299 results about "Lesion segmentation" patented technology

The invention discloses a medical image segmentation method and system based on guidance information and a multi-dimensional attention mechanism. The method comprises the following steps: collecting an original dermatoscope image for preprocessing; constructing a segmentation model, wherein the segmentation model comprises a double-path image encoder, a guide information encoder and a mask decoder; the two-way image encoder is used for extracting local detail features and global context semantic information in the image; the guide information encoder is used for converting a coarse segmentation mask predicted by the last round of network into guide feature information; the mask decoder fuses the image feature information and the guide feature information, gradually restores and refines the coarse-grained feature map, and finally outputs an accurate lesion segmentation mask; constructing a loss function, and training the segmentation model by using the preprocessed data; and inputting a to-be-segmented original dermatoscope image into the trained segmentation model, and outputting a lesion region segmentation mask of the image. According to the method, the segmentation precision and the model generalization ability can be improved, and the multi-scale lesion processing ability is enhanced.

The invention relates to the field of medical image processing, in particular to a lung cancer PET-CT fusion segmentation method and system based on multi-modal feature comparative learning, and the method comprises the steps: firstly extracting PET and CT image features, projecting the features to a shared semantic space through a semantic guide type symmetric comparative learning architecture, obtaining key region features through a focus adaptive attention sampling mechanism, and carrying out the segmentation of a target region; optimizing feature representation through a cross-modal feature difference self-calibration mechanism, constructing a multi-scale feature pyramid, fusing features of different scales by using a multi-scale hierarchical contrast learning mechanism, and performing self-supervised learning by combining an anatomical guidance self-supervised contrast learning enhancement module and using a CT anatomical structure, so as to further reinforce the features; a high-precision lung cancer lesion segmentation result is generated through a decoder network, the Dice coefficient is increased from 0.78 to 0.91, and the detection rate of lesions below 10 mm is increased from 65% to 87%. A novel efficient and accurate image processing method is provided for lung cancer diagnosis.

The invention discloses a lesion segmentation lightweight method applied to a mammary gland medical detection image, and relates to the technical field of medical image analysis. The method comprises the following specific steps: (1) acquiring a mammary gland medical detection image data set, performing preprocessing operations such as size standardization and data enhancement on an image, and dividing the image into a training set and a verification set; (2) a lightweight medical image segmentation model of a U-shaped coding and decoding architecture is constructed based on deep learning, an encoder of the model adopts an axial depth separable convolution block, and a decoder integrates a hierarchical scale perception fusion block; and (3) inputting the preprocessed training set image into the model, and training the constructed lightweight segmentation model. And (4) inputting the verification set into the trained model, evaluating segmentation precision through indexes, and adjusting and optimizing hyper-parameters according to a result to obtain a verified model. And (5) carrying out preprocessing such as size normalization and noise suppression on the to-be-segmented breast medical detection image. And (6) inputting the preprocessed image into the verified lightweight model, and outputting a pixel-level focus segmentation result to assist clinical diagnosis. According to the method, the model parameter quantity and computing resource requirements are remarkably reduced through lightweight architecture design, the reasoning speed is increased while the segmentation precision is optimized, the method is suitable for application scenes with limited resources, and efficient technical support is provided for rapid and accurate diagnosis of breast cancer lesions.

The invention belongs to the field of medical image processing and analysis, and provides a breast ultrasound image lesion segmentation method and system based on elastic semantic decoupling, and the method comprises the steps: obtaining a disclosed breast ultrasound image data set, carrying out the data enhancement of a breast ultrasound image, and randomly dividing a training set and a test set according to a corresponding proportion; an elastic semantic decoupling segmentation network is constructed, and the elastic semantic decoupling segmentation network is mainly composed of an encoder, a decoder and a jump connection; training the constructed elastic semantic decoupling segmentation network model by using the data of the training set to generate a model weight; testing the model by using the image data of the test set, and evaluating a segmentation result to obtain a trained elastic semantic decoupling segmentation network model; and performing focus segmentation prediction by using the trained elastic semantic decoupling segmentation network model. According to the method, challenges of pixel intensity similarity fluctuation and space detail loss can be coped with at the same time, so that the accuracy and robustness of breast ultrasound image lesion segmentation are improved.

The invention relates to the technical field of medical image processing and artificial intelligence, and discloses an artificial intelligence-based retinochoroid disease course structure change monitoring system, which comprises an image acquisition unit, a feature extraction unit, a lesion segmentation unit and the like. The image acquisition unit acquires retina optical coherence tomography sequence image data and fundus color image data; the feature extraction unit extracts a retina choroidal structure feature tensor through a three-dimensional convolutional neural network; the lesion segmentation unit outputs a lesion area probability distribution diagram by using a U-Net segmentation network; the time sequence alignment unit is used for registering the multi-time-point images to generate a displacement change matrix; the dynamic analysis unit extracts related indexes; an abnormal scoring unit constructs a disease course progress score; the decision grading unit outputs disease course stage classification labels; the multi-modal fusion unit fuses the multi-modal features; the report generation unit generates a structured disease course monitoring report. And favorable support is provided for diagnosis and treatment of ophthalmic diseases and illness state tracking.

The invention discloses a lesion area self-adaptive segmentation method and system for a seminal vesicle endoscope image, particularly relates to the field of medical image processing, is used for solving the problems of geometric distortion and artifacts in the seminal vesicle endoscope image, and aims to eliminate geometric deviation caused by thick layer sampling through synchronous acquisition and attitude correction. Then, a resampling strategy is adjusted in a self-adaptive mode through key geometric features, the problems of inter-layer artifacts and resolution imbalance are effectively weakened, a lesion segmentation network is optimized through smooth regularization and geometric constraint, continuity and geometric accuracy of lesion boundaries are ensured, finally, the accurate lesion mask is dynamically overlaid to a real-time frame stream, and the real-time frame stream is obtained. A quantitative basis is provided for biopsy path planning and photodynamic dose scheduling; smooth and continuous images are completed and output in a strict time window, the perception ability of an operator to tiny pathological changes is enhanced, meanwhile, the method is suitable for various endoscope devices, motion blur and light spot artifacts are restrained, and focus details are kept clear.

The invention relates to the technical field of image processing, in particular to a clinical focus auxiliary segmentation system based on a nuclear magnetic resonance image. The system comprises an MRI image processing module, a lesion auxiliary segmentation module, a probability segmentation correction module and a lesion boundary smoothing module, a corresponding clinical nuclear magnetic resonance image set of a patient can be obtained, image position alignment and gray level adjustment processing can be carried out, and meanwhile a corresponding clinical image lesion segmentation model is constructed to carry out multi-scale fusion auxiliary segmentation. Generating a clinical focus region segmentation fusion image; obtaining a focus confidence probability corresponding to each pixel point in the segmentation image through the clinical focus region segmentation fusion image, and carrying out probability segmentation boundary correction on the clinical focus region segmentation fusion image to obtain a clinical focus region segmentation correction result image; and performing focus edge shape smoothing processing on the clinical focus region segmentation correction result map to generate a clinical focus edge shape segmentation optimization result. According to the invention, high-precision segmentation of the focus in the MRI image can be realized.

The invention discloses a colorectal cancer focus segmentation method based on improved TransUNet. The method comprises the following steps: collecting related pathological image data of a colorectal cancer patient; enhancing and expanding the data set by adopting a data enhancement technology, and adjusting and processing the image data; the method comprises the following steps: constructing a model, integrating a PEMA module at multiple positions of the model, introducing an EUCB up-sampling module into a decoder part, replacing standard convolution of the decoder part with lightweight dynamic convolution, and using a composite loss function MediBoundFusion Loss; the preprocessed training data set is input into the improved TransUNet network model to be trained; and the colorectal cancer focus is segmented by adopting the improved TransUNet network model after training is completed. The key problems that focus features are fuzzy, boundaries are difficult to define, forms are irregular, and effective features are difficult to extract due to low contrast of early cancerous tissues can be solved.

The invention belongs to the field of medical image processing, relates to a medical image focus segmentation and three-dimensional reconstruction method and system based on artificial intelligence, and aims to solve the problem of low model precision caused by mutual isolation of segmentation and reconstruction and unidirectional transmission of errors. The method comprises the following steps: fusing a multi-modal medical image; a segmentation network is adopted to generate a preliminary focus mask; constructing an initial three-dimensional geometric surface based on the mask, and performing physically-driven curved surface optimization; reversely projecting the optimization model to the feature space of the segmentation network, calculating the spatial inconsistency between the optimization model and network prediction, and generating an attention weight map; feeding back the attention weight map to the segmentation network, and iteratively updating network parameters to refine segmentation boundaries; and based on the final segmentation result after convergence, three-dimensional reconstruction guided by the network features is executed again. According to the method, a closed-loop feedback and collaborative optimization mechanism between segmentation and reconstruction is constructed, and the accuracy of focus segmentation and the geometric fidelity of a three-dimensional reconstruction model are remarkably improved.

The invention provides an automatic focus identification and grading method for a medical image, and the method comprises the steps: carrying out the standardization of an obtained multi-modal original image based on anatomical constraint, and obtaining a standardized image; generating semantic enhancement features through a cross-modal feature compensation network based on the standardized image and associated radiological text description; performing dynamic feature adaptation processing on the semantic enhancement feature to generate a modal adaptive feature; performing context reasoning through a multi-scale feature interaction algorithm based on the modal adaptive features to generate context reasoning features; and lesion identification decoding processing is carried out on the context inference feature map, a lesion segmentation mask is generated, and the lesion segmentation mask is used for extracting lesion area feature parameters to carry out lesion classification. By adopting the method, the adaptability to the missing mode can be enhanced, and the focus identification and grading precision can be improved.

The invention relates to the technical field of image processing, and discloses a pulmonary embolism focus segmentation method and system based on image recognition, and a storage medium. The method comprises the following steps: extracting a multi-level blood vessel topological structure of a CTPA image through blood vessel diameter gradient analysis; modeling blood vessel density distribution by using a Weibull mixed model to obtain embolism characteristic parameters; the pixel embolism probability is estimated through variational Bayesian reasoning, and a focus distribution diagram is generated; performing multi-scale feature fusion on the lesion probability graph to obtain a segmentation boundary; and obtaining a final embolism focus segmentation result based on the vascular connectivity constraint optimization boundary. The problems that blood vessel level differentiation processing cannot be achieved, and accurate probability modeling and anatomical constraint verification are lacked are solved. The accuracy of pulmonary embolism focus segmentation is improved.

The invention discloses a focus segmentation method fusing PET and CT bimodal images, and relates to the technical field of computers. The method comprises the following steps: acquiring focus image data, separating the focus image data to obtain CT data and PET data, and inputting the CT data and the PET data into a bimodal medical image segmentation model to obtain a preliminary segmentation result; a difference region is generated based on the preliminary segmentation result, difference region detection is carried out on the difference region, and click signal data is generated; the difference region is a region which is not marked as a focus in the preliminary segmentation result; training the bimodal medical image segmentation model for a preset number of times through the click signal data, the CT data and the PET data to obtain a segmentation result; and determining a segmentation result generated by the last training as a lesion image segmentation result. The method can improve the medical image lesion segmentation precision.

The invention provides a dermatoscope image segmentation method based on an attention mechanism and UNet, and belongs to the technical field of medical image segmentation of image segmentation. The technical problem that a traditional method is insufficient in accuracy in lesion region segmentation is solved. According to the technical scheme, the method comprises the following steps: 1, data preprocessing: carrying out image denoising, image enhancement and division on a data set; step 2, constructing a dermatoscope image segmentation network based on an attention mechanism and UNet; 3, putting the processed skin disease image training set into a dermatoscope image segmentation network model based on an attention mechanism and UNet for training to obtain an optimal model; and 4, after training is completed, inputting the test set into the optimal model, and detecting a focus segmentation result in the skin disease image. The method has the advantage that high segmentation accuracy and robustness are guaranteed.

The present application discloses a semi-supervised medical image segmentation method and related devices based on contrast manifold regularization. The method includes: importing and preprocessing medical image datasets; initializing a teacher model and a student model, using labeled data to train the teacher model and generate pseudo labels; calculating the similarity between the labeled data and the pseudo labels to obtain a manifold regularization term; constructing positive and negative sample pairs and calculating the contrast loss term; weighted summation to obtain the contrast manifold regularization term, and combining it with the supervised loss as the loss function of the student model; iteratively training the student model to obtain the segmentation result. By combining contrast learning with manifold regularization, the present invention effectively alleviates the data dependency problem in semi-supervised medical image segmentation, improves the model generalization ability and segmentation accuracy, and performs particularly well in small target lesion segmentation scenarios.

The invention discloses a Crohn disease focus automatic segmentation and activity evaluation system based on deep learning, which belongs to the field of medical artificial intelligence and comprises a data preprocessing unit, a focus automatic segmentation unit, a radiomics feature extraction unit, a feature screening and dimension reduction unit and an activity classification unit. According to the method, an nnU-Net deep learning segmentation model is combined with image omics feature extraction, multi-stage feature screening and machine learning classification technologies, so that full-process automation from CTE image preprocessing, focus automatic segmentation, feature extraction and screening to activity classification is realized. The system can efficiently and accurately segment the focus of Crohn's disease, automatically assesses the disease activity based on the screened key radiomics characteristics, significantly improves the consistency, objectivity and efficiency of diagnosis, and is suitable for clinical auxiliary diagnosis and scientific research analysis.

The invention discloses a liver cancer focus segmentation method and system based on dynamic feature fusion, and relates to the technical field of liver cancer diagnosis assistance, and the method comprises the steps of multi-source data collection, feature extraction, dynamic fusion, segmentation output and result verification. A 4D image, a multi-posture liver image and a synchronous physiological signal are obtained through the multi-source data acquisition module, features are extracted through the static and dynamic feature extraction module, weights are distributed through the dynamic fusion module, and then processing is conducted through the segmentation output and result verification module. Through cooperative processing of the multi-source data acquisition module, the feature extraction module and the like, a real focus boundary and a motion artifact can be accurately distinguished, and the missing detection condition of a tiny focus caused by fuzzy boundary is effectively reduced; meanwhile, the omission ratio caused by the atypical single posture feature is greatly reduced, the identification accuracy is improved by capturing the stability difference of benign and malignant lesions, finally precise segmentation of the liver cancer focus is achieved, and a reliable basis is provided for clinical diagnosis and treatment decision making.

The invention discloses a rectal cancer medical image lesion segmentation method and device based on deep learning, and aims to solve the problem that in the prior art, only a medical image is used as a single input mode, and potential semantic association among multi-mode data is neglected, so that the segmentation effect is poor. The method comprises the following steps: acquiring a to-be-detected rectal cancer medical image focus image and corresponding clinical text information, and inputting the two images into a segmentation model for processing; the clinical text information is processed through a text multi-scale feature extraction module, and target text multi-scale features are generated; encoding the text features and the medical image by using a feature encoder, and outputting target text image fusion encoding features; inputting the fusion coding feature into a scale attention module, and outputting a target text image fusion attention feature; processing the fusion attention feature and the fusion coding feature through a feature decoder to obtain a target text image decoding feature; and finally, segmenting the decoding features by using a segmentation head, and outputting a final target segmentation result.

The invention discloses a medical image artifact identification and elimination method based on a big data technology, and relates to the technical field of medical image processing, and the method comprises the steps: carrying out the preprocessing of collected image data based on gray normalization, and then carrying out the semantic segmentation and ROI positioning of the image content; and performing lesion segmentation on the positioned image content, selecting lesion features for quantification and fusion, performing model verification, and performing distributed deployment on the verified lesion segmentation model. According to the method, the problems of high missed detection rate of small nodules and high missed diagnosis risk of malignant lesions are solved through the lesion detection model, the false positive rate is reduced, the recall rate of the malignant lesions is improved, and through the lesion segmentation model, the segmentation adaptability to lesions of different sizes is improved, clear segmentation boundaries are obtained, surgical planning is assisted, and boundary positioning errors are reduced.

The invention relates to the technical field of medical image processing, in particular to an embolism focus segmentation method and system based on medical image processing. The system comprises a medical image feature registration module, an image frequency band fusion enhancement module, a deep network lesion segmentation module and a lesion segmentation image fusion optimization module, an embolism CT image and an embolism MRI medical image can be acquired, and feature point minimization registration and image frequency band fusion are performed to generate an embolism fusion image; performing histogram equalization processing on the embolism fusion image to generate an embolism comparison standard image; constructing a corresponding deep network lesion segmentation model, and performing network lesion segmentation processing, up-sampling and element-by-element splicing fusion to generate an embolism lesion feature fusion image; and performing morphological optimization operation on the lesion boundary corresponding to the embolism lesion feature fusion image to obtain an embolism lesion segmentation result. According to the invention, accurate segmentation of the embolism focus in the medical image can be realized.

The invention belongs to the field of medical image processing and artificial intelligence, and provides a colon cancer CT image segmentation method and system fusing individualized features, and the method comprises the steps: obtaining the abdomen CT images of a historical patient and a target patient; obtaining structured individual feature information related to the patient, wherein the individual feature information comprises age, gender, colon cancer family history and intestinal medical history information; preprocessing the abdomen CT image; and training an image segmentation model by using the preprocessed abdominal CT image of the historical patient, segmenting the preprocessed abdominal CT image of the target patient by using the trained image segmentation model meeting the segmentation precision requirement, and generating a colon cancer focus segmentation image conforming to the individual feature difference of the target patient. According to the method, the structured patient information is utilized to guide the model network to adjust the feature response, so that the segmentation precision and individual adaptability of the model are improved, and the segmentation accuracy is further improved.

The invention relates to the technical field of medical images, in particular to a medical image intelligent detection and auxiliary diagnosis system based on deep learning, and the system comprises a data access module which is used for obtaining medical image data and clinical text data of a patient; the data fusion module is used for generating a focus feature vector and a text feature vector, and performing cross-modal alignment and fusion to generate a fusion feature vector; the diagnosis analysis module is used for executing focus detection, focus segmentation and focus classification tasks, generating a diagnosis result and generating a diagnosis label based on the diagnosis result; the decision generation module is used for mapping the generated execution result to a preset medical knowledge base and performing deep reasoning to generate an auxiliary diagnosis decision; the decision auditing module is used for performing confidence scoring on the auxiliary diagnosis decisions and selecting the auxiliary diagnosis decision with the highest confidence score as the final auxiliary decision; and the data visualization module is used for carrying out visualization processing on the auxiliary decision and the diagnosis result.

The invention discloses an intelligent detection method for a brain tumor focus area, and relates to the technical field of medical image processing. The method comprises the following steps: acquiring a multi-modal image; the multi-modal image is an MRI (Magnetic Resonance Imaging) image generated according to T1 weighting, T2 weighting and an FLAIR sequence; the multi-modal image is preprocessed, and a normalized image is obtained; constructing a focus detection model; the focus detection model adopts a double-branch network structure and comprises a global branch, a local branch and a weighted fusion module; the global branch and the local branch are both connected with the weighted fusion module; the weight fusion module adopts a dynamic weight fusion strategy; inputting the normalized image into the focus detection model to generate a preliminary focus segmentation result; and performing post-processing optimization on the preliminary focus segmentation result to generate a final focus positioning result. According to the invention, the detection precision of the brain tumor lesion area can be improved.

The invention discloses a nervous system disease teaching and training method and system based on an image recognition technology, and particularly relates to the field of nervous system disease teaching, and the method comprises the steps: obtaining a first dynamic image sequence and interaction behavior data of a user, marking a first dynamic focus segmentation map through an image recognition model, and obtaining a first dynamic focus segmentation map; performing feature comparison operation with a second dynamic image sequence generated by simulation so as to iteratively optimize the image recognition model; and obtaining an interactive feedback operation of the user on the second dynamic image sequence generated by simulation, and analyzing a multi-index evaluation result to generate a personalized learning path plan. According to the nervous system disease teaching and training method and system based on the image recognition technology, a first dynamic focus segmentation map is generated in sequence, automatic labeling is achieved, and the time consumed by manual labeling and case screening is greatly shortened; the dynamic change of the focus in time and space is captured in real time from multiple dimensions, so that the capability of analyzing the dynamic change of the disease is greatly improved.

The invention, which belongs to the technical field of medical image processing and computer vision, discloses a multispectral imaging-assisted post-radiotherapy skin injury early detection system comprising a multispectral data acquisition module, a deep tissue feature extraction module, a three-dimensional lesion segmentation module and a space-time tracking evaluation module. The subcutaneous 2.5 cm depth tissue information is obtained through multispectral imaging at the wave band of 400-1350nm, blood perfusion, melanin concentration, collagen structure and other physiological parameters are inversed based on the radiation transfer theory, a three-dimensional medical image segmentation algorithm is adopted to achieve three-dimensional accurate segmentation of an injury area, a spatio-temporal evolution model is established to predict the injury development trend, and the damage development trend is predicted. The radioactive skin injury can be detected in the subclinical period, the detection time window is advanced by 5.2 days on average, the occurrence rate of severe dermatitis is reduced by 65%, and a basis is provided for clinical timely intervention.

This application relates to the field of image processing technology and discloses a method and system for processing surgical minimally invasive surgery data based on AR technology. The method includes: performing three-dimensional reconstruction and lesion segmentation on medical imaging data and extracting feature points; using feature point data to acquire the position of surgical instruments and perform spatial mapping; reconstructing the light field based on feature point and position data and generating an augmented reality scene; performing trajectory analysis and path planning based on augmented reality; performing real-time monitoring of the surgical path and risk warning; and finally extracting surgical rules and establishing knowledge mapping relationships. This application implements the correlation analysis and fusion processing of multi-source heterogeneous data during surgery, establishes a complete technical chain from data acquisition to knowledge extraction, and improves the safety and intelligence level of surgery.

The invention provides a pneumonia CT image diagnosis model training method, diagnosis method and equipment, and the training method comprises the steps: inputting a 3D chest CT image into a multi-task deep learning model, enabling a shared encoder in the model to extract multi-scale feature data, and enabling a connection module and a decoder to obtain pneumonia focus region prediction result data according to the multi-scale feature data, the classification head obtains pneumonia diagnosis type prediction result data according to the multi-scale feature data; determining the joint loss of the model in the current iteration round and updating model parameters; and if the current multi-task deep learning model satisfies a training termination condition, outputting the current model as a pneumonia CT image diagnosis model. According to the method, the problems of low model feature utilization rate and low pneumonia diagnosis process efficiency caused by incapability of simultaneously completing focus segmentation and type classification due to task simplification of an existing pneumonia diagnosis model can be solved.

The invention discloses a medical image report automatic generation method and device based on artificial intelligence, relates to the technical field of artificial intelligence and medical image crossing, and aims to shorten the report generation period and improve the report generation efficiency while improving the accuracy and consistency of report diagnosis. The method comprises the following steps: carrying out cross-modal image space alignment processing on a PET image and a CT image; a pre-trained PET focus segmentation model is adopted, a high-metabolism focus part is identified and segmented in the processed PET image, and focus iconography parameters are acquired; segmenting an organ image in the processed CT image by adopting a pre-trained CT image organ segmentation model, and determining focus position information; sorting the lesion iconography parameters and the lesion position information to generate an initial report, and processing the initial report by adopting the fine-tuned large language model to obtain a medical image report and outputting the medical image report.

The invention belongs to the technical field of medical image processing, and particularly relates to a non-functional pancreatic neuroendocrine tumor preoperative index prediction method and system. According to the method, on the basis of abdomen CT image lesion segmentation, pathological grading (low risk / high risk) and lymph node metastasis (LNM) risks are jointly predicted through a multi-task deep learning framework; according to the method, tumor morphological features, radiomics features and clinical parameters (such as tumor size and position) extracted by a segmentation network are combined, and high-precision prediction of preoperative key indexes is realized by using a feature fusion module and a lightweight classification head; through a cross-modal attention mechanism and multi-center data verification, the model achieves the pathologic classification AUC of 0.75 on an internal verification set, achieves the LNM prediction AUC of 0.78, and is significantly superior to traditional clinical experience judgment. According to the method, reliable decision support is provided for preoperative precise operation planning and personalized treatment.

The invention discloses a potato late blight severity grading calculation system and method based on YOLOv8-UNet3Plus, and the system comprises an image collection module which is used for collecting a shadow-free potato leaf image through employing a bidirectional light source and a fixed pose camera; the leaf positioning module is used for optimizing a YOLOv8 network by using a space and channel redundant convolution ScConv and a bidirectional feature pyramid network BiFPN, positioning a plurality of potato leaves in the potato leaf image by using the optimized YOLOv8 network, and outputting bounding box information of each leaf; the scab segmentation module is used for optimizing the UNet3Plus network to obtain a lightweight UNet3Plus network, performing pixel-level segmentation on each leaf area by using the lightweight UNet3Plus network, and distinguishing a healthy area from a disease area; the grading calculation module is used for calculating the disease spot area proportion and dividing the disease severity grade of each leaf; according to the method, lightweight construction of the leaf positioning and scab segmentation network is realized, and the model parameter quantity, the calculation quantity and the terminal deployment cost are remarkably reduced.

The invention provides a pneumonia medical data analysis and processing method, system and device and a storage medium, and belongs to the field of medical image processing. Multi-scale features of the lung CT image are extracted layer by layer through convolution, global pooling operation down-sampling is carried out based on the multi-scale features, and saliency features of lung lobe and lesion areas are captured from local details to global semantics; performing convolution and multi-scale pooling on the saliency features to generate a feature map fusing global and local information; and recovering the feature map through layer-by-layer up-sampling to obtain a segmentation result of the lung lobe and the focus. Quantitative indicators are calculated based on regions and pixels of the segmentation results of the lung lobes and lesions. And performing model processing based on the quantitative index, the segmentation result of the lung lobe and the focus and clinical data of the lung lobe and the patient to obtain pneumonia focus characteristic data. And a doctor can conveniently make an objective and reasonable treatment scheme according to a result of lesion segmentation and feature data calculation based on the pneumonia lesion feature data.