks of 16×16, and performing a contrast-limited adaptive histogram equalization operation on each small block to obtain a contrast-optimized image;

[0084] Step S14: global intensity normalization, specifically, by controlling the gray value distribution of the entire image of the macrophage enhanced image using a dynamic range compression method during the contrast limited adaptive histogram equalization operation to obtain adaptive contrast enhanced image data;

[0085] Step S15: multimodal preprocessing, specifically, obtaining a macrophage enhanced image through the dual-channel improved generative adversarial network and the multi-scale loss setting, and performing multimodal data preprocessing by combining the adaptive contrast enhancement and the global intensity normalization to obtain macrophage feature enhanced image data;

[0086] The macrophage feature enhanced image data refers to macrophage image data after catheter artifact removal, structure clarification and contrast optimization.

[0087] By performing the above operations, in order to address the technical problem that in the basic processing of existing image data, IV-OCT images are affected by factors such as blood flow and tissue reflection during the acquisition process, resulting in low image contrast and unclear structural levels, which affects the accuracy of subsequent analysis, this solution creatively adopts a multimodal image enhancement method that combines adaptive contrast enhancement and a dual-channel improved generative adversarial network for multimodal preprocessing, which achieves intelligent improvement of image quality and true restoration of regional details, providing a clearer and more biologically meaningful image basis for subsequent recognition and analysis.

[0088] Example 3, see Figure 1 , Figure 2 and Figure 4 This embodiment is based on the above embodiment. In step S2, the dynamic feature enhancement is used to construct a biomarker feature that is sensitive to the macrophage infiltration area. Specifically, based on the macrophage feature enhancement image data, a normalized standard deviation ratio calculation method combined with partition sampling enhancement is used to perform dynamic feature enhancement to obtain biomarker mapping data, including the following steps:

[0089] Step S21: regional sampling, specifically, extracting the vertical distance from the surface of the vascular endothelium to the pixel point from the macrophage feature enhanced image data, and dividing the image in the macrophage feature enhanced image data into a shallow region and a deep region according to the vertical distance, and setting the sampling region window size for the shallow region and the deep region respectively, and obtaining the macrophage heterogeneous distribution characteristics through radial sampling and axial sampling;

[0090] The shallow area specifically refers to an area with a vertical distance less than or equal to 1 mm. The sampling area window of the shallow area includes a first shallow window and a second shallow window. The size of the first shallow window is 7×7, and the size of the second shallow window is 5×5.

[0091] The deep region specifically refers to a region with a vertical distance greater than 1 mm. The sampling region window of the deep region includes a first deep window and a second deep window. The size of the first deep window is 5×5, and the size of the second deep window is 3×3.

[0092] The vertical distance from the vascular endothelium surface to the pixel point is extracted by calculating the distance from the center point of the blood vessel to the outward normal line to estimate the vertical distance;

[0093] Step S22: normalized standard deviation ratio calculation definition, specifically, by performing normalized standard deviation calculation based on the first shallow window, the second shallow window, the first deep window and the second deep window, to obtain a reference normalized standard deviation value of the macrophage region;

[0094] The macrophage region reference normalized standard deviation value is used to reflect the local texture fluctuation degree of the region and to indicate the probability of being a macrophage region;

[0095] Step S23: Dynamically weighting the normalized standard deviation ratio, specifically, introducing a dynamic attenuation coefficient on the basis of the normalized standard deviation calculation, performing dynamic weighted calculation, and obtaining a noise-resistant optimized macrophage region reference value;

[0096] The calculation formula of the dynamic attenuation coefficient is:

[0097] ;

[0098] In the formula, is the dynamic attenuation coefficient, c is the slope parameter, NSD background is the normalized standard deviation ratio of the vascular wall background area, is the attenuation threshold;

[0099] Step S24: biomarker map fusion, specifically, fusing the macrophage region reference normalized standard deviation value and the anti-noise optimized macrophage region reference value with pixel values ​​to obtain biomarker map fusion feature data;

[0100] Step S25: Dynamic feature enhancement, specifically, performing dynamic sampling based on the blood vessel position through the sub-region sampling, and enhancing the boundary structure and texture change of the cell image by combining the normalized standard deviation ratio calculation definition, the normalized standard deviation ratio dynamic weighting and the biomarker map fusion to obtain biomarker map data;

[0101] Each pixel value of the biomarker mapping data is used to represent a dynamic weighted fusion feature value of a normalized standard deviation, and the dynamic weighted fusion feature value of the normalized standard deviation is used to reflect the activity level of macrophage infiltration.

[0102] By performing the above operations, in order to address the technical problem that in the existing image feature optimization process, due to the complexity of tissue structure and the local distribution characteristics of macrophages, traditional methods cannot effectively capture local change trends, affecting the sensitive identification of micro-cell areas, this solution creatively starts from the characteristics of obvious changes and uneven distribution of local structure of macrophages, and adopts the normalized standard deviation ratio calculation method combined with partition sampling enhancement to perform dynamic feature enhancement, thereby achieving accurate capture of micro-cell changes and improved recognition sensitivity.

[0103] Example 4, see Figure 1 , Figure 2 and Figure 5This embodiment is based on the above embodiment. In step S3, the hybrid deep learning is used to fuse local texture and global features and realize macrophage segmentation. Specifically, based on the macrophage feature enhanced image data and the biomarker mapping data, a branch feature extraction network with cross-attention feature fusion is used, combined with an enhanced discriminant generative adversarial network, to perform hybrid deep learning macrophage image segmentation to obtain macrophage segmentation feature data, including the following steps:

[0104] Step S31: constructing a dual-branch feature extraction network, specifically, performing feature map cutting on the macrophage feature enhanced image data and the biomarker mapping map data respectively, and constructing a dual-branch feature extraction network to extract the basic feature data of macrophages;

[0105] The dual-branch feature extraction network includes a local texture extraction branch and a global semantic feature extraction branch;

[0106] The local texture extraction branch specifically adopts a lightweight 3D ResNet-18 network, replaces the standard convolution layer with a three-dimensional convolution layer, and groups the macrophage feature enhanced image data after feature map cutting into three frames of view as data input of the local texture extraction branch, thereby extracting local texture feature map data;

[0107] The global semantic feature extraction branch specifically adopts a Swin transformer structure, sets the segmentation input to a fixed window of 7×7, and performs global feature extraction based on the segmented biomarker mapping data to obtain global feature map data;

[0108] The feature image cutting specifically refers to cutting the image in the macrophage feature enhanced image data and the biomarker mapping image data into a size of 224×224;

[0109] Step S32: cross-attention feature fusion, specifically, fusing the local texture feature map data and the global feature map data through a gated weighting mechanism to obtain cross-attention fusion feature data;

[0110] The calculation formula of the cross attention fusion feature data is:

[0111] ;

[0112] In the formula, F fused is the cross-attention fusion feature data, is the S-type activation function, W 1 is the local branch weight, W 2 is the global semantic branch weight, F 3D is the local texture feature map data, F Transformeris the global feature map data, is the element-wise addition operator;

[0113] Step S33: discriminant enhancement adversarial training optimization, specifically, constructing a cross-double-branch image segmentation subnet by fusing the double-branch feature extraction network and the cross-attention feature, and using the cross-double-branch image segmentation subnet as a generator to construct a three-scale enhanced discriminator, perform adversarial generation training, and obtain a discriminant enhancement generation adversarial subnet;

[0114] The three-scale enhanced discriminator includes a local discriminant scale, a mesoscale discriminant scale and a global discriminant scale;

[0115] The local discrimination scale specifically sets the receptive range to 70×70; the mesoscale discrimination scale specifically sets the receptive range to 140×140; the global discrimination scale specifically sets the receptive range to 224×224;

[0116] The discriminative enhancement generative adversarial subnet specifically adopts a multi-scale adversarial loss function for optimization training;

[0117] The multi-scale adversarial loss function includes a basic segmentation loss and a scale loss; the scale loss includes a local discriminant scale loss, a mid-scale discriminant scale loss, and a global discriminant scale loss; the basic segmentation loss specifically adopts a combination of a standard Dice loss and a standard cross entropy loss; the scale loss specifically adopts a standard adversarial loss;

[0118] The multi-scale adversarial loss function is obtained by weighted combination of the basic segmentation loss and the scale loss;

[0119] Step S34: Hybrid deep learning model training, specifically, constructing a cross-branch image segmentation subnet by constructing a dual-branch feature extraction network and the cross-attention feature fusion, and constructing a discriminant enhancement generation adversarial subnet through the discriminant enhancement adversarial training optimization, and performing subnet integration and hybrid deep learning model training based on the cross-branch image segmentation subnet and the discriminant enhancement generation adversarial subnet to obtain a macrophage segmentation model Model SEG ;

[0120] Step S35: macrophage segmentation, specifically, using the macrophage segmentation model Model according to the biomarker mapping data SEG , perform macrophage segmentation and obtain macrophage segmentation feature data.

[0121] By executing the above operations, in order to address the technical problems in the existing macrophage segmentation process, which are often affected by unclear image layers, blurred tissue boundaries and large heterogeneity, resulting in difficulty in accurately segmenting cell boundaries and easy confusion of adjacent structures, this solution creatively starts from the dual needs of image structure feature expression and cell tissue discrimination ability improvement, adopts a branch feature extraction network with cross-attention feature fusion, combined with an enhanced discriminant generative adversarial network, to perform hybrid deep learning macrophage image segmentation, achieving high-resolution segmentation of macrophage areas and stronger boundary discrimination capabilities, effectively improving the recognition accuracy and segmentation integrity under complex backgrounds.

[0122] Example 5, see Figure 1 , Figure 2 and Figure 6 This embodiment is based on the above embodiment. In step S4, the adaptive threshold optimization is used to dynamically improve the optimal segmentation threshold of macrophages. Specifically, based on the macrophage segmentation feature data, a differentiable maximum inter-class variance threshold algorithm combined with morphological correction is used to perform adaptive segmentation threshold optimization to obtain adaptively corrected recognition data, including the following steps:

[0123] Step S41: construct a differentiable maximum inter-class variance threshold algorithm, specifically, convert the standard maximum inter-class variance threshold algorithm into a differentiable form to obtain a differentiable maximum inter-class variance threshold algorithm, and embed the differentiable maximum inter-class variance threshold algorithm into the macrophage segmentation model Model in step S3. SEG At the end of , the optimal initial segmentation threshold is calculated;

[0124] Step S42: constructing a morphological correction network, specifically, constructing a lightweight U-shaped network including a 3-layer codec pair as the morphological correction network, predicting the segmentation mask gradient value according to the optimal initial segmentation threshold, and performing morphological correction by constructing an optimized correction loss function to obtain morphologically corrected segmentation threshold data;

[0125] The optimized correction loss function includes a standard segmentation loss and a boundary smoothing loss; the standard segmentation loss specifically adopts a standard Dice loss, and the boundary smoothing loss specifically adopts an L1 norm loss of the segmentation mask gradient value;

[0126] The calculation formula of the optimized modified loss function is:

[0127] ;

[0128] In the formula, is the optimization correction loss function, Dice(·) is the Dice similarity coefficient loss function, Y pred is the predicted segmentation result, Ypt is the true segmentation annotation, is the regularization weight coefficient, the specific value is 0.5, is the gradient operator, ||·|| 1 is the L1 norm operator;

[0129] Step S43: constructing a dynamic threshold feedback mechanism, specifically, by counting the number of connected domains and the average area of ​​the segmented area and setting a dynamic detection threshold for isolated areas, making an over-segmentation judgment, and dynamically adjusting the morphologically corrected segmentation threshold data to obtain dynamic feedback optimized segmentation threshold data;

[0130] Step S44: Adaptive threshold optimization, specifically, dynamically adjusting the segmentation threshold of the macrophage segmentation feature data according to the dynamic feedback optimization segmentation threshold data to obtain adaptively corrected recognition data.

[0131] By performing the above operations, in order to address the technical problems in the existing segmentation threshold adjustment process, such as the large individual differences in images and the wide range of cell activity variations, the traditional fixed threshold segmentation method is prone to over- or under-recognition, and the manual intervention cost is high and the subjectivity is strong, this scheme creatively adopts the differentiable maximum inter-class variance threshold algorithm combined with morphological correction to perform adaptive segmentation threshold optimization, thereby achieving a more flexible, efficient and robust image segmentation process, and effectively improving the stability and practicality of automatic recognition.

[0132] Example 6, see Figure 1 , Figure 2 This embodiment is based on the above embodiment. In step S5, the macrophage recognition is used to comprehensively output multiple features to output the final macrophage infiltration area to realize macrophage artificial intelligence recognition. Specifically, the macrophage segmentation feature data and the adaptive correction recognition data are combined to perform macrophage multi-feature adaptive recognition to obtain macrophage comprehensive recognition data.

[0133] Embodiment 7, see Figure 1 and Figure 2 , this embodiment is based on the above embodiment, and the present invention provides a macrophage recognition system based on IV-OCT images, including an input processing module, a dynamic enhancement module, a hybrid learning module, a threshold optimization module and a cell recognition module;

[0134] The input processing module is used for multimodal preprocessing, and obtains macrophage feature enhanced image data through multimodal preprocessing, and sends the macrophage feature enhanced image data to the dynamic enhancement module and the hybrid learning module;

[0135] The dynamic enhancement module is used for dynamic feature enhancement, obtains biomarker mapping data through dynamic feature enhancement, and sends the biomarker mapping data to the hybrid learning module;

[0136] The hybrid learning module is used for hybrid deep learning, obtains macrophage segmentation feature data through hybrid deep learning, and sends the macrophage segmentation feature data to the threshold optimization module and the cell recognition module;

[0137] The threshold optimization module is used for adaptive threshold optimization, obtains adaptive correction recognition data through adaptive threshold optimization, and sends the adaptive correction recognition data to the cell recognition module;

[0138] The cell identification module is used for macrophage identification, and through macrophage identification, macrophage comprehensive identification data is obtained.

[0139] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0140] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0141] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A macrophage identification method based on IV-OCT images, characterized in that: The method comprises the following steps: Step S1: multimodal preprocessing, using a multimodal image enhancement method combining adaptive contrast enhancement and a dual-channel improved generative adversarial network to perform multimodal preprocessing to obtain macrophage feature enhanced image data, including the following steps: Step S11: constructing a dual-channel improved generative adversarial network, taking the IV-OCT image as the first input channel and the catheter position mask information as the second input channel; Step S12: multi-scale loss setting, combining pixel-level L1 loss, adversarial loss and frequency domain consistency loss to perform multi-scale loss weighting setting; Step S13: adaptive contrast enhancement; Step S14: global intensity normalization; Step S15: multimodal preprocessing; Step S2: Dynamic feature enhancement, using a normalized standard deviation ratio calculation method combined with partition sampling enhancement to perform dynamic feature enhancement to obtain biomarker mapping data; Step S3: hybrid deep learning, using a branch feature extraction network with cross-attention feature fusion, combined with an enhanced discriminant generative adversarial network, to perform hybrid deep learning macrophage image segmentation to obtain macrophage segmentation feature data, including the following steps: step S31: constructing a dual-branch feature extraction network; step S32: cross-attention feature fusion; step S33: discriminant enhanced adversarial training optimization; step S34: hybrid deep learning model training; step S35: macrophage segmentation; Step S4: adaptive threshold optimization to obtain adaptively corrected recognition data; Step S5: macrophage identification, obtaining comprehensive macrophage identification data.

2. The method for macrophage identification based on IV-OCT images according to claim 1, characterized in that: In step S1, the multimodal preprocessing is used to collect data and enhance macrophage-related signal features in the image processing stage, specifically to collect IV-OCT images and catheter position information, and use a multimodal image enhancement method combining adaptive contrast enhancement and a dual-channel improved generative adversarial network to perform multimodal preprocessing to obtain macrophage feature enhanced image data.

3. The method for macrophage identification based on IV-OCT images according to claim 2, characterized in that: In step S1, the multimodal image enhancement method combining adaptive contrast enhancement and dual-channel improved generative adversarial network is used to perform multimodal preprocessing to obtain macrophage feature enhanced image data, including: Step S11: constructing a dual-channel improved generative adversarial network, specifically by constructing a dual-channel U-shaped network as the generator structure of the adversarial generative network, and performing dual-channel setting and discriminator setting of the generator structure to obtain the dual-channel improved generative adversarial network; The generator structure has a dual-channel setting, including a dual-channel input setting and an output setting; the dual-channel input setting specifically uses the IV-OCT image as a first input channel and the catheter position mask information as a second input channel; the output setting specifically uses the artifact-removed image as the generator data output; Step S12: multi-scale loss setting, specifically, combining pixel-level L1 loss, adversarial loss and frequency domain consistency loss, performing multi-scale loss weighting setting to obtain a multi-scale loss function; The pixel-level L1 loss is used to preserve image details; The adversarial loss is used to improve the realism of the generated image; The frequency domain consistency loss is used to perform frequency domain constraints on the frequency domain extracted after the generated image and the real image are processed by wavelet transform, and to retain the strong scattered points related to macrophages and the smooth features of the blood vessel wall; Step S13: adaptive contrast enhancement, specifically, performing generative adversarial training through the dual-channel improved generative adversarial network and the multi-scale loss setting and outputting a macrophage enhanced image, dividing the image in the macrophage enhanced image into small blocks of 16×16, and performing a contrast-limited adaptive histogram equalization operation on each small block to obtain a contrast-optimized image; Step S14: global intensity normalization, specifically, by controlling the gray value distribution of the entire image of the macrophage enhanced image using a dynamic range compression method during the contrast limited adaptive histogram equalization operation to obtain adaptive contrast enhanced image data; Step S15: multimodal preprocessing, specifically, obtaining a macrophage enhanced image through the dual-channel improved generative adversarial network and the multi-scale loss setting, and performing multimodal data preprocessing by combining the adaptive contrast enhancement and the global intensity normalization to obtain macrophage feature enhanced image data; The macrophage feature enhanced image data refers to macrophage image data after catheter artifact removal, structure clarification and contrast optimization.

4. The method for macrophage identification based on IV-OCT images according to claim 3, characterized in that: In step S2, the dynamic feature enhancement is used to construct a biomarker feature that is sensitive to the macrophage infiltration area. Specifically, based on the macrophage feature enhanced image data, a normalized standard deviation ratio calculation method combined with partition sampling enhancement is used to perform dynamic feature enhancement to obtain biomarker mapping data, including the following steps: Step S21: regional sampling, specifically, extracting the vertical distance from the surface of the vascular endothelium to the pixel point from the macrophage feature enhanced image data, and dividing the image in the macrophage feature enhanced image data into a shallow region and a deep region according to the vertical distance, and setting the sampling region window size for the shallow region and the deep region respectively, and obtaining the macrophage heterogeneous distribution characteristics through radial sampling and axial sampling; The shallow area specifically refers to an area with a vertical distance less than or equal to 1 mm. The sampling area window of the shallow area includes a first shallow window and a second shallow window. The size of the first shallow window is 7×7, and the size of the second shallow window is 5×5. The deep region specifically refers to a region with a vertical distance greater than 1 mm. The sampling region window of the deep region includes a first deep window and a second deep window. The size of the first deep window is 5×5, and the size of the second deep window is 3×3. Step S22: normalized standard deviation ratio calculation definition, specifically, by performing normalized standard deviation calculation based on the first shallow window, the second shallow window, the first deep window and the second deep window, to obtain a reference normalized standard deviation value of the macrophage region; The macrophage region reference normalized standard deviation value is used to reflect the local texture fluctuation degree of the region and to indicate the probability of being a macrophage region; Step S23: Dynamically weighting the normalized standard deviation ratio, specifically, introducing a dynamic attenuation coefficient on the basis of the normalized standard deviation calculation, performing dynamic weighted calculation, and obtaining a noise-resistant optimized macrophage region reference value; Step S24: biomarker map fusion, specifically, fusing the macrophage region reference normalized standard deviation value and the anti-noise optimized macrophage region reference value with pixel values ​​to obtain biomarker map fusion feature data; Step S25: Dynamic feature enhancement, specifically, performing dynamic sampling based on the blood vessel position through the sub-region sampling, and enhancing the boundary structure and texture change of the cell image by combining the normalized standard deviation ratio calculation definition, the normalized standard deviation ratio dynamic weighting and the biomarker map fusion to obtain biomarker map data; Each pixel value of the biomarker mapping data is used to represent a dynamic weighted fusion feature value of a normalized standard deviation, and the dynamic weighted fusion feature value of the normalized standard deviation is used to reflect the activity level of macrophage infiltration.

5. The method for macrophage identification based on IV-OCT images according to claim 4, characterized in that: In step S3, the hybrid deep learning is used to fuse local texture and global features and realize macrophage segmentation. Specifically, based on the macrophage feature enhanced image data and the biomarker mapping data, a branch feature extraction network with cross-attention feature fusion is used, combined with an enhanced discriminant generative adversarial network, to perform hybrid deep learning macrophage image segmentation to obtain macrophage segmentation feature data, including the following steps: Step S31: constructing a dual-branch feature extraction network, specifically, performing feature map cutting on the macrophage feature enhanced image data and the biomarker mapping map data respectively, and constructing a dual-branch feature extraction network to extract the basic feature data of macrophages; The dual-branch feature extraction network includes a local texture extraction branch and a global semantic feature extraction branch; Step S32: cross-attention feature fusion, specifically, fusing the local texture feature map data and the global feature map data through a gated weighting mechanism to obtain cross-attention fusion feature data; Step S33: discriminant enhancement adversarial training optimization, specifically, constructing a cross-double-branch image segmentation subnet by fusing the double-branch feature extraction network and the cross-attention feature, and using the cross-double-branch image segmentation subnet as a generator to construct a three-scale enhanced discriminator, perform adversarial generation training, and obtain a discriminant enhancement generation adversarial subnet; The three-scale enhanced discriminator includes a local discriminant scale, a mesoscale discriminant scale and a global discriminant scale; The discriminative enhancement generative adversarial subnet specifically adopts a multi-scale adversarial loss function for optimization training; The multi-scale adversarial loss function includes a basic segmentation loss and a scale loss; the scale loss includes a local discriminant scale loss, a mid-scale discriminant scale loss, and a global discriminant scale loss; the basic segmentation loss specifically adopts a combination of a standard Dice loss and a standard cross entropy loss; the scale loss specifically adopts a standard adversarial loss; Step S34: Hybrid deep learning model training, specifically, constructing a cross-branch image segmentation subnet by constructing a dual-branch feature extraction network and the cross-attention feature fusion, and constructing a discriminant enhancement generation adversarial subnet through the discriminant enhancement adversarial training optimization, and performing subnet integration and hybrid deep learning model training based on the cross-branch image segmentation subnet and the discriminant enhancement generation adversarial subnet to obtain a macrophage segmentation model Model SEG ; Step S35: macrophage segmentation, specifically, using the macrophage segmentation model Model according to the biomarker mapping data SEG , perform macrophage segmentation and obtain macrophage segmentation feature data.

6. The method for macrophage identification based on IV-OCT images according to claim 5, characterized in that: In step S4, the adaptive threshold optimization is used to dynamically improve the optimal segmentation threshold of macrophages. Specifically, based on the macrophage segmentation feature data, a differentiable maximum inter-class variance threshold algorithm combined with morphological correction is used to perform adaptive segmentation threshold optimization to obtain adaptively corrected recognition data, including the following steps: Step S41: construct a differentiable maximum inter-class variance threshold algorithm, specifically, convert the standard maximum inter-class variance threshold algorithm into a differentiable form to obtain a differentiable maximum inter-class variance threshold algorithm, and embed the differentiable maximum inter-class variance threshold algorithm into the macrophage segmentation model Model in step S3. SEG At the end of , the optimal initial segmentation threshold is calculated; Step S42: constructing a morphological correction network, specifically, constructing a lightweight U-shaped network including a 3-layer codec pair as the morphological correction network, predicting the segmentation mask gradient value according to the optimal initial segmentation threshold, and performing morphological correction by constructing an optimized correction loss function to obtain morphologically corrected segmentation threshold data; Step S43: constructing a dynamic threshold feedback mechanism, specifically, by counting the number of connected domains and the average area of ​​the segmented area and setting a dynamic detection threshold for isolated areas, making an over-segmentation judgment, and dynamically adjusting the morphologically corrected segmentation threshold data to obtain dynamic feedback optimized segmentation threshold data; Step S44: Adaptive threshold optimization, specifically, dynamically adjusting the segmentation threshold of the macrophage segmentation feature data according to the dynamic feedback optimization segmentation threshold data to obtain adaptively corrected recognition data.

7. The method for macrophage identification based on IV-OCT images according to claim 6, characterized in that: In step S5, the macrophage recognition is used to output the final macrophage infiltration area through comprehensive multi-features to realize macrophage artificial intelligence recognition, specifically, combining the macrophage segmentation feature data and the adaptive correction recognition data to perform macrophage multi-feature adaptive recognition to obtain macrophage comprehensive recognition data.

8. A macrophage recognition system based on IV-OCT images, used to implement a macrophage recognition method based on IV-OCT images as claimed in any one of claims 1 to 7, characterized in that: It includes an input processing module, a dynamic enhancement module, a hybrid learning module, a threshold optimization module and a cell recognition module.

9. The macrophage identification system based on IV-OCT images according to claim 8, characterized in that: The input processing module is used for multimodal preprocessing, and obtains macrophage feature enhanced image data through multimodal preprocessing, and sends the macrophage feature enhanced image data to the dynamic enhancement module and the hybrid learning module; The dynamic enhancement module is used for dynamic feature enhancement, obtains biomarker mapping data through dynamic feature enhancement, and sends the biomarker mapping data to the hybrid learning module; The hybrid learning module is used for hybrid deep learning, obtains macrophage segmentation feature data through hybrid deep learning, and sends the macrophage segmentation feature data to the threshold optimization module and the cell recognition module; The threshold optimization module is used for adaptive threshold optimization, obtains adaptive correction recognition data through adaptive threshold optimization, and sends the adaptive correction recognition data to the cell recognition module; The cell identification module is used for macrophage identification, and through macrophage identification, comprehensive macrophage identification data is obtained.

Images