391 results about "Lesion detection" patented technology

The invention discloses a brain image analysis method and system based on multi-modal fusion, and relates to the technical field of brain image processing. A brain image analysis system based on multi-modal fusion comprises a data acquisition module, a data preprocessing module, a feature extraction and fusion module, a brain network analysis module, a comprehensive analysis module and a focus detection module. The comprehensive analysis model adopts a double-branch structure, deep processing is performed on multi-modal features and brain network features, and interactive fusion of the two types of features is realized through a cross-modal attention mechanism; the model is further combined with a classification branch and a regression branch to cooperatively complete brain disease classification and focus quantitative analysis, and the adaptive capacity and diagnosis performance of an existing model in a complex task scene are improved.

The invention provides a farmland disease and pest monitoring method and device and a storage medium, and relates to the technical field of agriculture, the method comprises the steps: dividing a farmland into a plurality of sub-regions, obtaining an infrared spectrum image, and segmenting the infrared spectrum image into corresponding sub-images; and calculating a vegetation stress index based on the reflectivity of the insect pest related characteristic wave band, and screening high-risk sub-regions. Risk plants are randomly extracted in a high-risk area, physiological parameters of leaves are collected, physiological health indexes of the leaves are constructed, and dynamic correction is carried out through an environment regulation coefficient according to environment data. Using the trained network model to identify the risk sample leaf scab, and calculating the scab area index. And finally, fusing the corrected leaf physiological health index and the scab area index, calculating a normalized risk value through a risk assessment function, and dividing the normalized risk value into four risk levels. Through multi-stage fusion of spectral analysis, physiological parameter correction and deep learning scab detection, accurate early warning of diseases and insect pests is realized, and the monitoring efficiency is improved.

Presented herein are systems and methods that provide for improved detection and characterization of lesions within a subject via automated analysis of nuclear medicine images, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) images. In particular, in certain embodiments, the approaches described herein leverage artificial intelligence (AI) to detect regions of 3D nuclear medicine images corresponding to hotspots that represent potential cancerous lesions in the subject. The machine learning modules may be used not only to detect presence and locations of such regions within an image, but also to segment the region corresponding to the lesion and / or classify such hotspots based on the likelihood that they are indicative of a true, underlying cancerous lesion. This AI-based lesion detection, segmentation, and classification can provide a basis for further characterization of lesions, overall tumor burden, and estimation of disease severity and risk.

The invention discloses a pulmonary tuberculosis micro focus detection method and system based on multi-scale convolution. The method comprises the following steps: acquiring lung CT image data; the method comprises the following steps: performing feature extraction through a convolutional layer of a neural network detection model of a pulmonary nodule focus, inputting an extracted feature map into a Faster and efficient multi-scale attention fusion module, and then inputting outputs of different levels in a feature pyramid in a backbone network into a corresponding ADown module, a Conv module and an UpSample module in a multi-scale convolution module; and multiple pieces of feature information are obtained through multi-scale convolution, a prediction frame is finally generated, and a detection result is obtained. A YOLOv10 model framework is improved, the feature extraction performance is improved by using a C2f-Faster-EMA module, a multi-scale convolution module is added into a feature fusion network to process features of different scales, cross-multi-scale context feature information is captured, multiple pieces of feature information are obtained, and the information interaction capability is improved.

The invention discloses a laryngoscope imaging analysis method and system based on deep learning. The method comprises the steps of data acquisition, data preprocessing, lesion static detection, vocal cord dynamic analysis and laryngoscope imaging analysis. The invention relates to the technical field of laryngoscope analysis, in particular to a laryngoscope imaging analysis method and system based on deep learning, according to the scheme, two mutually supporting functional designs of lesion static detection and vocal cord dynamic analysis are adopted, detection of an intuitive lesion area is carried out through a static image, and assistance and support are carried out through vocal cord dynamic analysis; detecting by adopting an improved convolutional neural network method combined with feature optimization; the vocal cord dynamic analysis is carried out by adopting a residual error improved bidirectional long-short-term memory neural network method combined with bidirectional improved time sequence convolution, a powerful auxiliary reference is provided for static image lesion detection, meanwhile, the method can also be used as an independent detection function, and the overall connectivity and reusability of the method are improved.

The invention provides a CT image focus detection method and system based on artificial intelligence, and relates to the technical field of medical image processing, and the method comprises the steps: obtaining a plain-scan CT image and an enhanced CT image of a liver; determining first position information of a to-be-determined lesion area in the plain-scan CT image through an image detection model; according to the first position information and the arterial phase image, screening out a to-be-determined tumor focus area from the to-be-determined focus area, and determining second position information of the to-be-determined tumor focus area; according to the second position information and the portal pulse period image, the to-be-determined tumor focus area is classified, and the tumor focus area and a common focus area are determined; determining a warning level score according to the tumor focus area and the delay period image; and generating a liver focus detection report according to the warning level score. According to the invention, a complex screening and evaluation process can be automatically executed, and the probability of misjudgment is reduced.

The invention provides a disease monitoring method and system based on medical image data recognition, and relates to the technical field of image processing. The method comprises the following steps: acquiring multi-level prior information related to a target disease from a medical knowledge graph, encoding focus attributes and time sequence attributes in the multi-level prior information into knowledge prior vectors, inputting the knowledge prior vectors into a generator, and enabling the generator to output focus image data based on the knowledge prior vectors; when the lesion image data output by the generator and the knowledge prior vector are subjected to difference evaluation, the discriminator also calls lesion boundaries and staging limitation rules in the medical knowledge graph to perform compliance judgment on the form and staging state of the lesion image data; updating network parameters of the generator based on a difference evaluation and compliance determination result output by the discriminator to obtain target lesion image data; and combining the focus image data with the original medical image, inputting the combined image data into a preset disease monitoring model, and generating a potential focus detection result.

The invention discloses a cervical cytopathy detection method based on a hypergraph convolutional network. The method comprises the following steps: performing sliding window slicing processing, unsupervised image decomposition and dyeing normalization on a cervical cytopathy image, generating normalized image input, and constructing an image input sample set; a target detection model is constructed based on YOLO11, a block-level feature extraction network (BBMM) module is embedded to enhance the perception ability, and a sparse attention module is adopted to perform key region feature enhancement; constructing a hypergraph neural network HGNN module based on a Patch-level relationship, and extracting a structural relationship between cells; integrating an uncertainty quantification mechanism, and generating a confidence thermodynamic diagram; a front-end and rear-end separated diagnosis platform is built, image uploading, detection result display, frame selection correction and interactive management are supported, and whole-process auxiliary diagnosis is achieved; the method has the advantages of high detection accuracy, high interpretability, flexible deployment and the like, and is suitable for intelligent early screening and clinical auxiliary diagnosis scenes of cervical cytopathy.

The invention relates to the technical field of medical image processing, in particular to a cross-modal image fusion detection system for endoscopic early cancer lesions, which comprises a data acquisition module for acquiring a white light endoscope and a narrow-band imaging image; the data annotation module carries out pixel-level annotation, and the data enhancement module amplifies a data set by using CycleGAN; the white light data module and the narrowband data module train feature extraction models respectively; the feature alignment and attention module solves the problem that feature scales and semantics between two modes are inconsistent, and efficient fusion is achieved. The edge enhancement module enhances feature extraction of small focuses through the generative adversarial network; the fusion model construction module fuses the features and the transition area data to generate a fusion model; and the output module processes the white light and the narrow-band imaging image and outputs a fused image. Through feature alignment, an attention mechanism and a CyclGAN data enhancement technology, the problem of insufficient medical image data is effectively solved, the model generalization ability is enhanced, and efficient and accurate early cancer focus detection is realized.

The invention provides an index construction method for a medical ultrasonic database, and relates to the technical field of ultrasonic data processing, and the method comprises the steps: reading a multi-frame image sequence from a DICOM ultrasonic database, and generating a diagnosis intention vector for each frame; analyzing the diagnosis report, and segmenting the report into a plurality of segments; lesion detection and segmentation are performed on each frame of image to obtain lesion information, and a cross-frame aggregation strategy is adopted to identify a unified lesion object; extracting a feature vector of each focus object to obtain a focus object feature vector; establishing an alignment mapping between the focus object and the report fragment by adopting triple constraints, and generating a focus report mapping table; and constructing a multi-layer index structure, and storing the focus report mapping table and the multi-layer index structure in parallel. According to the method, a multi-layer index structure comprising a metadata index, a text index, a focus object vector index and a cross-modal embedding index is formed, so that efficient organization and focus-level accurate retrieval of medical ultrasonic data are realized.

The invention relates to the technical field of image focus detection, in particular to a fiber endoscope image focus detection method and system, and the method comprises the following steps: providing a data quality guide interface; capturing narrow-band light images of the endoscope away from a first preset position and a second preset position of the intestinal wall in the axial movement process, calculating brightness gain values of a blue light channel and a green light channel, calculating an asymmetric scattering correction coefficient, and updating indication of color information calibration integrity; tracking the pixel moving speed of the interferent in the view in the propulsion operation process, calculating the relative depth of the interferent, and updating the indication of the validity of the depth data of the interference layer; identifying the image area which is not shielded by the interferent, and updating the indication of the information coverage degree of the background area; triggering an image synthesis operation; performing color compensation on the image information of the image area which is stored in the background canvas and is not shielded by the interferent; reconstructing a clear image; and performing focus detection on the clear image. The method improves the accuracy of focus detection.

The invention relates to a liver tumor focal lesion prediction method based on iterative learning, and belongs to the field of computer models, and the method comprises the steps: firstly, carrying out the manual marking of a lesion region in part of medical image data, obtaining ROI training data, and constructing an automatic lesion detection model based on YOLOv8 through the data; in the ViT classification model, the cut image is divided into image blocks with a fixed size, and the image blocks are embedded into a high-dimensional space. Then, location information is added to each image block using location coding to preserve their spatial relationship. And inputting the embedded sequence into a Transform module, carrying out classification processing through a full connection layer, and finally outputting a predicted category. According to the method, a dynamic learning strategy is introduced, sample weights in a loss function are dynamically adjusted in different training stages, accumulative errors of the model can be reduced, and uncertain samples can be effectively processed at the same time.

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 provides methods and systems for lesion region identification. The methods may include identifying a target region corresponding to at least one reference organ from a first medical image of a target subject. The methods may include determining, based on the target region, a reference threshold used for lesion detection. The methods may further include identifying, based on the reference threshold, a lesion region from the first medical image.

The invention discloses a breast lesion matching method and system based on an ultrasonic image, and relates to the technical field of breast lesion matching, and the method specifically comprises the following steps: obtaining echo interference analysis information from extracted lesion detection information, and carrying out the analysis after obtaining, judging whether the boundary and the form of the breast lesion can cause inaccurate recognition due to echo interference or not; according to the judgment result, an echo suppression mechanism used for suppressing the influence of echo interference on focus recognition is constructed, and the constructed echo suppression mechanism is applied to an image processing flow; in the application process of the echo suppression mechanism, echo suppression feedback information is obtained in real time, analysis is carried out after obtaining, whether the suppression effect of the echo suppression mechanism can meet the requirement for distinguishing the breast focus and the echo interference area or not is evaluated, and the echo suppression mechanism is adjusted according to the evaluation result. The problem that the mammary gland lesion and the vascular echo interference are difficult to distinguish is solved, and the lesion recognition accuracy is improved.

The invention belongs to the technical field of medical image analysis, and relates to a lesion detection method and system based on multi-scan interactive deformable Mama, and the method comprises the steps: 1, image block embedding; 2, multi-scale feature extraction and interaction in the backbone network are scanned in parallel; 3, feature pyramid optimization based on dynamic weighted scanning fusion; 4, performing multi-scale fusion; according to the invention, through the adaptive scanning network and the deformable scanning mechanism, the characterization problem of the morphological heterogeneity of the oral cancer focus and the pulmonary nodule microstructure feature is effectively solved, the double breakthrough of the detection precision and the calculation efficiency is realized, and an efficient and reliable solution is provided for multi-cancer medical image analysis.

The invention discloses a multi-scale adaptive lesion detection method based on mammary gland ultrasound. The method comprises the following steps: pre-processing an image; the input module is used for extracting features through convolution blocks to clearly display boundaries, and then deconvolution blocks are used for refining boundary features to complete image expression; a trunk module; in the neck network, the CA generates feature maps in two directions, the feature maps in the two directions are spliced, features are extracted through convolution operation, and attention weights in the two directions are further generated; and the detection head network performs positioning prediction, classification prediction and loss calculation, and finally outputs a result. According to the method, the precision and richness of feature extraction are improved, and the clear recognition capability of the model on different tissue boundaries is enhanced; the capacity of capturing multi-scale lesion features is improved, and the lesion features are better captured; important areas such as small calcification points, changes of cyst walls and boundaries of fibroadenoma can be highlighted, and the accuracy and specificity of detection are improved.

The invention discloses a vascular access lesion recognition method and system based on target detection, and relates to the technical field of medical image processing, and the method comprises the steps: carrying out the cardiac cycle phase division according to an electrocardiogram signal, calculating a displacement vector field between adjacent frames of a blood vessel DSA image sequence, constructing a motion compensation model, and generating a stable DSA sequence; segmenting the stable DSA sequence into binary vascular mask images through a U-Net segmentation model, tracking motion displacement of contrast agent particles in continuous frames by using a particle image velocimetry algorithm, generating a blood flow velocity vector field, and calculating an eddy current intensity feature map; and carrying out channel cascading on the binarized blood vessel mask image and the vortex intensity feature map to generate a blood vessel feature tensor, and generating a lesion detection result by improving a YOLOv7 model. According to the method, the accuracy, robustness and clinical applicability of vascular disease recognition are improved, and a technical closed loop from multi-modal data processing to automatic diagnosis report generation is realized.

The invention discloses a diabetic foot early-stage detection method combining infrared and visible light imaging, and relates to the technical field of diabetic foot medical imaging diagnos.The diabetic foot early-stage detection method comprises the steps that multi-mode image collection and standardization processing are conducted, infrared images and visible light images are synchronously collected, and standardization processing such as size normalization is conducted; image registration and space alignment are carried out, marking points are set based on foot anatomical features, and feature extraction, mismatching point elimination and transformation matrix calculation are carried out; extracting and screening multi-dimensional features, extracting temperature and structural features, and screening by using a Relief-F algorithm; feature lesion recognition and classification are fused, and lesion probability is output through a double-branch convolutional neural network; carrying out detection result verification and feedback optimization, and comparing a clinical diagnosis optimization model; and generating a detection report and storing data, and generating a report containing the fused image. The early lesion detection precision is improved through multi-modal fusion, individual and environment differences are adjusted and adapted in a personalized mode, and reliable technical support is provided for clinic.

The invention relates to the technical field of disease diagnosis, in particular to a sacroiliac joint lesion intelligent identification method and system based on MRI, and the method comprises the steps: data preparation and enhancement, multi-expert collaborative labeling, mixed 3D-2D model processing, verification and optimization, and deployment and interaction. Compared with the prior art that a scheme for detecting the sacroiliac joint lesion by adopting a single 3D CNN or 2D CNN model has the problems of inaccurate positioning of an anatomical structure and loss of inter-layer feature association, resulting in insufficient sensitivity and specificity, the method provided by the invention has the advantages that through an improved hybrid 3D-2D model architecture, spatial features are extracted by utilizing the 3D CNN and an inter-layer dependency relationship is modeled in combination with Transform, so that the sensitivity and the specificity of the sacroiliac joint lesion detection are improved, and the detection accuracy of the sacroiliac joint lesion detection is improved. Meanwhile, spatial pyramid pooling and a channel attention module are adopted to enhance the multi-scale feature extraction capability, so that the method has the advantages that the lesion detection sensitivity and specificity are remarkably improved, the bone marrow edema region can be more accurately recognized, and the misdiagnosis rate is reduced.

In the endoscopic examination support apparatus, the video acquisition means acquires an endoscopic video captured by an endoscope. The lesion detection means detects a lesion candidate from the endoscopic video. The heat map generation means generates a heat map which represents a lesion possibility of the lesion candidate included in the endoscopic video by a color. The display control means superimposes and displays an area where the lesion possibility is equal to or higher than a first threshold on the endoscopic video in a first display mode, and superimposes and displays an area where the lesion possibility is equal to or higher than a second threshold on the endoscopic video in a second display mode.

The invention discloses a novel interstitial lung disease lesion detection method based on deep learning, and relates to the technical field of intelligent medical treatment, and the method comprises the following steps: obtaining an HRCT image of a patient, and carrying out the format conversion of the HRCT image to obtain a CT image; inputting the CT image into a U-Net, and carrying out pathological pulmonary parenchyma segmentation on the CT image to obtain a segmented image; determining a lesion candidate region of the segmented image; and evaluating the lesion candidate region to determine whether the lung disease lesion occurs in the lesion candidate region. According to the method, the lesion areas of different CT images are accurately positioned, and the lesion is correctly identified and false positive areas are removed by extracting the statistical features, the shape features and the fractal features of the lesion candidate areas under the condition of interference of blood vessels and tracheas, so that the detection accuracy of novel interstitial lung disease lesion detection is greatly 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.

According to an embodiment of the present disclosure, an image analysis method is disclosed. The image analysis method may include: receiving a body medical image; detecting one or more lesions from the received body medical image using a lesion detection model; generating anatomical location information for the one or more lesions using an anatomical analysis model, based on the received body medical image and the detection result for the one or more lesions; and generating a diagnosis result for the received body medical image, based on the anatomical location information for the one or more lesions.

The invention discloses an auxiliary diagnosis and treatment system based on a large-scale language model, and belongs to the technical field of medical image analysis, and the auxiliary diagnosis and treatment system comprises a medical image agent based on a large-scale pre-training language model, a tool assembly and a storage pool module, the tool assembly comprises a report generation module, an image segmentation module, a focus detection module, a report evaluation module, a diagnosis and treatment assistant module and a storage pool module. According to the auxiliary diagnosis and treatment system based on the large-scale language model, natural languages input by a user are analyzed and converted into specific task prompts through the medical image intelligent body, complex medical image problems are automatically decomposed, tool assemblies are matched and called, detailed step-by-step solutions are generated, and compared with an existing device, the auxiliary diagnosis and treatment system based on the large-scale language model has the advantages that the efficiency is high; the problem that the operation is complex and not visual enough is avoided, the operation is simplified, and the use threshold is lowered.

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 cerebral apoplexy focus detection method and system, and belongs to the technical field of medical image detection. Extracting a fusion feature map of the brain image; for each region type, obtaining a representative feature which has the highest similarity with the feature at each pixel point position in the fused feature map in the class prototype set and carries a corresponding region type label, and further determining the region type to which each pixel point position in the fused feature map belongs so as to obtain a corresponding pseudo-label map; the class prototype set comprises representative features of different region types and is obtained in the training process of the system, and feature distribution of different region types in the memory bank is calculated through a Gaussian mixture model; for each region type, sampling is carried out based on feature distribution of the region type, and a plurality of representative features are obtained; the prototype-like set in the cerebral apoplexy detection method has real global context perception ability, can clearly distinguish the focus and various complex background structures, and can accurately realize cerebral apoplexy detection.

The invention discloses a tiny residual focus detection method and system based on ultra-high depth sequencing, and relates to the technical field of tiny residual focus intelligent detection.The tiny residual focus detection method comprises the following steps that on the basis of a sequencing library, splitting is conducted according to a sample index to obtain a to-be-detected sample, and a consensus sequence is obtained according to a molecular identifier of the to-be-detected sample; based on a consensus sequence, filtering out the consensus sequence of which the mass value is less than 25 or the family size is less than 3, and combining a variation type and a distance from a fragment edge as noise introduced into an original nucleic acid molecular chain; a context sequence (context) and a chain direction are used as noise for introducing the capture level of PCR amplification; on the basis of the noise level, the circulating tumor DNA level is estimated in combination with tumor priori knowledge, and the MRD state is determined by detecting the significance of molecular signal sources. According to the invention, the sensitivity and specificity of MRD detection are improved.

The embodiment of the invention provides a cerebral apoplexy focus detection and scoring method and device based on a multi-modal medical image. The method comprises the following steps: acquiring medical images of various different modals of a suspected acute ischemic cerebral apoplexy patient; performing registration fusion and focus labeling on the medical images of different modalities based on an attention mechanism to obtain a labeled first modal image; training a target detection model of a YOLOv8 framework based on Swin-Transform improvement by using the labeled first modal image, and carrying out target detection on the to-be-detected image based on the trained target detection model to obtain an infarction region detection result; training a semantic segmentation model by using the labeled ASPECT scoring and partitioning brain atlas, performing scoring region segmentation on the to-be-detected image based on the trained semantic segmentation model, and outputting the ASPECT scoring and partitioning brain atlas; and mapping the infarct area detection result to the ASPECT score partition brain map, and calculating to obtain an ASPECT score.