10 results about "Clinical imaging" patented technology

This invention discloses a method and system for pontine infarct segmentation and END prediction based on multimodal joint learning. The method includes acquiring and preprocessing multimodal data; constructing a wavelet transform-based feature encoding network to perform multi-scale decomposition and detail preservation of features; constructing a dual-task guided fusion module to align the deep semantics of clinical variables and imaging features and generate task-specific representations; constructing a Mamba-based global feature aggregation module to model sequence dependencies using a state-space model; constructing a multimodal second-order fusion classifier to enhance the clinical-image interaction modeling using second-order statistics; and employing a two-stage joint training strategy for training and prediction, and outputting the prediction results. This invention utilizes the DWT / IWT mechanism to significantly improve the accuracy of capturing small pontine infarct lesions; it achieves explicit interaction between segmentation evidence and prediction signals, significantly improving the segmentation accuracy of small lesions and the reliability of stroke risk assessment.

The application belongs to the technical field of image processing, and discloses an image enhancement processing method for a visual bronchoscope. The method is applied to a visual bronchoscope with a distal eccentric integrated imaging module and a non-coaxial sputum suction channel inside the integrated imaging module. First, current frame original image data collected by the integrated imaging module and real-time posture data of the visual bronchoscope are acquired, and global spatial parameters are calculated in combination with pre-calibration parameters. Then, image correction is completed according to the global spatial parameters to obtain a standardized corrected image. Corresponding enhancement processing is performed on the corrected image in a regional manner, and an operation view image is obtained after feature alignment fusion and coordinate conversion, and is output in real time after optimization. The method combined with the eccentric bronchoscope can improve the definition and uniformity of the bronchoscope image, ensure the matching of the view and the operation path, and meet the clinical imaging requirements.

The application discloses a pneumoconiosis staging recognition method and system based on multi-modal artificial imaging feature fusion, and relates to the technical field of medical image recognition. The method comprises the following steps: standardizing and pre-processing an input chest X-ray image and extracting a lung field region; in the lung field region, detecting and counting micro nodules based on clinical imaging prior knowledge, and extracting micro nodule quantity features reflecting the quantity and spatial distribution characteristics of nodular lesions; calculating the first-order entropy features of the lung field region image to quantify the complexity of lung parenchyma texture, and extracting high-dimensional features representing gray heterogeneity and structure statistical characteristics by using an imaging feature analysis method. The multi-class artificial imaging features are standardized and fused in a unified feature space, and the fused features are input into a learning classification model for pneumoconiosis period recognition. The application improves the stability and interpretability of pneumoconiosis staging recognition through multi-modal artificial imaging feature fusion modeling.

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

This invention belongs to the field of image processing technology and discloses an image enhancement method for visual bronchoscopes. This method is applied to a visual bronchoscope with an eccentrically positioned integrated imaging module at the distal end and an internal suction channel that is non-coaxial with the integrated imaging module. First, the method acquires the current frame's raw image data from the integrated imaging module and the real-time attitude data of the visual bronchoscope, and calculates global spatial parameters using pre-calibrated parameters. Then, image correction is performed based on the global spatial parameters to obtain a standardized corrected image. Corresponding enhancement processing is performed on the corrected image by region, and after feature alignment and fusion, coordinate transformation, an operational view image is obtained, which is then optimized and output in real time. This method, combined with an eccentric bronchoscope, can improve the clarity and uniformity of bronchoscope images, ensure the match between the view and the operational path, and meet clinical imaging needs.

This invention discloses a generative AI diagnostic and treatment platform that integrates gene data and clinical images, belonging to the field of medical technology. It includes modules for data acquisition, desensitization and protection, data processing, feature extraction, gene analysis, fusion analysis, predictive analysis, treatment plan generation, result display, model management, self-iteration, and user management. This invention enables non-invasive and accurate diagnosis, reducing potential harm to newborns, minimizing unnecessary repeat examinations, lowering overall diagnostic costs, and improving the stability and accuracy of disease prediction results. Simultaneously, this invention enhances the foresight and scientific rigor of treatment plan development, providing strong support for early intervention and precision medicine, improving diagnostic efficiency and risk assessment accuracy, and saving hospitals and patients significant time and effort.

The present application belongs to the technical field of medical image intelligent analysis and auxiliary diagnosis, and specifically discloses a thyroid nodule auxiliary diagnosis method and system based on multi-modal fusion, which is used for intelligent classification and auxiliary diagnosis of thyroid nodules. The method comprises the following steps: collecting and labeling medical image data and clinical structured data; standardizing and normalizing the data for pretreatment; constructing an image feature extraction branch and a clinical feature extraction branch to extract the feature representation of each modality; jointly modeling the image features and the clinical features through a strip perception module (SAC) to form a fused clinical-image context representation; generating channel-level adaptive parameters through a feature-level linear modulation method to dynamically adjust the image features individually; and finally completing the classification of thyroid nodules based on the fused multi-modal features. The present application realizes an image feature modeling method involving clinical structured data, which enables dynamic fusion of patient individualized clinical background for image interpretation, and provides more comprehensive, stable and reliable technical support for computer-aided diagnosis of thyroid diseases.

This utility model discloses a desktop X-ray machine, specifically relating to the field of X-ray machine technology. It includes a base with a mounting groove at its top. A flat panel detector assembly and a stage are housed within the mounting groove, with the stage positioned above the flat panel detector assembly. The flat panel detector assembly includes a flat panel detector, which is fixed within the mounting groove via a linear motor slide rail. An X-ray assembly, including an X-ray source, is fixedly mounted on the rear side of the top of the base. The rear end of the X-ray source is slidably connected to an arc-shaped track. This utility model synthesizes a three-dimensional image through the coordinated operation of the X-ray source and the flat panel detector, facilitating the viewing of lesion details. Its desktop, compact design reduces product size and meets the needs of two-dimensional and three-dimensional clinical imaging. The arc-shaped track and linear motor slide rail allow for independent adjustment of the X-ray source and the flat panel detector's positions, thereby improving the product's flexibility.

The individualized virtual laparoscopic surgery robot platform and device belong to the technical field of medical robots and are constructed based on clinical specific patient disease characteristics and individualized anatomical characteristics.The platform comprises an individualized three-dimensional bionic model reconstruction module, an array module of mechanical arms for simulating the operation of laparoscopic surgery, a virtual surgery feedback module for providing real-time force feedback and visual feedback, and an interactive updating and storage export module for switching the visual angle of the virtual bionic organ in real time through a high-resolution, adjustable focal length virtual laparoscope lens and interacting with the aforementioned components to operate the array module of mechanical arms and control the updating of the individualized three-dimensional bionic model.The virtual laparoscopic surgery robot platform for individualized cases has a unique multi-module interaction and system integration strategy and realizes highly realistic and individualized surgery simulation.

This application discloses a method for predicting patellofemoral arthritis based on lateral X-ray radiomics of the knee joint. First, lateral X-ray images of the knee joint and clinical information of the patient to be tested are acquired. Then, the X-ray images and clinical information are input into a clinical radiomics prediction model. The model identifies regions of interest (ROIs) in the X-ray images and extracts features from these ROIs to obtain candidate radiomics features. These candidate features are then filtered according to dimensionality reduction rules to obtain target radiomics features. The types of radiomics features include at least grayscale features, shape features, texture features, and wavelet transform features. This application, by using a prediction model to extract and filter features from ROIs in X-ray images, accurately aligns with the diagnostic criteria for patellofemoral arthritis defined by MRI, improving the reliability of the prediction. Simultaneously, by using low-cost X-ray examinations to replace high-cost MRI, patients can detect the condition earlier without compromising diagnostic effectiveness, thus reducing the burden of examinations.