Mass spectrum imaging space super-resolution reconstruction method and system based on label propagation network

Abstract

The invention discloses a mass spectrum imaging space super-resolution reconstruction method and system based on a label propagation network, and the method comprises the steps: carrying out the preprocessing of MSI data, and obtaining the low-dimensional representation of the MSI data; representing the low dimension with the Hamp; e, registering the dyed image; for the Hamp; e, performing spatial segmentation on the dyed image to obtain a plurality of image blocks, and constructing a label propagation network for each image block; taking the MSI data as a label of a matched node in the network, propagating the MSI data along the network, and updating the label of the network node at the same time; and fusing the node labels with the statistics-based features, constructing a regression model to calculate the features of each superpixel, and realizing the spatial super-resolution reconstruction of the MSI. According to the invention, the Hamp is fused in the MSI; the method comprises the following steps: according to structural information of an E image, realizing global multi-modal registration through local affine transformation registration, and performing Hamp; and the E dyeing microscopic image is replaced by a high-resolution medical image of other modals, so that mass spectrum imaging super-resolution reconstruction driven by other modals can be realized, and the MSI spatial resolution is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a mass spectrometry imaging spatial super-resolution reconstruction method and system based on a label propagation network. Background Art

[0002] Spatial metabolomics is a field of omics research that focuses on the detection and interpretation of metabolites, lipids, drugs, and other small molecules in the spatial context of cells, tissues, and organs. Spatial metabolomics is a rapidly emerging field that involves many biomedical issues, including tumor molecular microenvironment, early developmental regulation, metabolic disorders during infection and inflammation, etc. This growing research demand has promoted the rapid development of mass spectrometry imaging (MSI). MSI is a high-throughput imaging technology used for the identification, quantification, and distribution of proteins, lipids, and chemical metabolites in single cells and complex multicellular tissues. Mass spectrometry imaging plays a key role in the field of metabolomics because it ensures high sensitivity and requires minimal sample preparation, thereby minimizing the loss of analytes. At the same time, mass spectrometry imaging can be used to analyze cells in situ, which not only reduces the interference with cells in traditional metabolomics, but also links molecular information with spatial context, such as cell morphology information or cell-cell interactions. However, the high spatial resolution (~10μm) and point-by-point acquisition strategy of mass spectrometry imaging make the MSI experiment last too long, which can easily lead to adverse effects such as sample deterioration. Super-resolution is an effective way to solve this problem. At present, there are two major methods to improve the spatial resolution of MSI. One is based on hardware technology. For example, Yu Jiancheng et al. proposed a super-resolution biomolecular mass spectrometry imaging device and its working method. They changed the inherent framework of the existing dot-matrix optical scanning type based on the mass spectrometry imaging technology of the coded aperture and mass spectrometer to achieve the super-resolution imaging goal; Gu Zhongze et al. proposed a high spatial resolution mass spectrometry imaging device, which makes it easier to obtain mass spectrometry imaging with higher spatial resolution; Zhao Weiqian et al. proposed a high spatial resolution laser dual-axis differential confocal spectroscopy-mass spectrometry microscopy imaging method and device, which provides a new method for obtaining high spatial resolution MSI. The second is based on algorithmic technology. For example, Professor Dong Jiyang and others built a super-resolution reconstruction model based on a deep neural network, used H&E staining images as reference images, and achieved the MSI super-resolution reconstruction goal through automatic registration and self-supervised learning; Zhang Xiaozhe and others proposed a mass spectrometry image super-resolution reconstruction method based on deep learning, which broke through the limitations of hardware methods on image resolution and reduced experimental costs. Compared with improving the spatial resolution of MSI through hardware technology, algorithmic technology is not limited by physical conditions and economic costs, and has gradually become a practical and efficient technical means in MSI research.

[0003] Since MSI data contains less spatial information, it is usually hoped that the spatial resolution of MSI images can be improved from the perspective of image reconstruction by learning from the fine cell structures of other high-resolution medical images. Hematoxylin-eosin stain (H&E) has become a standard stain for human histological examinations. This staining technique can reveal fine cellular details and infer ultrastructural features to a certain extent. Hematoxylin dye is alkaline and can dye basophilic structures such as RNA, nucleus, and ribosomes in the cytoplasm into blue-purple; eosin dye is acidic and can dye eosinophilic structures in tissues into pink, such as intracellular and intercellular proteins, including most of the cytoplasmic tissue. Well-stained sections can observe a large number of intracellular details under a light microscope. H&E stained images have rich tissue structures and high spatial resolution.

[0004] After the tissue sections for MSI are prepared, they are placed in the mass spectrometer and analyzed using a pre-defined position array. The data is recorded as a continuous list of mass spectra, each of which has a position pixel associated with it. Typically, the mass spectrometry imaging of a single tissue section includes tens of thousands of pixels, each of which is described by a mass spectrum, so the MSI data set is very large. Higher spatial resolution means more small-size pixels, which increases the data acquisition time and the data load; at the same time, the amount of sample that can be excited in a smaller pixel is less, so the ion signal is weaker. From the perspective of analytical chemistry, at higher spatial resolution, fewer ions are detected above the quantitative limit, so there is more noise in the data set. Since the MSI data set has the characteristics of high dimensionality and high complexity, the data dimension needs to be simplified first to facilitate subsequent analysis. MSI data dimensionality reduction is essentially a feature extraction process, which is feasible for global spatial features, but may ignore some local spatial information. In addition, in mass spectrometry imaging, Goodwin et al. have shown that factors such as the storage time of tissue sections at room temperature after sectioning and multiple freeze-thaw tissues have a significant impact on the MSI data sets of peptides, proteins, and drugs. Therefore, various factors will introduce system noise, causing errors in the MSI measured and reduced-dimensional data relative to the real data. The characteristics of the two types of data lead to lower spatial resolution of MSI data relative to H&E stained images. Therefore, researchers can improve the spatial resolution of MSI by combining the texture structure of H&E stained images as external reference information, which is not only beneficial for medical observation, but also helps to analyze the pathological information contained in MSI, which is of great significance for clinical diagnosis and disease treatment. Summary of the invention

[0005] The purpose of the present invention is to address the defects and shortcomings in the prior art and to propose a mass spectrometry imaging spatial super-resolution reconstruction method and system based on a label propagation network, which can improve the spatial resolution of MSI.

[0006] The technical solution of the present invention is as follows.

[0007] On the one hand, a mass spectrometry imaging spatial super-resolution reconstruction method based on a label propagation network comprises:

[0008] MSI data preprocessing step: preprocessing the original mass spectrometry imaging MSI data to obtain a three-dimensional MSI data matrix X; reducing the spectral data of the data matrix δ to obtain a low-dimensional representation E of the MSI data;

[0009] In the image registration step, the low-dimensional representation E of the MSI data is preliminarily aligned with the H&E stained microscopic image H by using rigid body transformation and image cropping to obtain a roughly registered MSI image; the spatial resolution of E is upsampled to the resolution of the H&E stained microscopic image, and the upsampled MSI data is accurately registered with the H&E stained image H by local affine transformation to obtain the registered MSI upsampled data matrix R;

[0010] The label propagation network construction step is to perform spatial segmentation on the H&E stained image H to obtain multiple image blocks; a label propagation network G is constructed for each image block, the nodes of the network are the image pixels of the H&E stained image H, and the connecting edges of the network represent the neighborhood structure similarity of two corresponding pixels;

[0011] The label generation and propagation step, in each label propagation network G, the MSI upsampled data matrix R is used as the label of the corresponding node in the label propagation network G, and the known label is propagated along the connection edge in the network G to the node with unknown label, and the node state of the network G is iteratively updated as the MSI feature of each node;

[0012] In the MSI super-resolution reconstruction step, the statistical features of each pixel of the H&E staining image H are extracted, and the statistical features are fused with the MSI features of the corresponding nodes of the label propagation network to form a feature vector; a regression relationship between the MSI data and the feature vector is established to reconstruct the MSI image S with high spatial resolution.

[0013] Preferably, the MSI data preprocessing step specifically includes:

[0014] Baseline correction, peak alignment, normalization and peak extraction are performed on the mass spectrometry signal of each pixel point to obtain an MSI ion image;

[0015] The MSI ion image is subjected to unsupervised compression and dimensionality reduction to obtain the MSI dimensionality reduction data E of the tissue section to be analyzed.

[0016] Preferably, the image registration step specifically includes:

[0017] The low-dimensional representation E of the MSI data is upsampled to obtain an MSI data matrix with the same spatial resolution as the H&E stained microscopic image H;

[0018] Extract the feature points of the H&E staining image H and the upsampled MSI data matrix and pair them. Any three adjacent feature points constitute an image patch.

[0019] The H&E stained image H and the corresponding image patches of the upsampled MSI data matrix are registered one by one by affine transformation to obtain the registered MSI upsampled data matrix R.

[0020] Preferably, the label propagation network construction step specifically includes:

[0021] Perform spatial segmentation on the H&E stained image H to obtain multiple image blocks; construct a label propagation network G for each image block, and the nodes of the network correspond to the pixels of the H&E stained image H;

[0022] The H&E staining value of the local area of ​​the pixel is used as the feature vector, the structural similarity between two pixels is calculated, and it is used as the connection edge weight between the corresponding nodes to obtain the label propagation network G.

[0023] Preferably, the steps of generating and propagating the label specifically include:

[0024] The MSI upsampled data matrix R is used as the label of the corresponding node in the label propagation network G, and the label is propagated along the connection edge in the network G to update the status of other nodes;

[0025] Combining the propagation results of each label propagation network, the MSI characteristics of each node are obtained.

[0026] Preferably, the MSI super-resolution reconstruction step specifically includes:

[0027] A variety of feature extraction methods are used to obtain the statistical features of each pixel in the H&E staining image H, and the statistical features are fused with the MSI features of the corresponding nodes of the label propagation network to form the fused features of each pixel;

[0028] A regression model is constructed between the fusion features of the pixels and their MSI features to predict the MSI data of the upsampled pixels and obtain the super-resolution reconstructed MSI image S.

[0029] On the other hand, a mass spectrometry imaging spatial super-resolution reconstruction system based on a label propagation network comprises:

[0030] The MSI data preprocessing module is used to preprocess the original mass spectrometry imaging MSI data to obtain a three-dimensional MSI data matrix X; reduce the spectral data of the data matrix X to obtain a low-dimensional representation E of the MSI data;

[0031] The image registration module is used to preliminarily align the low-dimensional representation E of the MSI data with the H&E staining microscopic image H by using rigid body transformation and image cropping to obtain a roughly registered MSI image; upsample the spatial resolution of E to the resolution of the H&E staining microscopic image, and accurately align the upsampled MSI data with the H&E staining image H by local affine transformation to obtain the registered MSI upsampled data matrix R;

[0032] The label propagation network construction module is used to perform spatial segmentation on the H&E staining image H to obtain multiple image blocks; a label propagation network G is constructed for each image block, the nodes of the network are the image pixels of the H&E staining image H, and the connecting edges of the network represent the neighborhood structure similarity of two corresponding pixels;

[0033] The label generation and propagation module is used to use the MSI upsampled data matrix R as the label of the corresponding node in each label propagation network G, and propagate the known label along the connection edge in the network G to the node with unknown label, and iteratively update the node state of the network G as the MSI feature of each node;

[0034] The MSI super-resolution reconstruction module is used to extract the statistical features of each pixel of the H&E staining image H, and fuse the statistical features with the MSI features of the corresponding nodes of the label propagation network to form a feature vector; establish a regression relationship between the MSI data and the feature vector, and reconstruct a high spatial resolution MSI image S.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] (1) The present invention increases the linear and nonlinear information of the H&E image pixels themselves and the surrounding pixels, and guides the propagation of MSI pixel intensity features with the local texture structure information of the high-resolution modality H&E staining image, so that the MSI pixel intensity features are integrated into the local texture structure of the H&E staining image, improving the image perception quality of low-resolution modalities such as MSI, and avoiding the defect of the upsampling technology that its own information is insufficient to achieve super-resolution reconstruction;

[0037] (2) The present invention realizes registration through a patch-based registration method, which alleviates the problem that the linear registration method cannot cope with slice distortion and is closer to the deformation restoration scene in the real world;

[0038] (3) Replacing H&E stained images with high-resolution medical images of other modalities can achieve super-resolution reconstruction of low-resolution modality images driven by other modalities and improve the spatial resolution of MSI. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 It is a flow chart of a method for spatial super-resolution reconstruction of mass spectrometry imaging based on a label propagation network according to an embodiment of the present invention;

[0041] Figure 2 It is a flowchart of a method for spatial super-resolution reconstruction of mass spectrometry imaging based on a label propagation network according to an embodiment of the present invention;

[0042] Figure 3 The embodiment of the present invention is to reduce the dimension of low-resolution MSI data E h×v×3 The super-resolution reconstruction image after implementing the present invention and the comparison image with the results of the common bicubic interpolation upsampling method and Plas method;

[0043] Figure 4 This is a structural diagram of a mass spectrometry imaging spatial super-resolution reconstruction system based on a label propagation network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. 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 creative work are within the scope of protection of the present invention.

[0045] See also Figure 1 and Figure 2 As shown, the present invention provides a mass spectrometry imaging spatial super-resolution reconstruction method based on a label propagation network, comprising the following steps.

[0046] S1, MSI data preprocessing steps.

[0047] Specifically, the original MSI spectrum data is preprocessed by baseline correction, peak alignment, normalization, and peak extraction to obtain the MSI ion image of the tissue sample; the spectrum data of the MSI ion image is compressed and dimensionally reduced to obtain the dimensionally reduced data E of the low-resolution MSI. h×v×3, where h and v are the number of pixels in the horizontal and vertical directions respectively.

[0048] In this embodiment, the MSI data preprocessing steps are specifically as follows.

[0049] S11, baseline correction was performed using a spline approximation of the baseline at the 10% quantile of ion intensity.

[0050] S12, spectral peak alignment: aligning the ion spectral peak of each pixel with the reference peak, and correcting the mass-to-charge ratio drift of each ion peak. Among them, the spectral peak with the highest correlation with other ion peaks is selected as the reference peak.

[0051] S13, normalizing the spectrum data at each pixel point.

[0052] S14, spectral peak extraction: retain the spectral peaks that meet the following two conditions to achieve spectral peak extraction: first, the intensity of the spectral peak is more than twice the noise intensity, where the noise intensity is determined by the median absolute deviation method; second, within the 10ppm window, the spectral peak is at the local maximum, and finally the MSI ion image data is obtained.

[0053] S15, using uniform manifold approximation and projection (UMAP) to reduce the dimension of the MSI ion image data to obtain the MSI reduced dimension data E h×v×3 .

[0054] S2, image registration step.

[0055] Specifically, the H&E stained microscopic image H corresponding to the MSI data is obtained through rigid body transformation and image cropping. H×V×3 ,; for MSI dimension reduction data E h×v×3 Upsample to obtain data E with the same resolution as the H&E staining image H×V×3 ; Extract H&E staining images H H×V×3 and upsampled MSI data E H×V×3 The feature points are paired and affine transformation is performed to obtain the registered MSI data matrix R H×V×3 Among them, H and V are the number of pixels in the horizontal and vertical directions respectively.

[0056] In this embodiment, the image registration steps are specifically as follows.

[0057] S21, through rigid body transformation and image shearing, obtain a high-resolution H&E staining image H that matches the MSI dimensionality reduction data H×V×3 , and reduce the dimension of the data E by upsampling MSI h×v×3 , obtain the MSI data matrix E with the same resolution as the H&E staining image H×V×3 ;

[0058] S22, H&E staining image H H×V×3 With MSI upsampled data E H×V×3 Perform feature point extraction and pairing to find multiple pairs of feature points.

[0059] S23, based on multiple pairs of feature points, the H&E stained image H H×V×3 With MSI upsampled data E H×V×3 Divide into HE-MSI corresponding patch pairs, so as to perform affine transformation alignment between patches.

[0060] For example, every three adjacent feature points form a Patch, so that H H×V×3 and E H×V×3 Divide into a series of Patch pairs; use affine transformation to transform E H×V×3 Patches are registered one by one to H H×V×3 superior.

[0061] S24, combining the results of each pair of patch registration to obtain the registered MSI data matrix R H×V×3 .

[0062] S3, label propagation network construction step.

[0063] Specifically, H&E staining images H×V×3 Perform spatial segmentation, build a label propagation network G for each image block, and transform the H&E stained image H H×V×3 Pixels are used as nodes in the H&E stained image H H×V×3 In the method, a local area centered on each pixel is used to represent the central pixel. The distance between two pixels is calculated based on the local area. The structural similarity between the two pixels is calculated based on the distance, and the structural similarity is used as the connecting edge between two corresponding nodes.

[0064] The specific steps for constructing the label propagation network are as follows.

[0065] S31, H&E stained images H×V×3 In the method, a local area centered on each pixel is used to represent the central pixel, the distance between two pixels is calculated based on the local area, and the structural similarity between the two pixels is calculated based on the distance.

[0066] d ij =||SW(x i )-SW(x j )|| 2

[0067] Among them, SW(x i ) represents x i The distance is calculated using a 3×3 window with centered at .

[0068] S32, H&E stained image H H×V×3 Divide so that H H×V×3 Segmented into 6 categories (Class1~Class6), the segmentation result F={F k ,(1≤k≤6)}.

[0069] S33, whole H&E staining image H H×V×3 Each pixel is used as a node X = [X L ;X U ], X L ={x1, x2, ...x l} is a labeled node, X U ={x l+1 , x l+2 , …x n} is the node of the label to be predicted, based on the segmentation result F = {F k ,(1≤k≤6)}, all nodes are divided into 6 corresponding categories.

[0070] S34, according to the segmentation result F={F k ,(1≤k≤6)}, in each category, the structural similarity W between two pixels kij As the connecting edge between the corresponding two nodes, the similarity matrix W of this category is constructed k , W={W k ,(1≤k≤6)}.

[0071] W kij =exp{-d ij / σ 2}

[0072] Wherein, σ represents the control parameter of the propagation length.

[0073] S35, calculate the probability propagation matrix T of each category by normalization k , so as to obtain a label propagation network G for each segmentation category (Class). Where T = {T k ,(1≤k≤6)}, which is expressed as follows:

[0074]

[0075] Among them, u, v represent the horizontal axis coordinate and vertical axis coordinate of a matrix element respectively, and col represents the matrix W k The total number of columns.

[0076] S4, label generation and propagation steps.

[0077] Specifically, in each label propagation network G, the value of the MSI data matrix E is used as the known label of the corresponding node in G, and the label is propagated along the connection edge on the network G to update the status of each node. Combining the propagation results of each label propagation network, the MSI feature of each superpixel is obtained.

[0078] The steps of label generation and propagation are as follows.

[0079] S41, according to the registered MSI data matrix R H×V×3 As the label of the corresponding node, the MSI raw data X L ={x1, x2, ...x l} is labeled Y L ={y i· ,(1≤i≤l)}, upsampling node X U ={x l+1 , x l+2 , …x n} is labeled Y U ={y i. ,(l+1≤i≤n)},Y U Represents the label to be predicted.

[0080] S42, based on the segmentation result F={F k ,(1≤k≤6)}, divide the labels into corresponding categories, that is, classify the node X and the label matrix Y into 6 categories, (X,Y)={(X k ,Y k ), (1≤k≤6)}, establish a label propagation network in each category, propagate the known labels to the nodes with unknown labels, until convergence to obtain the matrix

[0081] While: {Y k t+1 =T k ×Y k t}

[0082] S43, combining the propagation results of each label propagation network, obtains the H&E staining image H H×V×3 The corresponding MSI features of each pixel.

[0083] S5, MSI super-resolution reconstruction step.

[0084] Specifically, extract the H&E staining image H H×V×3 The statistical features of each pixel in the image are combined with the propagation results of the label network to perform feature fusion; regression modeling is used to obtain the mapping relationship between the fusion features and the MSI data, so as to predict and reconstruct the high-resolution MSI image S H×V×3 .

[0085] The MSI super-resolution reconstruction steps are as follows.

[0086] S51, extract H&E staining image H using principal component analysis (PCA), non-negative matrix factorization (NMF), independent component analysis (ICA), mean, variance, Gaussian filtering, and Sobel operator (Sobel) method H×V×3 The statistical characteristics of each pixel in .

[0087] S52, the MSI features obtained by the label network and the pixel statistical features obtained in S51 are combined into a complete feature group for feature fusion.

[0088] S53, establish feature groups and MSI dimension reduction data through support vector regression (SVR) h×v×3 The nonlinear mapping relationship between them is then used to predict the MSI super-resolution reconstructed image S H×V×3 .

[0089] The following is an example of super-resolution reconstruction of the MSI data and H&E staining images of the mouse brain. The generated image not only has the texture structure information of the H&E staining image, but also retains the MSI information, improves the image quality, and increases the MSI resolution. For details, see Figure 3 As shown, the MSI dimension reduction data E h×v×3 The super-resolution reconstruction image after the implementation of the present invention and the comparison diagram with the results of the ordinary bicubic interpolation upsampling method and the Plas method. The experimental results show that compared with the results of the ordinary upsampling method and the Plas method, the present invention can learn the texture information in the H&E staining image while retaining the MSI's own information, avoiding the defect that the own information in the upsampling technology is insufficient to achieve super-resolution reconstruction. At the same time, the registration is achieved through the Patch-based registration method, which alleviates the problem that the linear registration method cannot cope with slice distortion. Therefore, the present invention can achieve better super-resolution reconstruction performance.

[0090] In summary, the present invention has developed a method for spatial super-resolution reconstruction of mass spectrometry imaging based on a label propagation network, which uses affine transformation between patches for registration, alleviating the problem that the linear registration method cannot cope with the distortion of experimental slices. The present invention not only increases the linear and nonlinear information of the H&E pixel itself and the surrounding pixels, but also guides the propagation of MSI pixel intensity features with the local texture structure information of the high-resolution modality H&E staining image, so that the MSI pixel intensity feature is integrated into the local texture structure of the H&E staining image, improves the image perception quality of the MSI low-resolution modality, and avoids the defect that the self-information in the upsampling technology is insufficient to achieve super-resolution reconstruction. In addition, the present invention replaces the H&E staining image with a high-resolution medical image of other modalities, and can perform super-resolution reconstruction of mass spectrometry imaging and information of other modalities, further expanding the application of MSI technology in biomedical research.

[0091] See also Figure 4 As shown, this embodiment also discloses a mass spectrometry imaging spatial super-resolution reconstruction system based on a label propagation network, including:

[0092] The MSI data preprocessing module 401 is used to preprocess the original mass spectrometry imaging MSI data to obtain a three-dimensional MSI data matrix X; reduce the dimensionality of the spectrum data of the data matrix X to obtain a low-dimensional representation E of the MSI data;

[0093] The image registration module 402 is used to preliminarily align the low-dimensional representation E of the MSI data with the H&E stained microscopic image H by using rigid body transformation and image cropping to obtain a roughly registered MSI image; upsample the spatial resolution of E to the resolution of the H&E stained microscopic image, and accurately align the upsampled MSI data with the H&E stained image H by local affine transformation to obtain a registered MSI upsampled data matrix R;

[0094] The label propagation network construction module 403 is used to perform spatial segmentation on the H&E stained image H to obtain multiple image blocks; a label propagation network G is constructed for each image block, the nodes of the network are the image pixels of the H&E stained image H, and the connecting edges of the network represent the neighborhood structure similarity of two corresponding pixels;

[0095] The label generation and propagation module 404 is used to use the MSI upsampled data matrix R as the label of the corresponding node in the label propagation network G in each label propagation network G, and propagate the known label along the connection edge in the network G to the node with the unknown label, and iteratively update the node state of the network G as the MSI feature of each node;

[0096] The MSI super-resolution reconstruction module 405 is used to extract the statistical features of each pixel of the H&E staining image H, and fuse the statistical features with the MSI features of the corresponding nodes of the label propagation network to form a feature vector; establish a regression relationship between the MSI data and the feature vector, and reconstruct a high spatial resolution MSI image S.

[0097] Specific implementation of a mass spectrometry imaging spatial super-resolution reconstruction system based on a label propagation network The same mass spectrometry imaging spatial super-resolution reconstruction method based on a label propagation network is not repeated in this embodiment.

[0098] The principle and operation mode of the present invention are described through the above-mentioned specific implementation cases. These cases are intended to provide readers with a clear understanding framework to grasp the core ideas and operation points of the present invention. However, it should be clear that these cases are not limitations on the scope of application of the present invention. For professionals in this field, various forms of improvements and innovations based on the core ideas of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for spatial super-resolution reconstruction of mass spectrometry imaging based on label propagation network, characterized in that: include: MSI data preprocessing step, preprocessing the original mass spectrometry imaging MSI data to obtain a three-dimensional MSI data matrix X; Reduce the dimensionality of the spectral data of the data matrix X to obtain the low-dimensional representation E of the MSI data; In the image registration step, the low-dimensional representation E of the MSI data is preliminarily aligned with the H&E stained microscopic image H by using rigid body transformation and image cropping to obtain a roughly registered MSI image; the spatial resolution of E is upsampled to the resolution of the H&E stained microscopic image, and the upsampled MSI data is accurately registered with the H&E stained image H by local affine transformation to obtain the registered MSI upsampled data matrix R; The label propagation network construction step is to perform spatial segmentation on the H&E stained image H to obtain multiple image blocks; a label propagation network G is constructed for each image block, the nodes of the network are the image pixels of the H&E stained image H, and the connecting edges of the network represent the neighborhood structure similarity of two corresponding pixels; The label generation and propagation step, in each label propagation network G, the MSI upsampled data matrix R is used as the label of the corresponding node in the label propagation network G, and the known label is propagated along the connection edge in the network G to the node with unknown label, and the node state of the network G is iteratively updated as the MSI feature of each node; In the MSI super-resolution reconstruction step, the statistical features of each pixel of the H&E staining image H are extracted, and the statistical features are fused with the MSI features of the corresponding nodes of the label propagation network to form a feature vector; a regression relationship between the MSI data and the feature vector is established to reconstruct the MSI image S with high spatial resolution.

2. The method for spatial super-resolution reconstruction of mass spectrometry imaging based on label propagation network according to claim 1, characterized in that: The MSI data preprocessing step specifically includes: Baseline correction, peak alignment, normalization and peak extraction are performed on the mass spectrometry signal of each pixel point to obtain an MSI ion image; The MSI ion image is subjected to unsupervised compression and dimensionality reduction to obtain the MSI dimensionality reduction data E of the tissue section to be analyzed.

3. The method for spatial super-resolution reconstruction of mass spectrometry imaging based on label propagation network according to claim 1, characterized in that: The image registration step specifically includes: The low-dimensional representation E of the MSI data is upsampled to obtain an MSI data matrix with the same spatial resolution as the H&E stained microscopic image H; Extract the feature points of the H&E staining image H and the upsampled MSI data matrix and pair them. Any three adjacent feature points constitute an image patch. The H&E stained image H and the corresponding image patches of the upsampled MSI data matrix are registered one by one by affine transformation to obtain the registered MSI upsampled data matrix R.

4. The method for spatial super-resolution reconstruction of mass spectrometry imaging based on label propagation network according to claim 1, characterized in that: The label propagation network construction step specifically includes: Perform spatial segmentation on the H&E stained image H to obtain multiple image blocks; construct a label propagation network G for each image block, and the nodes of the network correspond to the pixels of the H&E stained image H; The H&E staining value of the local area of ​​the pixel is used as the feature vector, the structural similarity between two pixels is calculated, and it is used as the connection edge weight between the corresponding nodes to obtain the label propagation network G.

5. The method for spatial super-resolution reconstruction of mass spectrometry imaging based on label propagation network according to claim 1, characterized in that: The steps of generating and propagating the label specifically include: The MSI upsampled data matrix R is used as the label of the corresponding node in the label propagation network G, and the label is propagated along the connection edge in the network G to update the status of other nodes; Combining the propagation results of each label propagation network, the MSI characteristics of each node are obtained.

6. The method for spatial super-resolution reconstruction of mass spectrometry imaging based on label propagation network according to claim 1, characterized in that: The MSI super-resolution reconstruction step specifically includes: A variety of feature extraction methods are used to obtain the statistical features of each pixel in the H&E staining image H, and the statistical features are fused with the MSI features of the corresponding nodes of the label propagation network to form the fused features of each pixel; A regression model is constructed between the fusion features of the pixels and their MSI features to predict the MSI data of the upsampled pixels and obtain the super-resolution reconstructed MSI image S.

7. A mass spectrometry imaging spatial super-resolution reconstruction system based on a label propagation network, characterized in that: include: MSI data preprocessing module, used to preprocess the original mass spectrometry imaging MSI data to obtain a three-dimensional MSI data matrix X; Reduce the dimensionality of the spectral data of the data matrix x to obtain the low-dimensional representation E of the MSI data; The image registration module is used to preliminarily align the low-dimensional representation E of the MSI data with the H&E staining microscopic image H by using rigid body transformation and image cropping to obtain a roughly registered MSI image; upsample the spatial resolution of E to the resolution of the H&E staining microscopic image, and accurately align the upsampled MSI data with the H&E staining image H by local affine transformation to obtain the registered MSI upsampled data matrix R; The label propagation network construction module is used to perform spatial segmentation on the H&E staining image H to obtain multiple image blocks; a label propagation network G is constructed for each image block, the nodes of the network are the image pixels of the H&E staining image H, and the connecting edges of the network represent the neighborhood structure similarity of two corresponding pixels; The label generation and propagation module is used to use the MSI upsampled data matrix R as the label of the corresponding node in each label propagation network G, and propagate the known label along the connection edge in the network G to the node with unknown label, and iteratively update the node state of the network G as the MSI feature of each node; The MSI super-resolution reconstruction module is used to extract the statistical features of each pixel of the H&E staining image H, and fuse the statistical features with the MSI features of the corresponding nodes of the label propagation network to form a feature vector; establish a regression relationship between the MSI data and the feature vector, and reconstruct a high spatial resolution MSI image S.

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