100 results about "Lesion analysis" patented technology

The invention relates to the technical field of image analysis, in particular to an alimentary canal lesion analysis method and system based on image recognition, based on an endoscope image of an alimentary canal of a patient, the edge intensity of pixel points in the image is evaluated through image analysis, and a suspected lesion area in the image is recognized by adjusting an edge detection threshold value; the non-suspected lesion area and the suspected lesion area are distinguished, and a digestive tract area distinguishing result is obtained. According to the method, the identification process of the suspected lesion area and the non-suspected lesion area is optimized by dynamically adjusting the edge detection threshold value, the shape, size and color of the suspected lesion area are evaluated, and the suspected lesion area is matched with the known lesion type in the database, so that the lesion type identification accuracy is improved; and the image data corresponding to the lesion type is extracted, data support is provided for subsequent medical personnel to confirm the lesion type, and the evaluation of the healthy tissue is refined by analyzing the deviation between the non-suspected lesion area and the normal color data.

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.

According to the ultra-wide-angle fundus image analysis method and system, an ultra-wide-angle fundus image is divided into a macular region, a posterior pole region and a peripheral region, the focus of each region is automatically recognized, and then auxiliary diagnosis of various fundus diseases of the whole image is achieved. According to the method and the system, efficient, accurate and automatic fundus disease analysis and evaluation are realized through region partitioning and focus analysis of the ultra-wide-angle fundus image, particularly, the method and the system have remarkable advantages in retina peripheral region lesion detection, and the system can be applied to screening and auxiliary diagnosis of clinical ophthalmic diseases on the basis of a deep learning algorithm model. Along with the rapid development of deep learning, a convolutional neural network (CNN) obtains a significant result in the field of image classification, and the method carries out the partitioning of an ultra-wide-angle fundus image based on a deep learning model, and carries out the analysis and evaluation of common fundus diseases and lesions of the whole image, and optic nerve diseases including the lesions of glaucoma. The invention provides an automatic and accurate fundus image auxiliary diagnosis method.

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.

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 discloses a focus auxiliary analysis method, device and equipment and a computer readable storage medium, and the method comprises the steps: obtaining a plurality of stages of medical images collected in a target examination period; focus detection is carried out on the multi-stage medical images, and focus information of a target focus in a target examination period is determined; determining analysis information of the target focus according to the focus information of the target focus; determining a target medical image according to the analysis information and the multiple medical images in the target examination period; acquiring a reference medical image corresponding to the target medical image in a reference examination period; and outputting lesion analysis information of the target lesion according to the analysis information corresponding to the multiple stages of target medical images and the analysis information corresponding to the corresponding reference medical images. The method can effectively improve the lesion auxiliary analysis efficiency.

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 relates to the technical field of electronic medical record management, in particular to a grading and classifying auxiliary management system for critical case specialized electronic medical record information. Firstly, an electronic medical record of a severe pneumonia patient is acquired through a data acquisition module; further acquiring a vertebra region and a corrected rib region in the target image through a vertebra and rib positioning module; further obtaining a pneumonia lesion index of the target image through a lesion analysis module; finally, in a medical record classification module, the electronic medical records are preliminarily divided into a serious class or a gentle class according to the character information; and classifying the electronic medical records according to the time sequence change characteristics of the pneumonia lesion index of the chest X-ray film of the patient in combination with the distribution characteristics of the sheet density increasing shadow area.

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.

The invention provides a digestive tract lesion analysis method and system based on gastroscopy results and a medium. The method comprises the following steps: acquiring esophageal examination characteristic parameters, stomach examination characteristic parameters and duodenum examination characteristic parameters, calculating to obtain an upper digestive tract lesion risk index and a digestive tract comprehensive lesion risk index, and comparing the digestive tract comprehensive lesion risk index with a preset digestive tract comprehensive lesion risk threshold to obtain a digestive tract lesion level, obtaining a corresponding treatment scheme according to the lesion level of the digestive tract; therefore, by combining calculation of the upper digestive tract lesion risk index and the digestive tract comprehensive lesion risk index with threshold comparison, digestive tract lesion analysis is achieved, and a corresponding processing scheme is obtained.

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 invention relates to the technical field of medical image processing, in particular to a medical image acquisition and analysis method and system for an endocrine patient, and the method comprises the following steps: uniformly irradiating the thyroid tissue of the endocrine patient with laser with a target wavelength, detecting and recording the intensity of light reflected back from the tissue surface, and performing initialization mapping on the internal structure of the tissue to obtain an original medical image of the speckles. According to the method, the light intensity change in the speckle image is analyzed, pixel-level calculation is carried out, and tiny change in the tissue is identified in a detailed mode, so that the information amount of the medical image is increased, high-precision basic data is provided for subsequent lesion analysis, and the accuracy of the medical image is improved through the light intensity distribution data. The brightness change between pixels is compared to mark different areas, pathological features are distinguished through texture analysis, the disease early detection and classification ability is improved, and doctors can make a more accurate recovery scheme for patients.

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.