46 results about "Lesion segmentation" patented technology

The application provides a medical image segmentation method and system based on concept guidance and cross-modal alignment, and belongs to the field of medical image processing. The method comprises the following steps: obtaining a medical image to be segmented and its corresponding clinical text description; generating clinical knowledge concepts related to the target disease by using a large language model, and constructing a concept set through clinical review; inputting the medical image, the clinical text description and the concept set into a trained concept-guided segmentation model to extract visual features, text features and concept labels; generating concept features aligned with the visual features through a concept-visual alignment module; dynamically adjusting the normalization process of the visual features through a concept modulation decoder, combining the features through multi-head cross attention, and outputting the final image segmentation result by using a segmentation head. The application effectively solves the problems of lack of effective clinical prior guidance in existing medical image segmentation, poor cross-modal feature alignment, and insufficient lesion segmentation accuracy.

The present application relates to the field of liver cancer immunotherapy efficacy evaluation method combining imageomics and deep learning, and specifically discloses a liver cancer immunotherapy efficacy evaluation method combining imageomics and deep learning. The method comprises the following steps: acquiring multi-modal medical images and clinical information of a liver cancer patient; performing standardization preprocessing and automatic lesion segmentation on the images; extracting features through a multi-scale deep network and realizing semantic alignment by using a cross-modal attention mechanism; fusing the images and the clinical data to construct a multi-source heterogeneous feature matrix; adopting a hierarchical model structure, modeling the spatial distribution of tumor immune microenvironment by using a graph neural network at the bottom layer, dynamically tracking the evolution of efficacy by using a gated recurrent unit at the upper layer, and finally outputting an immune response probability, a tumor load trend and a treatment response grade. The present application can objectively and quantitatively evaluate the efficacy of liver cancer immunotherapy, and improve the precision and automation level of individualized diagnosis and treatment decision-making.

The application discloses a lung inflammation lesion quantitative evaluation artificial intelligence model based on CT images and relates to the field of artificial intelligence models.The model comprises CT image preprocessing, self-supervised lesion segmentation and quantification, multi-modal joint pre-training coding and multi-task diagnosis and prognosis prediction module composition which are sequentially and communicatively connected, and realizes feature interaction through a unified feature embedding space.The model realizes self-supervised lesion segmentation and multi-dimensional quantification by adopting a three-dimensional generative reconstruction network, completes image-text feature fine-grained alignment through cross-modal contrast learning of an adversarial enhancement, and fuses multi-dimensional features.The model can complete multiple tasks such as ARDS diagnosis, non-invasive estimation of P / F ratio, severity grading and prognosis prediction in parallel, reduces dependence on artificial labeling, improves cross-center generalization capability and diagnosis precision, and provides a standardized intelligent tool for clinical evaluation of severe lung inflammation.

The application discloses a kind of based on double branch coding and foreground background difference enhancement's focus segmentation method, including the following steps: obtaining GLAS, Kvasir-SEG and BUSI medical segmentation public dataset and the dataset of physician hand marking segmentation result;Data pre-processing, data enhancement and dataset division;DCDB-Net model is constructed, the DCDB-Net is based on double branch coding and foreground background difference enhancement's focus segmentation model;The DCDB-Net model of S3 construction is trained, and parameter adjustment is carried out;Using the trained model is tested in GlaS, Kvasir-SEG and BUSI dataset.The application effectively alleviates the problem that soft boundary between foreground and background is difficult to distinguish, while reducing the interference caused by the coexistence of significant and non-significant objects in the training phase to the model key feature recognition.

The application belongs to the field of medical image processing, and relates to a medical image lesion segmentation and three-dimensional reconstruction method and system based on artificial intelligence, aiming to solve the problem of low model precision caused by mutual isolation of segmentation and reconstruction and one-way error transmission. The method comprises the following steps: fusing multi-modal medical images; generating a preliminary lesion mask using a segmentation network; constructing an initial three-dimensional geometric surface based on the mask and performing physically driven surface optimization; projecting the optimized model to the feature space of the segmentation network in reverse, calculating the spatial inconsistency thereof with the network prediction, and generating an attention weight map; feeding the attention weight map back to the segmentation network, iteratively updating the network parameters to refine the segmentation boundary; and based on the final segmentation result after convergence, performing three-dimensional reconstruction guided by network features again. The application constructs a closed-loop feedback and collaborative optimization mechanism between segmentation and reconstruction, significantly improving the accuracy of lesion segmentation and the geometric fidelity of the three-dimensional reconstruction model.

This application provides a multimodal ultrasound intelligent identification method and precision diagnosis system for thyroid lesions. The method determines image data blocks with multimodal spatial registration and grayscale-contrast temporal alignment. Based on the grayscale ultrasound image sequence in the image data blocks, a first depth feature map of the target user's thyroid region is determined. A second depth feature map of the target user's thyroid region is determined based on the elastography map in the image data blocks. The hemodynamic spatial distribution characteristics of the parametric map in the image data blocks are captured to determine a third depth feature map of the target user's thyroid region. Cross-modal fusion is performed on the first, second, and third depth feature maps, and then the lesion segmentation mask, benign / malignant probability value, and risk classification of the target user's thyroid region are inferred and output in parallel. Using the scheme of this application, high-precision spatiotemporal alignment and cross-modal depth feature fusion of multimodal ultrasound images can be achieved to complete end-to-end intelligent identification of thyroid lesions.

The disclosure provides a glass membrane wart lesion extraction method and device, the method comprising: performing feature classification processing on a target eye image to determine whether a glass membrane wart lesion exists in the target eye image; if the glass membrane wart lesion exists in the target eye image, detecting the target eye image by using a pre-trained glass membrane wart lesion detection model to obtain an initial image of a glass membrane wart lesion region; segmenting the initial image by using a pre-trained glass membrane wart lesion segmentation model to obtain a first image of the glass membrane wart lesion; extracting the glass membrane wart lesion region of the target eye image based on a preset image processing algorithm to obtain a second image of the glass membrane wart lesion region; and performing intersection processing on the initial image, the first image of the glass membrane wart lesion and the second image of the glass membrane wart lesion region to obtain a glass membrane wart lesion segmentation result corresponding to the target eye image.

The application discloses an edge inference driven intelligent auxiliary decision system for medical image recognition, relates to the technical field of medical image recognition auxiliary decision, and comprises a multi-modal adaptation module, an edge inference module, a cloud collaborative module, a knowledge distillation module, a resource scheduling module and a decision output module; the multi-modal adaptation module is used for extracting modal features of CT, MRI and X-ray images through a differentiable neural architecture search method to generate a lightweight edge model; the edge inference module is used for performing real-time inference on the medical images by using the lightweight edge model to output preliminary diagnosis results and confidence scores; the cloud collaborative module is used for setting a threshold value; when the confidence score is lower than the threshold value or the lesion area is smaller than a preset value, the medical images are transmitted to the cloud for deep analysis to output accurate diagnosis results and a lesion segmentation mask; and the knowledge distillation module is used for taking the lesion segmentation mask as a spatial constraint.

The application discloses a stroke CT counterfactual consistency identification method, device and medium, and belongs to the field of medical image intelligent analysis. The method comprises the following steps: acquiring a non-enhanced head CT plain scan slice and performing pretreatment; constructing two structure-preserving disturbance views for the same slice; extracting content features and domain features through a shared encoder, performing interactive fusion and double-gate modulation to obtain a stable shared representation, and constructing a consistency loss of a probability layer and a representation layer; decomposing a global representation into a lesion-related component and a mixed component, implementing controllable gate intervention and neutral prototype replacement on the mixed component to obtain a counterfactual representation, and calculating a counterfactual constraint loss; and optimizing network parameters in combination with a supervised cross-entropy loss, a consistency loss and a counterfactual constraint loss. The application improves the generalization reliability under cross-center and cross-device conditions, does not require pixel-level lesion segmentation annotation, and is suitable for rapid engineering landing.

This application discloses a non-invasive method and system for predicting the ALK rearrangement status of lung adenocarcinoma based on a large-scale medical model. The method includes: acquiring chest medical imaging data and corresponding clinical information, wherein the chest medical imaging data includes lesion regions; inputting the chest medical images into a fine-tuned large-scale automatic segmentation model for lung adenocarcinoma lesions to obtain lesion segmentation mask images, wherein the segmentation model is optimized through a fine-tuning strategy using lung adenocarcinoma-specific cue tokens and a low-rank adapter module; based on the lesion segmentation mask images, cropping standardized lesion region images from the chest medical images; inputting the lesion region images and clinical information into a trained large-scale ALK rearrangement status prediction model for lung adenocarcinoma, and outputting the ALK rearrangement status prediction result for the lesion. The technical solution of this application effectively improves the segmentation accuracy of lung adenocarcinoma lesions and the accuracy and efficiency of ALK rearrangement status prediction.

The embodiment of the application discloses a kind of vitiligo auxiliary diagnosis methods, devices and storage medium of multi-modal medical image collaborative segmentation and classification, wherein the method comprises: obtaining the multi-modal image pair of the clinical image and Wood lamp image of the same examinee;According to the imaging characteristics of two kinds of modalities, the image pair is preprocessed and modality-specific data enhancement;The multi-modal image pair after processing is input into the feature extraction network to obtain each modality feature, and spatial guidance information for subsequent segmentation is generated;Each modality feature is input into vector quantization fusion module for cross-modal feature fusion to obtain semantic consistent fusion feature;Based on the fusion feature, a segmentation branch and a classification branch are constructed to realize the joint output of lesion region segmentation and disease activity classification, and the collaborative effect of the two tasks is improved through inter-task interaction. By using the present application, end-to-end joint optimization of vitiligo lesion segmentation and disease activity classification can be realized, and the diagnostic performance is improved.

The present application relates to a kind of training method, equipment and medium for the multi-task prediction model of medical image, the method includes the following steps: obtaining multi-center, multiple devices, multiple tracer initial medical image and clinical risk factor feature, the initial medical image includes PET image and CT image;First equalization processing is carried out to initial medical image corresponding lesion segmentation gold standard, obtain equalized 3D image sequence;Resampling is carried out to the 3D image sequence, extract deep learning feature and pre-defined image group feature;Second equalization processing is carried out to the deep learning feature, and feature post-processing is carried out to the equalized feature;The deep learning feature, pre-defined image group feature and clinical risk factor feature obtained are spliced, and final feature is obtained;With final feature as input, multi-task prediction model is trained and obtained.Compared with prior art, the present application has the advantages of high medical image processing accuracy, suitable for multi-modal image and the like.

This invention relates to the field of agricultural diseases, specifically providing a leaf disease classification method based on YOLOv8 and a multi-task learning model, comprising the following steps: S0, identifying diseased regions in the original image of diseased leaves based on the YOLOv8 model, and outputting a cropped image of the diseased leaf region; S1, constructing a segmentation-assisted classification multi-task learning model; S2, performing pixel-by-pixel segmentation of the detected leaves using the multi-task learning model, and classifying the segmented regions by disease type; S3, designing a custom total loss function combining segmentation loss, classification loss, and consistency loss, and training the multi-task learning model. This technical solution solves the problem of poor classification performance in agricultural plant disease detection, and constructs a highly efficient and accurate disease classification and lesion segmentation system.

The present invention relates to an intelligent medical display system, which comprises a video acquisition module, an AI edge computing module, a marking layer output module, a video superimposition module, and a display module. The video acquisition module receives a video input signal and transmits same to the AI edge computing module; the AI edge computing module analyzes the video input signal to obtain a lesion target detection packet, a lesion segmentation packet, and a semantic scene detection packet, and pushes same to the marking layer output module; the marking layer output module groups and marks the pushed packets, performs marking on background images according to a grouping and marking result to form a lesion target detection result video, a lesion segmentation result video, and a semantic scene detection result video, and outputs same to the video superimposition module; the video superimposition module is used for performing video superimposition; the display module is used for displaying. The present invention can improve image quality and system stability, reduce image processing delay, and improve real-time performance and accuracy in a surgical process.

The present application relates to the medical image intelligent recognition, three-dimensional reconstruction and medical rapid prototyping technical field, solve the defect that ultrasound, nuclear magnetic imaging is not intuitive to deep lesion, there is no 1:1 entity reference before operation, the communication difficulty of doctor and patient, the system contains the shell, high-definition display module, self-locking angle support and four core components: (1) ultrasonic detection assembly (ultrasonic probe acquisition assembly), (2) image processing mainboard (AI image main control processing machine case), (3) high-precision servo drive module, (4) 3D precision printing forming bin.Probe acquisition pelvic ultrasound data, complete lesion segmentation and three-dimensional reconstruction by AI, and print entity model.The present application adopts self-developed AI, clearly shows the relationship between lesion and pelvic floor, rectum, ovarian infiltration, adapts to gynecological difficult lesions, and assists clinical preoperative evaluation.

The present application relates to the field of gallbladder polypoid lesion analysis, in order to solve the technical problems of limited recognition ability of existing technology on ultrasonic image, inaccurate lesion risk assessment, insufficient medical semantic expression and explainability, etc., the present application provides a gallbladder polypoid lesion multimodal prediction system and prediction method based on ultrasonic image, including an image quality control and preprocessing module for processing ultrasonic images to generate preprocessed images; a lesion segmentation and ROI guided module for guiding and constructing preprocessed images to generate ROI guided images; a clinical hint and semantic mapping module for generating clinical semantic features; a multimodal diagnosis classification module for extracting spatial features of the ROI guided images to obtain classification probability results and prediction results of three types of lesions; an explainability output module for visualizing the prediction results to generate visualized image lesion images and clinical semantic contribution maps; and a data display module for real-time receiving, displaying and storing relevant data.

The application provides an eye fundus image lesion segmentation method and device, computer equipment and medium, and belongs to the technical field of medical image processing. The method improves the generalization ability of the model through data preprocessing, uses an eye fundus image lesion segmentation model containing an encoder, a global-local attention module, a multi-scale feature capturing module and a decoder, and realizes accurate segmentation of four kinds of sugar net disease lesions in the eye fundus image. The encoder adopts a three-branch structure to extract multi-scale features, the global-local attention module fuses global and local attention features, the multi-scale feature capturing module extracts multi-scale lesion features through different convolution kernels, and the decoder generates the final segmentation result through channel splicing and up-sampling. The application adopts the above-mentioned eye fundus image lesion segmentation method, device, computer equipment and medium, can simultaneously segment four kinds of sugar net disease lesions in the eye fundus image, has fewer calculation parameters, and has good accuracy and robustness.

The application relates to the technical field of medical image processing, and discloses a multiple myeloma lesion intelligent detection and quantitative evaluation method based on PET-CT, which comprises the following steps: acquiring and preprocessing PET and CT three-dimensional images; PET functional features and CT anatomical features are extracted through a double-branch network, and the two features are deeply fused by using a cross-modal attention mechanism; a cascaded architecture is used for lesion identification; candidate regions are located by a three-dimensional lesion detection module, and then the candidate regions are finely segmented by a three-dimensional lesion segmentation module to generate a three-dimensional mask; the segmented mask is post-processed to remove false positives and complete false negative lesions; finally, quantitative parameters such as overall metabolic tumor volume are automatically calculated based on the final mask. The application realizes full-automatic and objective lesion identification and segmentation, and improves the sensitivity, specificity and quantitative evaluation consistency of detection.

This invention relates to the field of medical artificial intelligence software, and discloses an AI-assisted diagnosis and treatment system for refractive errors based on an internet hospital. The system comprises five modules: a patient information management module, an intelligent diagnostic assistance module, a remote monitoring module, a disease progression risk warning and prognosis prediction module, and an online consultation and eye health education module. The image segmentation model uses wavelet transform operators to decompose and extract multi-scale frequency domain features from the original 3D-OCT volumetric data of the eye. These multi-scale frequency domain features are then merged and concatenated with the original 3D-OCT volumetric data of the eye in the channel dimension to generate a multi-channel feature cube. This multi-channel feature cube is input into the Unet algorithm network unit, and after encoder, decoder, and hierarchical skip connection operations, a multi-channel probability map is generated. Finally, the multi-channel probability map is converted into a binary lesion segmentation mask for output. The intelligent image analysis module of this invention can improve the accuracy and efficiency of image screening for early identification of pathological myopia.

This invention relates to the field of agricultural informatization and intelligent detection technology, specifically to a method and system for identifying crop lesions based on multispectral images. The method includes: acquiring multispectral images of leaves and generating reflectance cubes and leaf masks through radiometric calibration and reflectance normalization; performing constrained spectral unmixing based on an endmember library within the leaf mask region to obtain a lesion abundance map and an unmixing residual map, and generating a corrected reflectance cube by aligning the reflectance cube with optimal transmission; generating a lesion segmentation probability map and leaf vein information based on the corrected reflectance cube, and performing evidence fusion with the lesion abundance map to obtain a lesion confidence map and fusion conflict degree, outputting the lesion mask and lesion coverage interval and interval width; when the fusion conflict degree or interval width exceeds a threshold, acquiring counter-evidence multispectral image data for repeated processing and updating the endmember library, and rolling back when the rollback criterion is met. This invention achieves stable identification and interpretable quantification under complex acquisition conditions.

The application provides a medical image collaborative analysis method and device based on parallel truncated fusion and morphology perception visual prompt, and relates to the technical field of medical image analysis. The method comprises the following steps: performing high-precision lesion segmentation on a medical image by using a parallel truncated fusion network. The network injects the deep semantics of a frozen large language model into a visual backbone in a residual manner through a non-destructive parallel double-flow design, enhances semantic understanding while retaining texture details; generating a morphology perception visual prompt based on the segmentation result, guiding a downstream visual language model to focus on the lesion area through a non-occlusion safety bounding box and a soft thinking chain, and completely retaining the environment features which are crucial for diagnosis. The application effectively improves the medical image segmentation performance and the accuracy of the medical VQA task.

The application discloses an ultrasound image lesion segmentation method based on a CNN-Transformer hybrid network feature transformation, which comprises the following steps: after pre-processing the input ultrasound image, inputting the hybrid network with an encoder-decoder structure; extracting local features and global semantic features through a parallel double-encoder module, and realizing adaptive conversion between the two branches through a feature conversion module; purifying the global semantic features through a feature enhancement and conversion module and converting the global semantic features into adaptive features; performing feature upsampling and fusion through a convolutional neural network decoder, and outputting a segmentation probability map; and obtaining a lesion region mask after binarization. The method can accurately capture lesion features, improve the accuracy and efficiency of ultrasound image lesion segmentation, has good generalization and robustness, and can still maintain high segmentation accuracy on an external verification set and a data set containing normal image interference; the balance between segmentation accuracy and calculation efficiency is realized under a reasonable parameter scale, and the method is suitable for the practical application of clinical ultrasound images.

A semi-supervised medical image segmentation method and system based on wavelet transform and multi-frequency auxiliary networks, belonging to the field of medical image processing technology, includes the following steps: First, an enhanced training sample is generated by mixing labeled and unlabeled images using a bidirectional copy-paste strategy. Then, wavelet transform is used to decompose the mixed sample into low-frequency and high-frequency components to explicitly separate global structural information and local detail features. Next, a multi-frequency auxiliary segmentation network is designed, comprising multiple targeted sub-networks to process different frequency components. The low-frequency sub-network uses a graph attention mechanism to capture semantic associations, while the high-frequency sub-network uses an edge attention mechanism to enhance boundary features. Finally, under the mean teacher framework, consistency constraint training is performed by weighted fusion of the losses of each sub-network, thereby achieving accurate and robust medical image segmentation with limited labeled data. This invention significantly improves the accuracy and boundary integrity of organ and lesion segmentation.

This invention proposes an intelligent segmentation method for reticular fiber stained pathological images and its application. Addressing the limitations of existing technologies in identifying reticular fiber structure disruptions to define tumor boundaries and artifacts caused by sliding windows, this invention employs adaptive sliding window segmentation and multi-stage pre-filtering to extract effective image blocks. The input is a dual-stream network, where structural and visual flows are fused through cross-attention based on tissue physical size mapping receptive fields to achieve collaborative verification of visual abnormalities and structural disruptions. Model training utilizes a dynamic boundary-aware loss term with physical scale constraints for joint optimization. Finally, a spatial weight fusion mechanism combined with a graph model incorporating physical priors eliminates breakage artifacts and smooths global boundaries. This invention is primarily used for high-fidelity lesion segmentation of pituitary neuroendocrine tumors.

The present application relates to the technical field of medical image processing and artificial intelligence, and specifically relates to a method and system for automatic positioning and three-dimensional prompt guided segmentation of nasopharyngeal carcinoma lesions. In view of the problems of excessive human interaction, large background interference and insufficient segmentation stability in existing nasopharyngeal carcinoma lesion segmentation, the present application obtains nasopharyngeal three-dimensional medical image data, extracts lesion spatial features using a positioning model, automatically obtains three-dimensional positioning results of the lesion, generates three-dimensional prompt information located in the lesion area based on the positioning results, guides the segmentation model to perform fine segmentation in the local candidate area, and outputs the three-dimensional segmentation results of the nasopharyngeal carcinoma lesion. Optionally, the error area is identified according to the spatial relationship between the segmentation results and the prompt information, and new three-dimensional prompt information is automatically generated for iterative correction. The present application reduces manual operation, improves the accuracy and spatial consistency of nasopharyngeal carcinoma lesion segmentation, and is suitable for auxiliary diagnosis and treatment planning of nasopharyngeal carcinoma lesions.