355 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 relates to the technical field of image processing, in particular to an image segmentation system for medical diagnosis, which comprises a regional characteristic analysis module, a modal selection optimization module, an image local enhancement module, an edge characteristic analysis module and a boundary optimization adjustment module. According to the medical image segmentation method, the gray level distribution, the texture density and the structure contour of the tissue structure in the image layer are analyzed, the system is allowed to accurately measure the characteristic deviation between different modes, the accuracy of medical image segmentation is improved, especially on the processing of the complex tissue structure, the adjacent tissues with different properties can be better analyzed and distinguished, and the medical image segmentation accuracy is improved. Real-time evaluation and adjustment of local contrast enable details of the image to be clearer, image quality is improved, accuracy of edge recognition is improved, by analyzing edge curvature and morphological characteristics, the system can optimize edge trend and reconstruct a boundary path, overall performance of image segmentation is further improved, and image segmentation efficiency is improved. Therefore, the treatment and prognosis effects of the patient are ensured.

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 belongs to the technical field of image segmentation, and discloses a multi-modal medical image automatic segmentation method and system based on a three-dimensional reconstruction model.The multi-modal medical image automatic segmentation method comprises the steps that multi-modal image data are acquired, and local features of the multi-modal image data are extracted; identifying a patient tissue structure in the multi-modal image data based on the local features; segmenting the tissue structure of the patient in the multi-modal image data by using a segmentation technology, and reconstructing a three-dimensional virtual model based on the tissue structure of the patient; and fusing a three-dimensional virtual model body drawing result and a three-dimensional virtual model surface drawing result to realize three-dimensional display of a segmentation result. According to the method, the influence of human errors and subjective judgment is reduced by identifying and segmenting the tissue structure, so that boundaries of various tissues are identified more accurately, automatic image segmentation can be completed in a short time, and subsequent diagnosis and analysis can be performed more quickly.

A bioscaffold structure is provided, the bioscaffold comprising a plurality of connected unit cells, each unit cell comprising a plurality of filaments composed of an extracellular material containing Collagen I and Collagen III, wherein each of the plurality of unit cells includes at least one opening connected to an internal volume.

The invention relates to the field of medical image processing, and discloses a medical image enhancement processing and tissue boundary intelligent identification method, and the method comprises the steps: constructing a medical image segmentation model, and enabling a learnable filter module to comprise a Gabor filter group and an LoG filter group which are parallel; the multi-modal feature fusion module fuses the texture features output by the plurality of Gabor filters, the edge features output by the plurality of LoG filters and the original input image; the Swin Transform is used for dividing the fusion feature map into an image block sequence and extracting a multi-scale feature map from the image block sequence; the segmentation head is used for recovering the multi-scale feature map to the original resolution and outputting a segmentation mask; training the constructed medical image segmentation model; and performing image segmentation processing on a clinical medical image by using the trained medical image segmentation model, and outputting pixel-level segmentation masks capable of clearly displaying different tissue structures and tissue boundaries. The method can effectively improve the recognition precision of the tissue boundary in the medical image.

The invention relates to the technical field of disease diagnosis, and comprises a disease diagnosis method and system based on neural network cognitive diagnosis, and the method comprises the following steps: obtaining medical image data, calculating the gray level change rate, gradient direction distribution and edge continuity of a lesion region, counting the lesion tissue damage area, and analyzing the pathological tissue damage degree. And obtaining a lesion distribution consistency index. According to the invention, through comprehensive calculation of the medical image data and the pathological tissue slice data, fine-grained description of a focus area is realized, association among different lesion features is realized, and calculation of a lesion propagation path matching rate is realized, so that identification of lesion diffusion conditions is more accurate, and development trends of diseases in different tissue structures can be effectively predicted; the calculation of the cross-modal feature error is combined with the adjustment of the distribution weight of the lesion region, misdiagnosis caused by modal difference and extraction of abnormal signals in a lesion diffusion range are reduced, so that screening of potential diseases is more targeted, and the accuracy of disease recognition is improved.

The invention provides a desktop digital human interactive studio method based on JavaFX, and the method comprises the steps: scanning the face of a real person through a depth camera, obtaining the three-dimensional shape data of a facial tissue, carrying out the subdivision and marking of the data through the combination of medical anatomical knowledge, and building a digital human face model which comprises the geometric shapes and texture features of lip, tongue and tooth facial tissue structures; establishing a tooth visibility control rule according to the mapping relation between the tongue position and the phoneme in the playing process, and dynamically adjusting the tooth visibility of the digital person in the playing process according to the tooth visibility parameter of the phoneme sequence, so that the tooth visibility change is matched with the characteristics of the real person in the playing process; and tracking the shape and texture features of the lip, tongue and tooth areas of the digital person in the playing process in real time, comparing the shape and texture features with target playing lip shape, tongue position and tooth visibility parameters, dynamically adjusting the parameters of the digital person face model, and performing cooperative control of lip, tongue and teeth in the playing process.

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 provides a digital pathological section scanner control system based on the Internet of Things. The digital pathological section scanner control system comprises a user management module, a product classification module, an equipment management module, a pathological image data service module, a remote real-time monitoring and operation module and an intelligent fault diagnosis and early warning module. The system has the following beneficial effects: the product classification module realizes efficient classification management of pathological section scanner equipment; an intelligent section type identification sub-module in the pathological image data service module can automatically identify HE staining types and tissue features, and dynamically adjust scanning parameters according to staining quality, so that the stability of scanning quality is improved; a 3D digital slice reconstruction sub-module in the pathological image data service module can construct a three-dimensional organization structure view, so that the accuracy and reliability of pathological diagnosis are greatly improved; the intelligent fault diagnosis and early warning module adopts a machine learning technology to carry out fault prediction and real-time diagnosis, and equipment faults are effectively prevented.

Surgical marker systems and methods for delineating a lesion margin of a subject are provided. An example system includes a handheld probe device configured to capture an optical coherence tomography (OCT) image and a processor coupled to a memory. The handheld probe device includes a handheld probe including a fiber-optic probe assembly and a marker assembly. The processor is configured to: segment, by a neural network, each pixels of the OCT into different tissue-type categories; generate one or more feature vectors based at least in part on the segmented pixels; determine, by a one-class classifier, a boundary location in the OCT image between a normal tissue and an abnormal tissue of the tissue structure; and control the marker assembly to selectively create a visible label on a tissue location of the subject, the tissue location corresponding to the boundary location.

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.

A computer device performs feature extraction on two-dimensional medical images included in a three-dimensional medical image, to obtain image features corresponding to the two-dimensional medical images. The three-dimensional medical image are obtained by continuously scanning a target tissue structure. The computer device determines offsets of the two-dimensional medical images in a target direction based on the image features. The computer device performs feature alignment on the image features based on the offsets, to obtain aligned image features. The computer device performs three-dimensional segmentation on the three-dimensional medical image based on the aligned image features, to obtain three-dimensional layer distribution of the target tissue structure in the three-dimensional medical image.

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.

Ultrasound attenuation coefficient estimation (“ACE”) techniques that can ameliorate frequency power ratio curve oscillations caused by signal interferences, non-uniform tissue structures, or both, are described. The resulting smoothed frequency power ratio curves enable more accurate ACE and reduced region-of-interest (“ROI”) sizes for linear regression.

The utility model relates to the technical field of cell culture, and discloses a chamber structure adaptive to Faraday wave assembly, which comprises a pore plate, N mounting grooves are formed in the top surface of the pore plate, and N is greater than or equal to 1; the N container bodies are arranged on the pore plate in a rectangular array mode or a central symmetry mode and correspondingly arranged in the N mounting grooves, a plurality of culture grooves used for cell assembly are sequentially formed in the container bodies in the longitudinal direction from the top face to the bottom face, and the widths of the culture grooves are gradually decreased. According to the device, internal cells or cell microspheres can be gathered into a sound potential well of a standing wave sound field under the action of acoustic pressure, gravity and buoyancy under the action of the sound field and are tightly arranged according to an expected pattern, and a culture solution is longitudinally stacked in a plurality of culture tanks of which the widths are gradually reduced; longitudinal shape limitation on the tissue structure is realized, and complex three-dimensional longitudinal arrangement is constructed through mutual combination of the two. Therefore, the requirement of constructing any multi-scale and complex cell structure is met. And forming a multi-layer structure.

The invention discloses a microscopic visual detection method for mixing animal-derived adulterated raw materials into fish meal, and particularly relates to the technical field of feed raw material evaluation, and the method comprises the steps of S1, data set construction, S2, sample pretreatment and image acquisition, S3, model construction and training, and S4, model application and result display. The microscopic visual detection method can effectively capture the appearance form, tissue structure, fiber texture, color and luster characteristics and other tiny characters of fish meal and abnormal adulterants under a microscope, and improves the adaptability of the model to environments with different complexity degrees. A visual, accurate and efficient microscopic vision automatic identification method is provided for an adulteration detection link in fish meal quality detection.

The embodiment of the invention provides a detection device and electronic equipment, relates to the technical field of health data detection, and is used for solving the problem of relatively low health detection data precision caused by complex organization structure of a to-be-detected part of a user. In the detection device, a first photoelectric converter is arranged between every two adjacent first light sources, and a second light source is located in a detection area defined by the multiple first light sources and the multiple first photoelectric converters. A first distance H1 and a second distance H2 are respectively arranged between the first light source and at least two of the first photoelectric converters. A third distance H3 and a fourth distance H4 are respectively arranged between the second light source and at least two of the first photoelectric converters. A first light source, a second light source and a first photoelectric converter are distributed in the distribution area of the detection device. A PPG module can be formed between each light source and the corresponding photoelectric converter, the distances between the light sources and the photoelectric converters are different, and the effective depth of the detected skin can be different.

The invention relates to the field of image processing, in particular to a multi-immunohistochemical image processing and analyzing method and system. The method comprises the following steps: acquiring multiple immunohistochemical images at a plurality of time points, and performing sequential serialization fitting to obtain an immunohistochemical image sequence; performing depth visual semantic recognition and frame-by-frame segmentation reconstruction on the immunohistochemical image sequence, and constructing an image frame sequence of each marker; performing cell spatial distance change calculation and spatial expression correlation mining according to the image frame sequence of each marker to generate a time sequence behavior feature of each marker; and carrying out frame-by-frame organization structure analysis on the image frame sequence of each marker to obtain a three-dimensional organization structure model of each region. According to the method, accurate and efficient tissue structure evaluation is realized, and a personalized quantitative diagnosis result is provided.

The invention relates to the technical field of heart structure parameter measurement, and provides a heart structure parameter acquisition method, and a heart video processing method, device and equipment, which can measure relatively accurate heart structure parameters. The method comprises the following steps: obtaining a left ventricle surrounding key point set, a left ventricle cardiac cavity structure boundary and a left ventricle surrounding tissue structure boundary according to a cardiac image map of a left ventricle long-axis section; according to the left chamber long axis direction determined by the key point set around the left chamber, an inner diameter measuring line of the left chamber is obtained; and determining the inner diameter of the left ventricle and the thickness of the tissue around the left ventricle according to the intersection information of the inner diameter measuring line and the heart cavity structure boundary and the tissue structure boundary.

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.

According to the method and the system for optimizing the interface strength of the ink-jet printing film sensor, provided by the invention, the meso-structure characteristics of the ink-jet printing film sensor with different process parameters under a multi-dimensional process type are observed and acquired, and the LSTM neural network is trained by an ink-jet process parameter matrix and the three-dimensional meso-structure characteristics; obtaining a trained neural network model; a load displacement curve of a scribing head is calculated and obtained through a preset micro-mechanical model and a phase field fracture model, the real interface bonding strength is obtained, the real interface bonding strength is introduced, the trained neural network model is trained again, and a process parameter-micro-structure-interface bonding strength coupling structure-activity relationship is obtained; and in combination with a genetic algorithm, optimizing the bonding strength of the current interface according to the corresponding process parameters in the ink jet process parameter matrix. The parameter optimization period is shortened, the interface strength optimization cost is reduced, and the optimization efficiency is improved.

The invention provides a myopia grading method driven by coupling of mechanical and morphological characteristics of corneal cells, and belongs to the technical field of myopia eye disease diagnosis and occurrence and development mechanisms based on artificial intelligence. The surface topography, Young modulus, adhesive power and other mechanical characteristics of corneal cells of patients with low, medium and high myopia are measured, corneal cell surface topography images obtained by an atomic force microscope are used as a cytoskeleton quantitative analysis data set, and edge extraction, brightness detection and multi-scale image segmentation methods are utilized to determine the surface topography of the corneal cells of the patients with low, medium and high myopia. The overall cytoskeleton distribution characteristics are quantified through the cytoskeleton region average complexity and cytoskeleton region average similarity, the method does not depend on dyeing, the tissue structure of the cytoskeleton can be evaluated more comprehensively, the method is suitable for more diversified cytoskeleton structures including complex clusters, a myopia grading model is established, and the myopia grading efficiency is improved. And a more direct cellular-level experimental basis is provided for the research on the occurrence and development mechanism of myopia.

The invention provides a multi-modal tissue segmentation method and a surgical navigation system, and the method comprises the steps: inputting a multi-modal three-dimensional medical image into a corresponding deep learning network model for segmentation, and obtaining a corresponding three-dimensional segmentation result; registering the three-dimensional medical image of each modal to obtain a registration relation; and according to the registration relationship, combining the preset weight of the three-dimensional medical image of each modal for each type of tissue, and carrying out weighted fusion on the three-dimensional segmentation results of each modal. According to the method, the segmentation model is trained for the medical image of each modal, and each deep learning model has higher segmentation capability for the medical image of the corresponding modal and higher segmentation precision; different types of medical images respectively design respective preset weights (modal weights) for each type of tissues, and the segmentation results are fused according to the modal weights in the fusion process, so that the tissue structure features are reflected to a greater extent, and the segmentation precision is improved.

The invention relates to the technical field of facial ultrasonic image recognition, and particularly discloses a facial structure artificial intelligence recognition, quantification and contrastive analysis method and system, and the method comprises the steps: obtaining facial ultrasonic images at multiple angles; based on a trained image recognition model, recognizing tissue structures of the facial ultrasonic image, and obtaining masks of the tissue structures; calculating a quantitative index of each organization structure based on the mask of each organization structure; the quantitative indexes comprise an average thickness quantitative index, an average area quantitative index and an average volume estimation quantitative index; and comparing the quantitative indexes of the plurality of angles to finish analysis. According to the method, the image segmentation neural network is trained based on the section images of a large number of facial structures, a customized model can be provided for the complexity of the facial ultrasonic image, the accuracy and robustness of tissue structure recognition are remarkably improved, and a high-quality mask is provided for subsequent quantitative analysis.

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 an image CT (Computed Tomography) image enhancement method and system. The image enhancement method comprises the following steps: acquiring a CT image; preprocessing the CT image to obtain a feature vector; clustering different tissue structures in the CT image based on the feature vectors to obtain tissue structure images corresponding to different categories; and performing saturation transparent adjustment on the tissue structure image to obtain an adjusted CT image. The visual difference of the tissue structure can be enhanced from multiple angles, the overall quality and readability of the CT image are improved, and clear and accurate information support is provided for image observation and diagnosis of doctors.

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.