141 results about "Tissue segmentation" patented technology

The present invention relates to the technical field of stomatology. Provided is an auxiliary dental implant generation method based on a diffusion model. The method in the present invention comprises: acquiring oral CBCT image data of historical patients, preprocessing the oral CBCT image data of the historical patients to obtain a CBCT image dataset, using the CBCT image dataset to train a multi-task segmentation network, and using the segmentation network to obtain an intraoral tissue segmentation result; using the intraoral tissue segmentation result to train detection networks from the three dimensions of a cross-sectional plane, a coronal plane and a sagittal plane, respectively; using the detection networks to obtain detection results in the three directions of the cross-sectional plane, the coronal plane and the sagittal plane; fusing the detection results in the three directions of the cross-sectional plane, the coronal plane and the sagittal plane, and using a majority voting algorithm to construct a three-dimensional bounding box, so as to acquire an edentulous area; and using the intraoral segmentation result and the edentulous area as prompt information to guide, by means of an iterative process, a network to generate a post-implantation effect. The implantation effect obtained by the present invention is highly accurate, thereby providing a more precise auxiliary tool for stomatology.

The invention provides a multi-angle photographing and three-dimensional reconstruction method and system for soft tissue, and relates to the technical field of soft tissue three-dimensional reconstruction, and the method comprises the steps: analyzing soft tissue features based on deep learning, generating a bulb tube motion track and sampling interval data, and controlling a mechanical arm to drive a bulb tube to collect projection images at multiple angles; screening an optimal image sequence by using an image quality evaluation network, and performing preprocessing such as material decomposition, feature extraction, space registration and image optimization; and inputting the optimized image and the material features into a reconstruction model, performing iterative calculation in combination with an anatomical structure template to generate density distribution data, performing curved surface reconstruction and intelligent tissue segmentation based on graph convolution based on the density distribution data, and finally generating a three-dimensional model containing tissue layering information.

The invention relates to the technical field of prediction analysis, and discloses a lung cancer phenotype prediction auxiliary analysis method and system based on artificial intelligence. The method comprises the following steps: carrying out adaptive preprocessing on a digital image of a lung cancer pathological section to obtain a standardized preprocessed image; carrying out hierarchical progressive tissue segmentation on the preprocessed image to obtain a lung cancer region labeled image; carrying out multi-dimensional feature cascade extraction on the lung cancer region labeled image to obtain a phenotypic feature set; performing hierarchical prediction on the phenotype feature set to obtain an immune phenotype prediction result; performing subtype mapping analysis on the immune phenotype prediction result to obtain a patient diagnosis report; and carrying out multi-dimensional data integration on the diagnosis report of the patient to obtain a personalized treatment scheme. The accuracy and interpretability of lung cancer phenotype prediction are improved through cascade extraction and hierarchical prediction modeling of multi-dimensional features.

The invention discloses a tooth intelligent segmentation and parameterized bionic reconstruction method fusing multi-modal data, and belongs to the field of artificial intelligence and computer-aided biomedical engineering. The method comprises a CBCT intelligent segmentation module, an intraoral scanning intelligent segmentation module, an internal tissue segmentation module, a point cloud generation registration module and a parameterized bionic reconstruction module. The point cloud generation registration module further comprises a point cloud generation sub-module and a point cloud registration sub-module; the parameterized bionic reconstruction module further comprises a dentin reconstruction sub-module, a cancellous bone reconstruction sub-module and a gingival reconstruction sub-module. The invention aims to realize tooth semantic segmentation, point cloud reconstruction and parameterized bionic modeling of an organization structure under cross-modal data fusion, and provides high-precision bionic internal structure information for teeth in combination with an artificial intelligence model and parameterized modeling.

The invention discloses a cerebral apoplexy prediction method and device based on a cerebrovascular image representation group, a medium and a product, and relates to the field of image processing, and the method comprises the steps: constructing a clinical database; according to the multi-modal image feature group data, the clinical feature data and the clinical cause typing of the patient, constructing a clinical cause typing prediction model; according to TOF-MRA, a cerebrovascular tissue segmentation model and voxel-based morphological analysis are adopted to determine cerebrovascular image features, and positioning diagnosis of a responsible blood vessel area is carried out according to the cerebrovascular image features; according to the cerebrovascular image features in the TOF-MRA, the SWI, the T1WI, the T2-FLAIR and the corresponding clinical feature data, a cerebral apoplexy recurrence risk prediction model is constructed; according to the application, the pathogenesis classification of the acute cerebral apoplexy patient can be quickly and accurately realized in a clinical scene, the responsible blood vessel can be accurately identified, and the recurrence risk prediction can be realized in the early stage of the disease.

The invention discloses an image segmentation method and system, and relates to the technical field of image segmentation. According to the image segmentation method, to-be-segmented laryngoscope medical image data is obtained and input into a pre-trained image segmentation model for preliminary segmentation processing, and a laryngoscope structure segmentation image is generated and comprises a plurality of laryngoscope segmentation areas and corresponding segmentation data sets; performing comprehensive analysis to obtain a structure segmentation perception index of each throat segmentation region, performing analysis to obtain a plurality of low-confidence throat regions, and inputting the low-confidence throat regions into an image restoration model in combination with a segmentation data set of a set annular adjacent throat segmentation region for analysis to obtain an enhanced segmentation data set of each enhanced throat region; according to the method, the laryngoscope structure segmentation image is updated through the enhanced segmentation data set of each enhanced laryngoscope region to obtain the structured laryngoscope tissue segmentation image, so that the enhanced regions have the consistency of pixel levels, and the segmentation precision and clinical availability are effectively guaranteed.

The invention discloses a collaborative optimization method and device for three-dimensional tissue segmentation and registration of a brain nerve image, and the method comprises the steps: S01, constructing a segmentation and registration collaborative model which comprises a shared feature encoder, a segmentation path and a registration path, the segmentation path is used for generating a segmentation probability distribution diagram, and the registration path is used for generating a deformation field; s02, acquiring a training set of the brain three-dimensional magnetic resonance image pair; s03, performing cooperative training on the segmentation and registration cooperative model according to a multi-task cooperative loss function, the loss function including segmentation loss, registration loss and a cooperative regularization term, and the cooperative regularization term modulating a deformation field gradient penalty term by using a multi-scale boundary weight map and a tissue-specific mechanical weight; and S04, receiving an image pair to be registered in real time, and inputting the image pair to be registered into the trained segmentation registration collaborative model to obtain a registration result. According to the method, the calculation efficiency can be remarkably improved while the segmentation and registration precision is ensured.

The invention belongs to the technical field of intelligent diagnosis, and particularly relates to an AI combined MRI and clinical JIA diagnosis system and a storage medium. According to the system disclosed by the invention, the early auxiliary diagnosis of the juvenile idiopathic arthritis is carried out by combining artificial intelligence with multi-dimensional and multi-modal information of multi-sequence MRI images of knee joints of children and various clinical information. The main technology of the method is child knee joint tissue segmentation based on deep learning, a multi-dimensional feature extraction strategy based on a segmentation result, and disease classification based on multi-modal feature integration and deep learning. By integrating the multi-dimensional features of the multi-sequence MRI images and fusing different modal features such as image information and clinical information, an auxiliary diagnosis result with high accuracy can be provided. The technology provided by the invention is beneficial to the realization of early diagnosis and early treatment of juvenile idiopathic arthritis, and has a very good application prospect.

The invention discloses a nerve regulation and control intervention system based on time domain interference electrical stimulation, and the system comprises a magnetic resonance image registration module which receives and registers an imported magnetic resonance image, and obtains a to-be-processed MRI image; the head model tissue segmentation module is used for performing head model tissue segmentation on the to-be-processed MRI image; the stimulation simulation module is used for performing stimulation simulation based on the tissue segmentation structure to obtain a front lead field matrix; the intervention coordinate selection module is used for determining a target brain region corresponding to a target spot brain region coordinate position needing stimulation intervention and putting the target brain region into a running sequence; the electrode position arrangement optimization module is used for verifying, optimizing and finely adjusting the electric field intensity of the target brain region under different electrode arrangement schemes to obtain an electrode configuration parameter result and displaying the electrode configuration parameter result; the parameter importing module is used for receiving the selected electrode configuration parameters and imported preset electrical stimulation parameters; and the electrical stimulation module is used for performing electrical stimulation on the target brain region of the target object. According to the application, the accuracy and the effectiveness of nerve regulation and control treatment can be realized.

The invention discloses an artificial intelligence lumbar plexus block ultrasound image recognition and puncture guiding method and device, and the method comprises the steps: carrying out the image segmentation of a lumbar plexus block ultrasound image when the site type information of the lumbar plexus block ultrasound image is a preset site type, and obtaining a preset site type of the lumbar plexus block ultrasound image; obtaining a plurality of tissue segmentation regions, a puncture needle segmentation region and region categories of the plurality of tissue segmentation regions on the lumbar plexus block ultrasound image; generating a puncture direction line of the puncture needle on the lumbar plexus block ultrasound image based on the puncture needle segmentation region; judging whether the puncture direction line passes through a puncturable type tissue segmentation region or not; if the puncture direction line passes through the puncturable type tissue segmentation area, judging whether the puncture direction line passes through the evasion type tissue segmentation area or not; if the puncture direction line does not pass through the avoiding type of tissue segmentation area, an instruction of continuing puncture according to the current puncture direction is sent out. According to the invention, the accuracy of lumbar plexus block ultrasonic image recognition and puncture guide can be improved.

The invention discloses a weak supervision pathological image tissue segmentation method based on text prompt learning. The method comprises the steps of feature extraction and initial class activation graph generation; using an MCRM module to optimize the initial class activation graph to obtain a refined class activation graph; and aggregating the plurality of refined class activation graphs to form a fused pseudo mask, taking the fused pseudo mask as a supervision signal, training a segmentation model, and after the training is completed, segmenting the new pathological image tissue by using the segmentation model. According to the method, a text prompt learning mechanism is utilized to focus the model on learning high-discrimination features, so that the influence of tissue co-occurrence is reduced. An initial class activation graph is optimized through a multi-mode class activation graph refining module, and the integrity of boundary segmentation is enhanced. Meanwhile, pseudo masks from different network layers are fused to train a segmentation model, and semantic segmentation of the pathological image is realized. According to the method, high-annotation data dependence is effectively relieved, and the generalization ability of the model is improved, so that application in the field of artificial intelligence-assisted medical treatment is promoted.

The invention discloses a PET / MR (positron emission tomography / magnetic resonance) all-in-one machine-based dynamic attenuation correction and respiratory and cardiac dual motion correction method. The method comprises the following steps of: generating a dynamic mu graph of a respiratory cycle; mR cardiac function movie sequence scanning of the cardiac cycle; accurately segmenting the cardiac muscle; 6, performing tissue segmentation on the dynamic mu graph; clustering the PET data; pET data space-time registration is carried out; merging PET data of the same time phase; performing frame-by-frame dynamic attenuation correction; respiratory movement correction; correcting a partial volume effect; carrying out myocardial MR image space standardization on different time phases of the cardiac cycle; and carrying out spatial standardization on the myocardial PET image of different time phases in the cardiac cycle. According to the method, the mu graph of the respiration and cardiac cycles is reconstructed through time-phase sharing and is accurately registered with the PET data, so that the high-quality attenuation correction of the heart PET image is realized and the motion correction is completed at the same time.

The invention discloses a high-precision and automatic dental crown generation method. The method comprises seven steps of oral cavity three-dimensional data acquisition, full-mouth tooth semantic segmentation, upper and lower jaw point cloud precise registration, abutment and associated gingiva precise segmentation and trimming, abutment surrounding environment multi-tissue segmentation, neck-edge line secondary precise detection, and dental crown generation and multi-dimensional refinement. The method comprises the steps of oral cavity three-dimensional data acquisition, full-mouth tooth semantic segmentation, upper and lower jaw point cloud precise registration, abutment and associated gingiva precise segmentation and trimming, abutment surrounding environment multi-tissue segmentation, neck-edge line secondary precise detection and dental crown generation. Through combination of deep learning and a point cloud algorithm, high-precision segmentation of teeth and gingiva and precise reduction of an occlusion relationship are realized, an oral anatomical feature library and a clinical repair standard are fused, and through multi-dimensional fine adjustment optimization, a dental crown 3D model which is high in fitting degree, harmonious in occlusion and capable of meeting clinical requirements is generated. The method is full-process automatic, greatly improves efficiency, reduces operation threshold, reduces material waste and diagnosis and treatment cost, remarkably improves repair success rate, and is suitable for various oral repair scenes.

The invention discloses a method, device and equipment for generating a head model, a storage medium and a program product. The method is characterized by comprising the following steps of: receiving and verifying original three-dimensional T1 weighted image data and an original three-dimensional grid model; performing tissue segmentation based on the verified three-dimensional T1 weighted image data and the original three-dimensional T1 weighted image data to obtain a cerebral grey matter mask, a cerebral white matter mask, a scalp mask and a skull mask; performing topological structure repair on the brain grey matter mask and the brain white matter mask to obtain a brain mask; respectively converting the scalp mask, the skull mask and the brain mask into a scalp mesh model, a skull mesh model and a brain mesh model; calculating and applying a spatial transformation matrix from the verified three-dimensional grid model to the scalp grid model, and performing spatial registration and fusion on the scalp grid model, the skull grid model and the brain grid model to obtain a head model; the method has the advantages that the efficiency, precision and robustness of head model generation are effectively improved, and the method has good cross-platform deployment capability.

The invention relates to the field of artificial intelligence and medical image processing, in particular to a mammary gland pathology visual model establishing method based on multi-model fusion and combined distillation, which comprises the following steps: performing tissue segmentation and dyeing normalization on a full-slice image; inputting the image blocks into a pre-training teacher model of a plurality of freezing parameters in parallel to extract high-dimensional features, and generating unified enhanced features through a learnable feature fusion network; constructing a student model, and performing end-to-end training by using a joint loss function including feature simulation, logic output distillation and multi-task supervision; and connecting a plurality of task specific prediction heads to the student model, and realizing full-slice-level multi-task diagnosis and treatment prediction through an aggregation strategy. According to the technical scheme, efficient knowledge migration and multi-task cooperation can be achieved, and the accuracy, generalization ability and reasoning efficiency of mammary gland pathology analysis are remarkably improved.

The invention relates to a fetal brain tissue segmentation method based on prior information guidance, and the method comprises the following steps: obtaining a public data set, and selecting a part of data in the public data set as a training set; preprocessing the training set to obtain a grey-white matter volume ratio, an edge feature map and a gestational week feature map; constructing a prior segmentation model GA-Swin, and designing a loss function LTotal to train the model; and finally obtaining a trained fetal brain segmentation model. According to the method, on the basis of following the development law, each tissue of the fetal brain can be precisely segmented, and collaborative optimization of precision interpretability and biological rationality in medical image analysis is achieved.

The invention provides a method for constructing a CT-MRI (Computed Tomography-Magnetic Resonance Imaging) personalized three-dimensional heart model based on multi-modal imaging, which comprises the following steps of: firstly, carrying out myocardial and infarction region segmentation on a CMR-LGE image, and carrying out myocardial and intramyocardial adipose tissue segmentation on a CE-CT image to generate a corresponding Label image; then, a high-precision three-dimensional volume mesh model of the corresponding ventricle is constructed based on the segmentation results of the two types of images; and then, mapping the corresponding Label image with the tissue type to a corresponding finite element model according to the coordinates of the grid, and obtaining a corresponding personalized ventricular model with the tissue type. An interpolation method based on a general ventricular coordinate system is adopted, data of an MR ventricular model with infarct tissue attributes is transferred to a CT ventricular model, and therefore a mixed CT-MRI ventricular model is constructed. And finally, storing the constructed CT-MRI ventricular model data with the integrated tissue attributes as a standard geometric and topological file format.

The invention discloses a sacral plexus and sciatic nerve ultrasound image recognition and puncture guide method and device, and the method comprises the steps: inputting a sacral plexus and sciatic nerve ultrasound image into a target image classification model, and carrying out the classification of the sacral plexus and sciatic nerve ultrasound image, and obtaining the image types of the sacral plexus and sciatic nerve ultrasound image; determining a region category set based on the image categories of the sacral plexus and sciatic nerve ultrasound images; inputting the sacral plexus and sciatic nerve ultrasonic image into a target image segmentation model and carrying out image segmentation on the sacral plexus and sciatic nerve ultrasonic image to obtain a plurality of tissue segmentation regions and region categories of the plurality of tissue segmentation regions on the sacral plexus and sciatic nerve ultrasonic image; and sending prompt information based on the plurality of tissue segmentation regions and the region categories of the plurality of tissue segmentation regions. According to the method, the accuracy of sacral plexus and sciatic nerve ultrasonic image recognition and puncture guiding can be improved.

The invention discloses a submucous membrane multi-point thickness measuring method for endoscopic submucous membrane dissection. The submucous membrane multi-point thickness measuring method comprises the following steps that images are collected, wherein pathological images cut by the endoscopic submucous membrane dissection are obtained; image preprocessing: improving the brightness of the pathological image and the contrast ratio of a bright part, and smoothing the pathological image through Gaussian filtering; image feature extraction: extracting texture features of the pathological image, using a local binary pattern (LBP) algorithm, and generating a vertical coordinate feature map with the same size as the pathological image; feature fusion: superposing the preprocessed noise reduction image, the texture feature image and the ordinate feature map; submucous membrane pathological tissue segmentation: using a UNet neural network to carry out image segmentation task training on the superimposed image; calculating the thickness of multiple points; and quality evaluation. According to the method, the thickness of the submucous membrane is measured through the pathological tissue image, and ESD effect judgment and operation quality control are facilitated.

The invention discloses a pericardium adipose tissue segmentation method, device and equipment based on an MR image and a medium. The method comprises the following steps: acquiring a target heart magnetic resonance image containing pericardium adipose tissue of a target object; segmenting pericardium adipose tissue in the target heart magnetic resonance image based on a tissue segmentation model to obtain target pericardium adipose tissue of the target object; the tissue segmentation model comprises a three-branch cross-domain feature collaborative encoder, a double attention feature fusion module and a dynamic boundary perception decoder, and the three-branch cross-domain feature collaborative encoder performs feature extraction from global, local and frequency domains based on a three-branch structure; the double attention feature fusion module is established based on a space attention mechanism and a parallel channel attention mechanism, and the dynamic boundary perception decoder is used for enhancing the global and boundary perception capability. According to the scheme, the pericardium adipose tissue in the heart MR image can be accurately and efficiently segmented by using the tissue segmentation model based on multi-feature collaboration.

The invention relates to the technical field of medical training, and discloses a lumbar puncture simulation teaching system based on an AR technology, and the system comprises an AR content generation module which collects a lumbar medical image and carries out the tissue segmentation and focus recognition; performing three-dimensional geometric modeling based on the tissue segmentation result and the focus recognition result to generate a lumbar vertebra 3D model; the AR display interaction module develops an interaction function of the lumbar vertebra 3D model by using a Unity platform and AR equipment; interactive operation of the user is monitored, and feedback is conducted in real time; and the operation evaluation module is used for evaluating the interaction operation of the user and assigning an improvement suggestion according to an evaluation result. A three-dimensional visual scene is constructed through the AR technology, a user can observe the needle inserting process from different angles according to the complex anatomical structure of the lumbar vertebra part, and dynamic effects such as cerebrospinal fluid pressure change are simulated; in the lumbar puncture simulation training, the AR system can feed back operation data in real time, such as the needle inserting angle, the puncture strength and the puncture depth, and the user is helped to accurately master operation skills.

The invention discloses a benign and malignant identification and growth prediction system based on pulmonary nodule radiomics, and relates to the technical field of medical image processing. Comprising an acquisition module used for acquiring a pulmonary nodule segmentation mask, a CT value and surrounding tissue segmentation data; the calculation module is used for synchronously calculating a first score representing internal CT value distribution heterogeneity, a second score representing surface complexity and a third score representing blood vessel interaction according to the data; the judgment module is used for triggering a high-risk alarm when the scores of the three items all exceed corresponding threshold values; the prediction module is used for correcting the basic growth model according to the three scores and generating growth prediction data; and the output module outputs an alarm and a prediction result. The system also comprises optimization modules of weight fusion, time sequence processing, growth partitioning and the like. According to the scheme, through multi-dimensional feature fusion and dynamic modeling, more accurate identification of benign and malignant pulmonary nodules and more reliable growth trend prediction are realized, and key support is provided for clinical decision making.

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 provides an HER2 immunohistochemical pathological image full-automatic quantitative diagnosis method and device and a readable storage medium, the method comprises a foreground tissue segmentation algorithm and an intensity segmentation algorithm, foreground tissue segmentation is based on a Yov8 model, intensity segmentation is based on a PIDNet model, and multi-scale two-stage iteration training is adopted. Through the steps of image screening processing, data labeling, model training and the like, a foreground tissue area can be accurately segmented, the intensity can be subdivided, and meanwhile diagnosis result verification of a pathologist is introduced. According to the method, the defects of a traditional manual film reading and image segmentation method can be overcome, high-precision tissue segmentation and efficient full-automatic quantitative diagnosis are achieved, and the method has wide application prospects in the fields of tumor diagnosis, medicine research and development and the like and has the advantages of being high in automation degree, high in robustness, good in expandability and the like.

The invention provides an automatic segmentation and region division imaging method and system for ultrasonic velocity estimation, a storage medium and electronic equipment, and the method comprises the steps: dividing an ultrasonic B image into a plurality of tissue regions based on an edge recognition result; obtaining channel data in the ultrasonic imaging process; based on the sound velocity range, performing beam forming according to the channel data; based on the reconstructed image at each wave velocity, according to each tissue area, performing focusing quality evaluation; determining the optimal sound velocity of each tissue area based on the focusing quality evaluation result of each tissue area; based on the optimal sound velocity of each tissue area, performing color coding mapping on each tissue area to obtain a sound velocity color image of each tissue area; and superposing the sound velocity color image of each tissue area with the ultrasonic B image to obtain a final ultrasonic sound velocity image. Through automatic tissue segmentation and region division, in combination with accurate sound velocity estimation and visual sound velocity color coding mapping, the method has a wide application prospect and an important clinical value.

The invention discloses an immediate implantation operation method and system based on augmented reality. The method comprises the steps that CT data of the oral cavity and the head of a patient are acquired and subjected to denoising and normalization processing; the method comprises the following steps: constructing oral digital twins of a patient based on CT data, and completing tissue segmentation, key position marking and gravity center calculation; calculating tooth extraction and implantation paths through operation design software, dynamically predicting stress states of the alveolar bone at different implantation angles and depths based on finite element analysis, and generating an implantation planning scheme; accurately registering the digital twin with the oral cavity of the patient by using multi-point identification and an SLAM (Simultaneous Localization and Mapping) algorithm, and establishing a space mapping relation of an implanting tool; a doctor wears AR glasses to carry out surgery operation, a virtual guiding path and implanting depth and angle information are displayed in real time, and a three-dimensional virtual resistance simulation curved surface is overlaid to prompt bone wall resistance changes; tooth extraction and implantation operations are completed according to AR prompts, stable fusion of the implant and a tooth extraction socket or a bone wall is achieved, and data of the whole operation process are recorded for postoperative analysis.

Segmentation or other classification of digital pathology images with a deep learning model allows for sophisticated spatial features for cancer diagnosis to be extracted in an automated, fast, and accurate manner. A tiered analysis of tissue structure based in part on deep learning methods is provided. First, tissues depicted in a digital pathology image are segmented into cellular compartments (e.g., epithelial and stromal compartments). Second, the heterogeneity in the different cellular compartments are examined based on a clustering algorithm. Tissue can then be characterized in terms of inertia (or other spatial measures or features), which can be used to recognize disease. In some instances, multidimensional inertia (i.e., inertia computed in different cellular compartments or clustered components) can be used as an indicator of disease and its outcome.

The invention discloses an oral cavity image multi-tissue full-automatic segmentation method, which belongs to the technical field of oral cavity image multi-tissue segmentation, combines multi-stage classification with image processing, and improves the accuracy of image recognition, enhances the depth of image understanding and improves the efficiency of image processing through the cooperative operation between the multi-stage classification and the image processing. By means of the multi-stage classifier, images can be classified from local to global and from rough to fine, the classification mode is beneficial to more accurately recognizing target objects or features in the images, in the image processing process, the region of interest can be gradually reduced through multi-stage classification, the target judgment capacity is enhanced in each stage, and the target recognition efficiency is improved. In this way, efficient and accurate target detection is achieved, in some cases, multi-level classification can be combined with other image processing tasks for learning, and the efficiency and accuracy of the model are improved by sharing the feature extractor and the model parameters.

The invention particularly relates to an algorithm based on cervical tissue squamous epithelium lesion diagnosis. The algorithm comprises the following steps of tissue segmentation, region cutting and a classification algorithm. According to the method, multi-step fine processing is carried out on a cervical tissue slice image, for example, in the tissue segmentation stage, gray transformation and Gaussian filtering are adopted to enhance image features, and a cross entropy loss function is used to optimize model parameters; flexible segmentation is carried out according to form contour information during region cutting; in the classification algorithm, image preprocessing and data enhancement are carried out, and a multi-layer neural network is utilized to extract and analyze features, so that squamous epithelium lesion tissues can be identified more accurately, the lesion degree is judged comprehensively, and the diagnosis accuracy is improved.

The invention discloses a full-computer intelligent high-similarity prosthesis production method and system, and the method comprises the steps: a main computer obtains a 3D model containing the segmentation information of all tissues in a missing part of a patient through a pre-constructed artificial intelligence model by collecting the information of the patient, and obtains a 2D unfolded image for the surface layer of the 3D model based on the information of the patient and the 3D model; controlling 3D printing equipment to print the 3D model to obtain each layered part of the prosthesis main body; controlling film manufacturing equipment to manufacture a surface layer film of the missing part; controlling 2D printing equipment to print a 2D unfolded image on the surface layer film of the missing part to form a skin; assembling equipment is controlled to combine all the parts of the prosthesis, the skin is attached to the surface of the prosthesis body, surface treatment is conducted, and a finished prosthesis product is obtained. According to the invention, the prosthesis production process is computerized and intelligent, the high-precision and high-similarity prosthesis can be provided in a shorter time, and the manufacturing cost is reduced; the artificial intelligence technology is used for prosthesis design and modeling, and the quality and similarity of the prosthesis can be improved.