229 results about "Tissue architecture" patented technology

The invention relates to the technical field of image classification, in particular to a training method and system of a cervical pathological image classification model based on multiple modes. The method comprises the following steps: acquiring a cervical tissue image and carrying out tissue structure segmentation, forming a nucleus-interstitial-epithelium three-distribution framework, collecting development historical data, confirming a prediction trend of each layer, carrying out environment field simulation through the image, generating a simulated cervical environment field, and carrying out hierarchical evolution prediction on the framework. Evolution mapping images are generated according to the evolution data and classified, finally, a visual basic model is obtained through combined modeling training, image-text fusion is achieved, and a cross-center deployment model system is generated. According to the method, vision-language combined modeling is realized, and the stability and controllability of the whole model structure in image space deformation modeling, semantic cross-modal alignment construction and task-level response flow scheduling are improved.

The invention discloses an image video super-resolution enhancement method based on a degradation generative adversarial network, and relates to the field of image processing, and the method comprises the steps: carrying out the image collection and preprocessing; building and training a super-resolution enhancement model; and carrying out super-resolution enhancement on the image based on the degradation generative adversarial network model. According to the method, an image content self-adaptive dynamic degradation kernel generation mechanism is adopted, the degradation process of the image under different equipment and organization structures is truly simulated, a dynamic up-sampling and residual error correction network guided by the degradation kernel is adopted, the detail reduction capability and the structure fidelity of the super-resolution image are remarkably improved, and the super-resolution image quality is improved. The image texture authenticity and key organization density consistency are effectively enhanced, the balance of training games between a generator and a discriminator is realized, and the model stability and convergence quality are improved.

The invention discloses a breast cancer neoadjuvant chemotherapy curative effect prediction method and system, and relates to the medical image processing and analysis technology, and the method comprises the steps: carrying out the Macenko dyeing normalization of a sample HE pathological image of a patient; extracting a tissue region from the normalized sample HE pathological image by using an HEST method; inputting the extracted tissue region into a Prov-GigaPath network, extracting a feature vector of a specified dimension under set parameters, and performing dimension reduction on the extracted feature vector; inputting the feature vectors subjected to dimension reduction into an ABMIL model, and training the feature vectors; and predicting the HE pathological image of the patient based on the trained ABMIL model, and visualizing the prediction result by using the attention score of the trained ABMIL model. According to the method provided by the invention, multi-level complex features such as cellular morphology and tissue structure in the HE image can be automatically mined, and the accuracy and reliability of prediction are greatly improved.

According to the invention, the multi-mode time-frequency domain conversion and the deep learning technology are combined, and a multi-component mixed Raman spectrum unmixing method is developed, so that clinical in-vivo and in-situ detection and disease diagnosis of novel Raman probes, instruments and the like are facilitated. The method comprises the following steps: (1) converting a mixed Raman spectrum from a time domain to a frequency domain by using fast Fourier transform (FFT), discrete cosine transform (DCT) and discrete sine transform (DST), and extracting frequency domain features; (2) extracting multi-scale local time-frequency domain characteristics of the mixed spectrum by using short-time Fourier transform (STFT) and discrete wavelet transform (DWT); (3) carrying out spectral unmixing calculation in each mode in combination with a one-dimensional attention mechanism U-shaped neural network model; and (4) fusing various modal unmixing results by using a meta-learning method, and analyzing the weight of each modal to obtain an accurate unmixing spectrum. Compared with a traditional Raman spectrum analysis method, the Raman spectrum multi-component signal unmixing method based on multi-modal time-frequency domain transformation and deep learning can accurately separate independent Raman signals of different tissue structures and biochemical components in a complex environment in a living body, so that the unmixing accuracy of the Raman spectrum multi-component signal is improved. Therefore, convenience is provided for subsequent disease mechanism analysis and diagnosis. The method provides an innovative and potential solution for in-vivo and in-situ detection analysis and disease diagnosis of medical clinical Raman spectroscopy.

The invention relates to the technical field of medical information, in particular to an anomaly recognition system for trend characteristics of chronic disease monitoring data. The system comprises a chronic disease monitoring data integration module, a chronic disease feature change analysis module, a chronic disease composite feature analysis module and a chronic disease trend feature anomaly recognition module, and can perform chronic disease classification and associated chronic disease integration according to real-time chronic disease monitoring data. Generating integrated associated chronic disease data; performing tissue structure mapping reconstruction of the chronic disease patient and disease mutation characteristic factor analysis of the chronic disease patient according to the integrated and associated chronic disease data, and generating disease mutation characteristic factor data of the chronic disease patient; according to the disease mutation characteristic factor data of the chronic disease patient, performing disease composite characteristic analysis on the chronic disease patient, and performing chronic disease trend abnormal characteristic analysis to generate chronic disease trend abnormal characteristic data. According to the invention, accurate identification of chronic disease trend abnormity is realized.

The invention discloses an ultrasonic image denoising method based on variational mode decomposition and local space sparse fusion, which comprises the following steps of: firstly, adaptively optimizing key parameters of variational mode decomposition by using a grey wolf optimization algorithm to realize stable and efficient decomposition of an ultrasonic image; then, classifying the modal components according to the structural features of the modal components, and implementing differentiated denoising strategies for different types of modals to separate noise and reserve useful information; after modal reconstruction, a sparse expression method based on local space information is further introduced, according to the method, accurate boundary detection is carried out through gradient vector flow and gray scale proportion analysis, self-adaptive partitioning is carried out on an image according to boundary information, and finally sparse reconstruction is carried out through double dictionaries trained for different areas. According to the method, speckle noise in the ultrasonic image can be effectively suppressed, and meanwhile, the capability of keeping the edge and detail information of a tissue structure is remarkably improved, so that the ultrasonic image with higher quality is obtained.

The utility model relates to the technical field of fixation and preservation of biological tissue samples, and provides an embedding mold for multiple frozen tissues, which comprises a base, a plurality of clamping pieces, a plurality of clamping pieces and a plurality of clamping pieces, and is characterized in that the top surface is provided with a pair of centrosymmetric supporting blocks; the sleeve is detachably arranged outside the pair of supporting blocks in a sleeving manner; wherein the pair of supporting blocks are arranged at an interval of 180 degrees, and the outer side walls of the supporting blocks are attached to the inner side wall of the sleeve in a transition fit mode; the sleeve is as high as the supporting block, and an accommodating cavity is defined by the inner side wall of the sleeve and is used for embedding the plurality of strip-shaped frozen tissue blocks in a vertical form. The containing cavity formed in the sleeve is changed into a columnar shape with enough depth from an existing flat shape with limited depth, the problems that the tissue structure is incomplete and an OCT embedding medium overflows are directly solved, the edge of a tissue combined block is smooth, slicing is facilitated, meanwhile, a plurality of strip-shaped frozen tissue blocks are embedded in a vertical mode, and the working efficiency is improved. The height of the tissue block is unified, the size of the tissue block is fixed, and the problems that sample loss is large and tissue arrangement is too close to the edge are solved.

The invention discloses a noise determination model training method and a medical image generation method and device, and belongs to the technical field of computers. The method comprises the following steps: determining sample knowledge, wherein the sample knowledge represents visual information of a tissue structure in a first medical image at a plurality of visual angles; performing noise addition on the image features of the second medical image for multiple times to obtain feature data after noise addition for each time; determining first de-noising data corresponding to each noise addition based on the sample knowledge and the feature data after each noise addition through a neural network model; and training a neural network model based on the first de-noising data corresponding to each noise addition to obtain a noise determination model. The added noise is accurately predicted based on the sample knowledge guide model, the accuracy of the first de-noised data is improved, the accuracy of the noise determination model is improved, the noise determination model can accurately predict the noise data needing to be removed, and by removing the noise data, the noise removal efficiency is improved. And obtaining target medical images of the tissue structure in the source medical image at different visual angles.

The invention discloses a laparoscopic surgery image enhancement and real-time auxiliary system, and relates to the technical field of medical image processing and intelligent auxiliary surgery. According to the laparoscopic surgery image enhancement and real-time auxiliary system, a laparoscopic image flow is obtained and cleaned through the video input and preprocessing module, image enhancement processing is carried out through a deep learning model, then spatial position features of a tissue structure and an instrument are extracted, and a structure recognition index and a spatial safety index are analyzed; according to the invention, through cooperative work of the image preprocessing module and the depth image enhancement model, brightness compensation, edge enhancement, denoising, smoke suppression and other operations are performed on the laparoscope image stream, the depth model is utilized to extract multi-scale image features and reconstruct details, and an enhanced video image stream is generated; therefore, the intraoperative abdominal cavity structure and instrument boundary is clearer, the positioning judgment precision of a doctor is remarkably improved, and the misjudgment risk caused by image blurring is reduced.

The invention discloses a plastic surgery auxiliary strategy generation method and system based on a multi-modal medical image, and relates to the technical field of auxiliary strategy generation. After the multi-modal medical image is collected, multilevel organization structure information is extracted, and a facial structure function linkage graph is constructed; identifying a structure coupling region with a correlation influence relationship in the function linkage graph as an intervention region; reversely querying an operation strategy knowledge base based on the intervention area to obtain a candidate operation fragment set; according to the structure conflict relation matrix and the recovery time dependence matrix between the candidate operation segments, constructing an initial operation path diagram; according to aesthetic preference parameters set by a user, generating an aesthetic offset information field, and acting on the initial operation path diagram to form a structural deformation map; and based on the structural deformation map, injecting control constraints, generating a reversible control chart, and outputting a personalized operation execution path. Personalized operation path generation is realized, and dissection safety and aesthetic requirements are considered.

The invention provides a regional anesthesia puncture path planning method and system under ultrasonic guidance, and relates to the technical field of ultrasonic image.According to the regional anesthesia puncture path planning method and system, dynamic motion information of a tissue structure is obtained by collecting ultrasonic image data of a target region and conducting motion analysis, and electrocardiosignals are synchronously collected to determine heart beat time phase information; determining a vascular movement mode based on the two, learning a time change rule by using a long-short-term memory network, and establishing an association relationship between a vascular spatial position and a heart beat phase; constructing a motion prediction model to predict the future motion trend of the blood vessel, and fusing the respiratory motion information to correct to obtain the blood vessel motion information; and finally, a safe puncture window is calculated according to the information, a regional anesthesia puncture path is planned, vascular movement prediction and safe puncture window calculation based on vascular movement rule learning and breathing correction in regional anesthesia under ultrasonic guidance can be achieved, and then the puncture path is precisely planned.

The invention discloses a deformation registration method and device, a storage medium and electronic equipment, and relates to the field of medical science and technology and the field of image processing. The method comprises the following steps: acquiring reference data and floating data; according to the organ type, data separation is carried out on the reference data and the floating data, and the data separation is used for separating the reference data and the floating data to obtain image data of each independent organ; performing deformation registration on the image data of each independent organ to obtain a deformation field corresponding to each independent organ; and fusing the deformation field corresponding to each independent organ into a target deformation field, and performing deformation registration on the floating data through the target deformation field. The technical problem that in the prior art, when a registration algorithm is adopted for dose accumulation in radiotherapy, due to the fact that alignment is redundant or insufficient in the registration process of a local organ tissue structure, dose accumulation in an organ boundary area is inaccurate is solved.

The invention discloses a CT image metal artifact correction method and device based on a U-Net model, electronic equipment and a storage medium, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a to-be-corrected CT image; inputting a CT image to be corrected into the U-Net network model to obtain a CT image which is output by the U-Net network model and is subjected to metal artifact correction; wherein the U-Net network model comprises a multi-scale attention mechanism and an adaptive fusion module. According to the method, a multi-scale attention mechanism is introduced to a U-Net network model, multi-scale feature extraction can be performed in jump connection, metal artifact regions and organization structure features are highlighted, the correction precision of metal artifacts is improved, a correction strategy is dynamically adjusted through an adaptive fusion module, over-fitting or under-fitting is avoided, and the correction precision of the metal artifacts is improved. The artifact residual is effectively reduced; and the metal artifact correction quality is improved.

The invention relates to the technical field of medical image processing and three-dimensional reconstruction, and particularly discloses a reconstruction and analogue simulation method based on a cardiac image. The method comprises the following steps: firstly, preprocessing an input CT or MRI medical image, and automatically segmenting a heart multi-tissue structure by using an nnUNetv2 model, including a plurality of anatomical regions such as atrium, ventricle, myocardial tissue and blood pool; and then performing topology repair, smoothing processing and label resampling on the segmentation result to obtain a three-dimensional label body with a continuous structure and a complete boundary. And based on the optimized tag body, constructing a heart three-dimensional surface model by adopting a Marching Cubes algorithm, and generating a multi-material volume grid suitable for finite element analysis. According to an electrode position and a radio frequency parameter set by a user, a radio frequency ablation simulation model based on a Pennes biological heat conduction equation and an Arrhenius model is established, a temperature field and a tissue thermal damage range are calculated, and a simulation result is displayed in a three-dimensional mode in an overlapping mode. According to the method, the integrated process from medical image automatic segmentation to heart three-dimensional reconstruction and thermal simulation is realized, the segmentation precision, the modeling efficiency and the simulation reliability are improved, and the method can be applied to application scenes such as surgical planning, preoperative evaluation and medical teaching.

The invention discloses a multi-layer heterogeneous network unicellular organism network inference method based on meta-path enhancement, which mainly comprises a gene regulation knowledge base enhanced multi-layer heterogeneous network construction module for integrating an external gene interaction network and multiple omics data such as scRNA-seq, scATAC-seq, ST and the like; constructing a single-cell multi-omics multilayer heterogeneous network containing cell-cell, cell-gene and gene-gene relationships, and fusing spatial constraints to consider cell positions and tissue structures; and the feature enhancement module based on the meta-path explores complex semantics of the network by designing a multi-hop meta-path mode, designs an adaptive multi-view learning framework and a multi-round enhancement mechanism, and optimizes feature representation by using cell-gene interaction and cross-modal attention fusion. The unicellular biological network can be effectively deduced, the deduction accuracy and biological interpretation are remarkably improved, the method plays an important role in understanding the cell biological process, developing and treating diseases and the like, has good expandability, and can further integrate multi-modal omics data such as proteomics and metabonomics.

The invention provides a pathological image analysis method and device based on multi-instance learning, equipment and a storage medium, and the method comprises the steps: receiving a lung pathological section image, segmenting the lung pathological section image into a plurality of 256 * 256 pixel image blocks, and screening and reserving effective image blocks containing a tissue structure; extracting a high-dimensional feature vector of each effective image block by using a self-supervised pre-trained deep neural network on the large-scale dyeing pathological image; the features are input into a multi-branch attention module containing 15 parallel branches, self-attention, cross-attention and global attention mechanisms are executed respectively, and local spatial dependence, cross-image block similarity and global relevance are modeled; and carrying out weighted aggregation on the basis of the processed attention weight to generate a global feature of the slice image, outputting a pathological category through a classification network, and supporting heat map visualization and feature clustering analysis. According to the method, unlabeled region information is effectively utilized, and the pathological classification precision and the model generalization ability are improved.

The disclosure provides various embodiments of systems to facilitate the cutting of tissue structures and other tissue structures percutaneously.

The invention discloses a CBCT metal artifact suppression method and system based on preoperative prior guidance, and the method comprises the steps: taking second CBCT image data, obtained in real time in an operation, of a target tissue as a reference, and carrying out the rigid registration of first CBCT image data, obtained before the operation, of the target tissue; deforming the first CBCT image data to obtain third image data; the relative positions of the tissue structures in the third image data and the second CBCT image data are consistent; based on the third image data, performing coarse segmentation on the metal implant in the second CBCT image data by adopting a digital subtraction technology; segmenting to obtain first mask data and background prior data of the metal implant; performing true and false positive judgment on the first mask data and performing false positive suppression processing to obtain second mask data; carrying out data restoration on the background prior data by adopting a self-adaptive interpolation grid to obtain projection domain data; and reconstructing by using an FDK reconstruction algorithm to obtain a CBCT image of the target tissue without metal artifacts.

The invention discloses a cigar tobacco tissue thickness measuring method and a cutting device, and relates to the technical field of tobacco tissue thickness measurement, and the method comprises the following steps: S1, carrying out screening treatment before cigar tobacco and rootstock cutting; s2, device cutting module selection and parameter setting work; s3, tobacco plant rhizome and leaf tissue structure slices are cut; s4, tissue slice electron microscope scanning and image selection work; and S5, carrying out tissue slice image part identification and division and average thickness calculation. The method solves the problems that in the prior art, in thickness measurement of different tissue structures of tobacco leaf sections, most methods or leaf surface image analysis systems are used for estimating and reading scale scales, and due to the fact that thickness distribution of tissue parts of leaves is not uniform and consistent, a traditional scale measurement method is large in error due to different selected measurement point positions, and the measurement accuracy is poor. And the average thickness of each part cannot be quickly and accurately measured.

The application discloses a cancer survival prediction method and device based on whole-slice pathological images, and relates to the technical field of medical artificial intelligence and digital pathological image analysis. The method comprises the following steps: acquiring whole-slice pathological images and patient survival information, and performing pretreatment; performing cell detection on the whole-slice pathological images to obtain cell structure data; dividing the whole-slice pathological images into a plurality of image regions, and determining regions of interest; screening the cell structure data in each region of interest to construct a cell subset; sorting the cell structure data according to the distribution relationship of cells in space to construct a cell structure sequence; constructing a cell structure modeling model, performing feature modeling on the cell structure sequence, and obtaining tissue structure features capable of representing the microstructure of tumor tissues; and inputting the tissue structure features into a survival prediction model to predict the survival risk of the patient. The application can improve the accuracy and stability of cancer survival prediction.

The invention discloses an ultrasonic shear wave elastography-based intrameniscus tearing quantitative evaluation method and system, and relates to the technical field of computer-aided diagnosis. The method is used for solving the problem of insufficient quantification of meniscus tearing detection. The method comprises the following steps: firstly, fixing a knee joint at a preset buckling angle, exciting shear waves along an annular fiber direction and a vertical direction, and collecting ultrasonic radio-frequency signals of an affected side meniscus and an uninjured side meniscus in different load states; then, a three-dimensional anisotropic elastic atlas is constructed based on the shear wave propagation velocity field, and spatial elastic distribution characteristics of the tissue are obtained; thirdly, calculating an elastic continuity index, a spatial variation coefficient and an abnormal region morphological parameter, and quantitatively representing the continuity and local tearing characteristics of the tissue structure; and finally, inputting the multi-dimensional features into a multi-branch neural network, fusing the features through a cross-branch attention mechanism, and outputting a meniscus tearing quantitative evaluation value, thereby realizing intelligent and objective evaluation of the tearing degree, and providing a basis for clinical diagnosis and postoperative rehabilitation.

The embodiment of the invention discloses an auxiliary analysis method and device for a pathological section image based on pre-training, and the method comprises the following steps: training an image encoder based on a visual self-supervision strategy in a first pre-training stage, and capturing the tissue structure features of the pathological section image; in a second pre-training stage, based on alignment training of image and pathological histomorphology description, through image-text comparison loss and image-text generation loss, joint training is performed on the image encoder and the text encoder, a pre-trained pathological section core encoder is obtained, and histomorphology semantic fusion is realized; and in a third pre-training stage, carrying out cross-modal alignment training on the image encoder and the pathological report text, optimizing the semantic expression capability of the image encoder, and realizing semantic fusion of the pathological report. According to the embodiment of the invention, the efficiency and accuracy of auxiliary analysis of the pathological section image can be improved.

The invention provides an ultrasonic plane wave multi-mode cooperative imaging method and system, and the method comprises the steps: controlling an ultrasonic probe to execute shear wave elastic imaging and sound attenuation imaging of which the mechanical index is equal to or greater than a first threshold value based on a multi-level voltage control scheme; and executing super-resolution imaging with the mechanical index smaller than or equal to a second threshold value after the super-resolution imaging is completed. The method comprises the following steps: acquiring a sound attenuation coefficient graph generated in a sound attenuation imaging process, and respectively acquiring a first super-resolution graph without contrast agent perfusion, a second super-resolution graph with contrast agent perfusion and a tissue elasticity graph generated in a shear wave elasticity imaging process based on an ultrafast plane wave technology; performing post-processing on the collected first super-resolution graph to generate a probability density graph of the tissue structure, and performing post-processing on the collected second super-resolution graph to generate a microbubble tracking speed graph; and carrying out cooperative imaging on the tissue elasticity map, the sound attenuation coefficient map, the probability density map and the velocity map. The imaging comprehensiveness and accuracy are improved, and early lesion diagnosis is facilitated.

The utility model relates to the technical field of medical instruments, and discloses a positioning and orienting tissue embedding box which comprises a box body, a positioning mechanism and a fixing mechanism, wherein the positioning mechanism comprises a first limiting groove formed in the inner side wall of the bottom plate and a second limiting groove formed in the inner bottom wall, a first limiting rod is movably installed in the first limiting groove, a second limiting rod and a positioning rod are fixedly installed at the middle end of the first limiting rod, and the other end of the second limiting rod is movably installed in the second limiting groove; the fixing mechanism comprises a positioning hole formed in the top end of the inner side wall of the bottom plate, and a positioning needle is movably installed in the positioning hole. According to the positioning and orienting tissue embedding box disclosed by the utility model, the positioning mechanism and the fixing mechanism which are arranged in the embedding groove are adopted, so that the tissue can be effectively positioned, orientated and fixed, the compression or deformation of the tissue structure can be avoided, and the original form of the tissue is ensured.

The present invention provides a method for analyzing molecular differences in glioblastoma and its application, belonging to the field of biotechnology. Using differential gene expression analysis, spatial transcriptomics analysis, deconvolution analysis, single-cell RNA sequencing data, and immunofluorescence, this application identified specific molecular signatures associated with pseudopalisading necrosis (PAN) and microvascular proliferation (MVP), revealing differences between these tissue structures. The results showed that PAN and MVP shared spatial expression of specific marker genes and were enriched for endothelial cells of distinct phenotypes. Single-cell trajectories inferred pseudotemporal trajectories of glioblastoma stem cells (GSCs) to EC differentiation. This study reveals distinct roles for endothelial cells in the PAN and MVP regions of GBM, highlighting the complexity and diversity of the tumor microenvironment and providing new insights into endothelial cell subpopulations and GSC differentiation into endothelial cells in GBM.

Apparatus, systems, and methods are provided for localizing lesions within a patient's body, e.g., within a breast. The system may include one or more markers implantable within or around the target tissue region, and a probe for transmitting and receiving electromagnetic signals to detect the one or more markers. During use, the marker(s) are into a target tissue region, and the probe is placed against the patient's skin to detect and localize the marker(s). A tissue specimen, including the lesion and the marker(s), is then removed from the target tissue region based at least in part on the localization information from the probe.

The application provides a multi-modal tissue segmentation method and a surgical navigation system, and the method comprises the following steps: inputting three-dimensional medical images of multiple modes into corresponding deep learning network models respectively for segmentation to obtain corresponding three-dimensional segmentation results; registering the three-dimensional medical images of each mode to obtain a registration relationship; and according to the registration relationship, combining the three-dimensional medical images of each mode to weight and fuse the three-dimensional segmentation results of each mode according to preset weights of each type of tissue. The application trains a segmentation model for each mode of medical image, each deep learning model has stronger segmentation capability for the medical image of the corresponding mode, and has higher segmentation precision; different types of medical images are respectively designed with preset weights (mode weights) of each type of tissue, and the segmentation results are fused according to the mode weights in the fusion process, so that the tissue structure characteristics are more fully reflected, and the segmentation precision is improved.

This invention discloses a coherent electrical stimulation method for idiopathic scoliosis combined with exercise therapy, belonging to the field of electrical stimulation methods for idiopathic scoliosis. It aims to address the problem of how to dynamically adjust stimulation parameters based on individual differences to improve treatment efficacy. The method includes acquiring raw image sequences of the patient's spine through MRI scans, preprocessing the raw image sequences to construct a three-dimensional geometric model of the spine, configuring simulated electrical property parameters for the tissue structures, and then adding coherent electrical surface electrodes to the model. The electrode placement area on the three-dimensional geometric model of the spine is initially defined, and a coherent electrical stimulation signal is generated using a function generator. A comprehensive evaluation index value is calculated through finite element analysis to dynamically adjust the coherent electrical stimulation signal. Based on the coherent electrical stimulation signal parameters and its comprehensive evaluation index value, a coherent electrical stimulation scheme is determined, and ultrasound-guided technology is used to locate the electrode positions and implement coherent electrical stimulation therapy.

The present application relates to a kind of coke reaction after strength prediction method of comprehensive optical tissue and pore structure characteristics, comprising:1) optical tissue structure analysis;2) pore structure parameter analysis;3) comprehensive influence analysis and the establishment of CSR prediction formula.The present application uses microcosmic optical tissue structure and pore structure characteristic index to predict CSR, compared with the conventional prediction method using macroscopic index, the workload needed for microcosmic index determination is greatly reduced, thereby reducing the coke oven test operation and response time in field, improve efficiency and reduce cost;In addition, microstructure can essentially reveal the change rule of coke performance, according to the prediction result, coal blending structure can be adjusted, more widely applicable, the prediction result is more accurate.