77 results about "Lesion analysis" patented technology

The invention relates to the technical field of navigation and positioning, and discloses an arthroscopic surgery navigation and positioning system based on artificial intelligence, which comprises a preoperative image processing and lesion analysis module for analyzing preoperative images, a multi-modal data fusion module for integrating image data from different sources in the surgery, and a navigation and positioning module for performing navigation and positioning on the integrated image data. The intra-operative real-time joint tracking and posture estimation module is used for tracking the joint position and soft tissue deformation in real time, the AI dynamic operation path planning module is used for intelligently optimizing the operation path, the robot auxiliary operation and automatic adjustment module is used for automatically adjusting the position and angle of an operation instrument, and the intra-operative AI voice and visual feedback module is used for providing real-time feedback. The operation data storage and intelligent learning module is used for storing and analyzing operation data. By means of the multi-modal data fusion technology, arthroscopic images, ultrasonic images and infrared images are combined, accurate visualization of an operation area is achieved, and the effect of improving operation accuracy and safety is achieved.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

The invention relates to the technical field of medical detection, in particular to a sacroiliac arthritis judgment method and system based on X-rays, and the method comprises the steps of multi-modal data synchronous collection, image data real-time optimization and control, cross-modal causal reasoning and three-dimensional reconstruction, and AI-based lesion preliminary analysis and dynamic grading and interpretability report generation. According to the method, a real-time image processing technology is integrated in a data acquisition stage, an original image is directly and dynamically optimized at an imaging end, and the image resolution and the detail definition are improved through an advanced enhancement algorithm; the problems of delay and detail loss caused by relying on post-processing in a traditional method are solved, so that a doctor can obtain a high-quality diagnosis image in real time; by means of the optimization, the requirement for repeated shooting is reduced, and the availability and diagnosis efficiency of images are remarkably improved.

The invention provides an image lesion attribute prediction model training method, a prediction method and a related device, and the method comprises the steps: carrying out the visual coding of a fundus image sample through a visual encoder, and obtaining a pseudo-image visual feature; performing text coding on each lesion attribute sub-category description text to obtain lesion attribute sub-category features; performing matching according to the visual features of the pseudo image and the lesion attribute sub-category features to obtain a pseudo matching probability; performing visual coding on the fundus image sample through the initial image lesion attribute prediction model to obtain initial image visual features; performing matching according to the initial image visual features and the lesion attribute sub-category features to obtain a predicted matching probability; carrying out loss calculation according to the pseudo matching probability and the predicted matching probability to obtain a target matching loss function; and updating the initial model based on the target matching loss function to obtain an image lesion attribute prediction model. In conclusion, the lesion analysis accuracy of the fundus image can be improved.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

The present disclosure relates to a dental lesion detection method and a system to which the method is applied. A dental lesion information visualization method according to the present disclosure includes the steps of acquiring data of a lesion analysis model which outputs data on the type and location of a lesion included in a panoramic image obtained by capturing an image of the oral cavity of a patient, and inputting a panoramic image into the lesion analysis model to output data on the type and location of a lesion and a lesion detection confidence score.

The invention discloses a full-self-service fundus photo analysis method and device, a storage medium and a system, and relates to the technical field of image recognition. The method comprises the following steps: loading an original fundus image for preprocessing; extracting a candidate lesion area of the image, and performing morphological operation, threshold segmentation and mean clustering; constructing a U-net model, and training a network model by using the fundus image training set to obtain a fundus focus recognition model; segmenting the test set by the trained fundus focus recognition model to obtain a fundus image segmentation result; a TensorRT or ONNX format is used to realize GPU / CPU cross-platform deployment, and two-stage output is provided: a coarse segmentation result is provided for a doctor to quickly screen, and a refined result is used for diagnosis. The original fundus image is preprocessed to train the focus analysis model, disease risk prediction is performed according to the model analysis result, auxiliary diagnosis and decision support are provided for doctors, and the accuracy and efficiency of disease analysis are improved.

The invention relates to a diabetic retinopathy fundus photography grading report system combined with a clinical guide, based on an artificial intelligence deep learning technology, and belongs to the field of fundus lesion analysis. The system accurately identifies and segments various lesions such as microhemangioma, bleeding, exudation and the like and symbolic structures such as optic discs, macular regions and the like by automatically analyzing fundus photographic images. The system adopts ICDR international standards to grade diabetic retinopathy, and provides diagnosis and treatment suggestions for lesion characteristics in combination with clinical guidelines of American ophthalmology institute in 2019. Through big data training, the system can generate detailed reports in real time, the early screening rate is remarkably improved, misdiagnosis and missed diagnosis are reduced, and the diagnosis speed and accuracy are improved. The system comprises a plurality of modules, such as an image pre-classification module, a deep learning focus recognition module, an ICDR grading module and a report generation module, efficient and accurate diabetic retinopathy diagnosis and grading are cooperatively achieved, and the clinical management level is improved.

The present invention discloses a deep learning-based method for automatic segmentation of intravascular lipid plaques. The method utilizes an improved U-Net++ network architecture to achieve high-precision segmentation of the inner and outer membranes of blood vessels. Based on this, a K-means clustering algorithm based on grayscale values, edge features, and texture features is then employed. By incorporating a memory mechanism, stability and consistency are ensured during the clustering process, enabling accurate classification of plaque types. By using an improved U-Net++ network for inner and outer membrane segmentation in ultrasound and photoacoustic images, the present invention can accurately distinguish between different structures of the vascular wall, thereby achieving accurate identification, high-precision segmentation, and classification of lipid plaques. The K-means clustering algorithm, optimized based on feature selection and a memory mechanism, achieves relatively accurate and stable classification results in virtual histology, providing a reliable foundation for further lesion analysis and clinical application.

The invention provides a CT image lesion change analysis method and system based on a time sequence, and relates to the technical field of lesion analysis. The method comprises the following steps: acquiring a CT image of a current examination of a patient, and acquiring a plurality of historical CT images of the patient; obtaining a lesion area where a lesion is located in the CT image through a lesion detection model; obtaining a historical lesion area where the lesion is located in the historical CT image through a lesion detection model; determining a focus state coefficient of the focus area and a historical focus state coefficient of the historical focus area; determining the development condition of the focus; and generating a focus change analysis report. According to the application, when the change condition of the lesion is determined to be analyzed, the lesion area in the CT image and the historical lesion area in the historical CT image are analyzed, and the lesion state coefficient and the historical lesion state coefficient are determined, so that the development condition of the lesion is determined. The accuracy of lesion development condition judgment is improved, and the lesion change analysis efficiency is improved.

The invention provides a throat reflux disease detection method and system based on a laryngoscope image and an RFS-Net algorithm, and relates to the technical field of medical image intelligent detection.The method comprises the following steps that a laryngoscope image sample is obtained and preprocessed, and a standard image is obtained; inputting the standard image into the RFS-Net model, and extracting spatial features and spectrum features; carrying out model training based on an improved Focal Loss function in combination with the extracted spatial features and spectrum features, and balancing sample distribution; fusing the shallow texture features and the deep semantic features of the image samples through a residual attention mechanism RAM; performing lesion analysis on the patient data in combination with clinical indexes and fusion features, and outputting a detection result; according to the method, multi-dimensional feature extraction and fusion are performed on the laryngoscope image through the RFS-Net model, spatial features and frequency spectrum features are covered, the features of the throat reflux disease can be identified more accurately in combination with the improved Focal Loss function optimization model training, and misdiagnosis and missed diagnosis conditions are reduced.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

This invention relates to the field of medical information management technology, including an AI-based endometriosis management system. The system comprises a thickness dynamic monitoring module, an inflammation level assessment module, a lesion spread analysis module, a periodic lesion tracking module, and a disease progression analysis module. In this invention, AI is used to segment ultrasound image data, enabling dynamic monitoring of lesion areas. Lesion boundaries are extracted, and changes in local inflammatory marker concentrations are calculated, making the screening of abnormal inflammatory areas more accurate and enabling early identification of lesion development. The tracking of periodic lesions, combined with analysis of lesion contour changes and area increases / decreases, enhances the quantitative control of disease progression. The future development trend of lesions, combined with the calculation of periodic inflammatory pathways, makes the prediction of disease evolution more consistent with physiological changes. Multi-dimensional lesion analysis combined with AI intelligent calculation expands the diagnosis and treatment of endometriosis from static assessment to dynamic trend prediction, improving the adaptability of personalized treatment plans.

The application provides a blood vessel lesion determination method and device, electronic equipment and storage medium. The determination method comprises: inputting acquired no-label images and labeled images as input images into a blood vessel lesion analysis model in a single-alternating input manner; if the input image is a no-label image, training the blood vessel lesion analysis model according to a reconstruction loss function; if the input image is a labeled image, determining whether the reconstruction loss function, a lesion type loss function and a lesion degree loss function simultaneously satisfy a target condition, and if the target condition is satisfied, obtaining a trained blood vessel lesion analysis model; and determining a lesion type result and a lesion degree result of a blood vessel image according to the trained blood vessel lesion analysis model. The technical solution provided by the application can reduce manual labeling operations, reduce the workload of doctors, and ensure the accuracy of blood vessel lesion determination.

The application discloses an old person free breathing coronary artery scanning image lesion analysis system and relates to the technical field of scanning image lesion analysis. In order to solve the problem that the accurate condition of a patient cannot be obtained according to a scanning image. The application adopts a diameter method, an area method and a contrast agent filling condition to judge the stenosis degree and the hemodynamic change, analyzes the lesion from the morphological and functional double angles, provides comprehensive information for clinical decision-making, identifies the R wave peak value and divides the cardiac phase, matches the respiratory signal and the projection data in time, can accurately capture the characteristics of the heart in different motion states and the respiratory stage, effectively avoids the interference of the artifacts caused by the heart beat and the respiratory motion, makes the reconstructed image clearer and more accurate, sets the CT and the injector parameters in sequence from the scanning type confirmation, each link is closely related and the target is clear, improves the work efficiency, and is convenient for quality control and process management.

The present invention relates to the technical field of image processing, and specifically discloses a lesion analysis method and device based on fundus images, including: obtaining a first fundus image set and a second fundus image set; performing image stitching on the first fundus image set and the second fundus image set to obtain a first stitched fundus image and a second stitched fundus image; performing intersection analysis on the second stitched fundus image based on the first stitched fundus image, and processing the first stitched fundus image and the second stitched fundus image based on the intersection analysis result to obtain a first intersection image and a second intersection image; cropping the first intersection image and the second intersection image to obtain a first cropped image set and a second cropped image set; performing lesion analysis on the first cropped image set and the second cropped image set to generate first analysis data and second analysis data; generating a lesion analysis result based on the first analysis data and the second analysis data. The present invention improves the accuracy and reliability of lesion recognition.

An artificial intelligence-based lesion analysis device and method utilizing the characteristics of blood vessels surrounding a lesion are disclosed. An artificial intelligence-based lesion analysis method utilizing the characteristics of blood vessels surrounding a lesion according to one embodiment of the present invention may include: (a) acquiring a medical image of a subject; (b) inputting the medical image into a pre-established segmentation model to segment a lesion region included in the medical image and a blood vessel region surrounding the lesion region; (c) extracting blood vessel feature information from the segmented blood vessel region; and (d) inputting the blood vessel feature information into a pre-established analysis model to generate analysis information associated with a target lesion of the subject.

This invention discloses a kidney pathology image processing method integrating multi-tissue segmentation and quantitative lesion analysis, belonging to the field of medical image processing technology. The method includes the following steps: acquiring and preprocessing PAS-stained whole-slice images of kidney pathology; fine-tuning the segmentation model using an unsupervised domain adaptive strategy to address batch-to-batch staining differences; inputting the preprocessed image into a multi-class semantic segmentation neural network to obtain tissue segmentation results; training the network based on pixel-level annotations, employing a Class-Token mechanism, encoder-decoder architecture, and multi-scale feature fusion, and optimizing the Dice loss and binary cross-entropy loss based on joint weighting of categories and boundaries; performing statistical analysis based on the segmentation results and outputting quantitative analysis results. This invention provides an objective, reproducible, and intelligent auxiliary tool for the accurate assessment and large-scale clinical research of chronic kidney disease.

Disclosed is a method for analyzing a lesion based on a medical image performed by a computing device. The method may includes generating, by using a pre-processing module, an input image of a pre-trained detection module from the medical image. The method may include generating, by using the detection module, a probability value regarding a presence of a nodule in at least one region of interest and first location information about the at least one region of interest, based on the input image. The method may include determining, by using a post-processing module, second location information about a suspicious nodule present in the medical image from the first location information, based on the probability value regarding the presence of the nodule.

The invention relates to the technical field of image registration analysis, and particularly discloses an osteomyelitis lesion analysis method based on medical image registration, which ensures the precision of registration information by providing a precise target area and ensures the accuracy of a lesion image evaluation result extracted after registration. The method comprises the following steps: step 1, carrying out a multimode registration simulation experiment on a reference image and a floating image based on a mutual information technology to obtain a registration target area; the reference image is a CT image, and the floating image is a CBCT image; step 2, carrying out test analysis after carrying out spatial transformation on the registration target area, and judging whether the actual mutual information value between the two images meets the requirement or not according to a test analysis result: if so, obtaining the information of the registration target area and extracting the feature of a lesion area; if not, returning to the step 1 to adjust the target area range of multimode registration; 3, performing statistical analysis on lesion area features to obtain lesion area images; and 4, analyzing the lesion area image and outputting an evaluation result.

The invention relates to the technical field of artificial intelligence, in particular to a concept activation vector-based medical image lesion analysis and clinical decision interpretation system, which comprises a feature analysis module, a semantic mapping construction module, a concept activation extraction module, a boundary response fusion module and an interpretation path generation module. According to the method, the gradient direction and the gray abrupt change position of continuous elements in a focus area are detected, texture density and edge consistency are jointly judged to generate structured alignment mapping, microscopic image features are accurately captured, cross comparison is carried out on co-occurrence probability of lesion nouns and modifiers in phrase combinations and activation frequency of the same area, and therefore the accuracy of the lesion nouns and the modifiers in the phrase combinations is improved. Constructing a deep correlation map of pathological semantics and image features, screening an activation group according to continuous response of phrase pairs and image regions under case input change, generating a concept vector trigger track to dynamically track pathological feature evolution, and performing clinical consistency judgment; and an interpretable decision basis with strict logic support is output while subjective interference is eliminated.

The present application relates to a kind of multi-center medical image lesion typing methods based on federal spectrum clustering, belong to medical image analysis technical field, including the following steps: S1: multiple medical institutions are regarded as federal learning client, and the medical image data and supporting clinical data of each client local are obtained and stored in each client local;S2: feature extraction and multimodal feature fusion are carried out in each client local, and multimodal lesion feature set is obtained;S3: center server and each client pass through iteration communication, and the multimodal lesion feature set is jointly clustered using federal spectrum clustering algorithm, until global clustering center converges;Only transmission encrypted statistical parameters in communication process;S4: according to clustering result, output lesion typing information.The present application solves the pain points of difficult data sharing and high privacy leakage risk in traditional centralized lesion analysis, and improves the lesion typing accuracy in heterogeneous data scenario.

The invention provides a Mama-based membranous nephropathy multi-mode pathological image quantitative analysis system and method, and belongs to the crossing field of biomedical engineering and artificial intelligence. The problems that in an existing membranous nephropathy diagnosis system, the diagnosis process is high in subjectivity, single-mode analysis is limited, lesion feature quantification is insufficient, and model calculation is complex are solved. According to the technical scheme, the system comprises an image preprocessing module, the image preprocessing module is in communication connection with a macroscopic lesion analysis module, a microstructure analysis module and a thickness quantification module, and the macroscopic lesion analysis module and the thickness quantification module are both in communication connection with a feature fusion and prediction module. The macroscopic lesion analysis module, the microstructure analysis module, the thickness quantification module and the feature fusion and prediction module are all in communication connection with the result visualization module, and multi-modal pathological image quantitative analysis of membranous nephropathy is realized; the method is applied to multi-mode pathological image analysis of membranous nephropathy.

The invention relates to the technical field of machine learning, and discloses an oral cavity lesion analysis model training method and device, equipment and a medium, and the method comprises the steps: obtaining an oral cavity diagnosis and treatment text and an oral cavity lesion image of a user set, carrying out the text feature coding of the oral cavity diagnosis and treatment text, and obtaining a clinical text feature; performing multi-layer residual error convolution on the oral cavity lesion image to obtain image convolution features; performing channel attention and space attention calculation on the image convolution features to obtain target image features; performing feature fusion on the clinical text features and the target image features to obtain a feature set; calculating a multi-task loss value according to the feature set; and performing parameter optimization on the oral lesion analysis model by using the multi-task loss value to obtain a target oral lesion analysis model for identifying an oral mucosa lesion area in the oral diagnosis and treatment image. Through the implementation of the invention, the complementation of the characteristic modes can be realized, the loss function of a plurality of related tasks is optimized, and the prediction precision of the oral cavity lesion analysis model is improved.

Embodiments of the present disclosure provide systems and methods for analyzing medical images containing vascular structures using a sequential model. An example system includes a communication interface configured to receive the medical image and the sequential model. The sequential model includes a vessel extraction sub-model and a lesion analysis sub-model. The vessel extraction sub-model and the lesion analysis sub-model are trained independently or jointly. The example system further includes at least one processor configured to apply the vessel extraction sub-model to the received medical image to extract location information of the vascular structure. The at least one processor also applies the lesion analysis sub-model to the received medical image and the location information extracted by the vessel extraction sub-model to obtain a lesion analysis result of the vascular structure. The at least one processor further outputs the lesion analysis result of the vascular structure.

The embodiments of the present invention relate to the technical field of pathological image processing, and specifically disclose a pathological image processing method and system based on lesion target area selection. The embodiments of the present invention obtain a target pathological image and perform image preprocessing; perform fuzzy feature analysis to extract a suspicious lesion image; perform fine segmentation processing to extract a target lesion image; perform lesion classification, grading and quantitative analysis to obtain a lesion analysis report; based on the lesion analysis report, create a target lesion image and a background lesion image, synthesize and display a multi-dimensional lesion image. The system can perform image preprocessing, fuzzy feature analysis, fine segmentation processing, lesion classification, grading and quantitative analysis on the target pathological image, create a target lesion image and a background lesion image, synthesize and visualize a multi-dimensional lesion image, achieve standardized processing of pathological images, improve the integrity of pathological image processing, and the processing process is fully automatic, reducing annotation costs and promoting popularization and use.

The present application belongs to the technical field of image processing, and discloses an intelligent auxiliary processing system for abdominal cavity operation images, which comprises a data acquisition module, a data processing module, an intraoperative lesion diagnosis module, an intraoperative lesion analysis module, a clinical decision module and a scheme regulation module. The data acquisition module is used for acquiring laparoscope 3D optical molecular image data, RAL imaging data and device operation data. The data processing module is used for obtaining a comprehensive feature data set by processing the acquired laparoscope 3D optical molecular image data, RAL imaging data and device operation data. The intraoperative lesion diagnosis module is used for constructing an intraoperative lesion diagnosis model and predicting an optical feature index. The intraoperative lesion analysis module is used for comparing the predicted optical feature index with a preset optical feature index threshold value and judging whether there is a lesion. The clinical decision module is used for constructing a lesion degree prediction model and predicting a lesion severity. The scheme regulation module is used for implementing a corresponding tumor treatment scheme according to the lesion severity.

The invention relates to the technical field of image processing, and particularly discloses a focus analysis method and device based on fundus images, and the method comprises the steps: obtaining a first fundus image set and a second fundus image set; performing image stitching on the first fundus image set and the second fundus image set to obtain a first stitched fundus image and a second stitched fundus image; performing intersection analysis on the second spliced eye fundus image based on the first spliced eye fundus image, and processing the first spliced eye fundus image and the second spliced eye fundus image based on an intersection analysis result to obtain a first intersection image and a second intersection image; cutting the first intersection image and the second intersection image to obtain a first cut image set and a second cut image set; performing focus analysis on the first cutting image set and the second cutting image set to generate first analysis data and second analysis data; and generating a lesion analysis result based on the first analysis data and the second analysis data. The accuracy and reliability of focus recognition are improved.