491 results about "Imaging modalities" patented technology

The invention provides a tumor area double-view detection method and system based on image modalities and views, and relates to the field of medical image analys.The method comprises the steps that multi-modal image data are collected from the body of a patient, and image data of different view angles at the same position are obtained according to time frames; extracting multi-modal features from the image data of different modalities and different view angles, and performing weighted fusion on the extracted multi-modal features to form a fused feature map; inputting the fused feature map to train and predict the 3D-CNN network so as to identify and position a tumor region, and marking the tumor region by using a mask output by the segmentation model; calculating the pixel number of the tumor area in each layer of image according to the marked mask, and multiplying the pixel number by the volume of each pixel to obtain the volume of the tumor area; and the segmentation results of each layer of image are superposed, and a three-dimensional model of a tumor region is generated by using 3D reconstruction, so that the limitation of single-mode and single-view image data in tumor detection is overcome.

An ophthalmic imaging system provides an automatic focus mechanism based on the difference of consecutive scan lines. The system also provides of user selection of a focus point within a fundus image. A neural network automatically identifies the optic nerve head in an FA or ICGA image, which may be used to determine fixation angle. The system also provides additional scan tables for multiple imaging modalities to accommodate photophobia patients and multi-spectrum imaging options.

The invention belongs to the technical field of image processing, and particularly relates to a motion artifact adaptive correction method based on multi-modal MRI fusion, which comprises the following steps: acquiring sMRI and fMRI of a patient, preprocessing the sMRI and fMRI, creating a cross-modal feature fusion model, and inputting the preprocessed sMRI and fMRI into the cross-modal feature fusion model for double-branch feature extraction; the cross-modal feature fusion model carries out cross-modal feature interaction fusion on the extracted double-branch features and carries out multi-scale feature reconstruction; detecting image artifacts based on the reconstructed multi-scale features, and performing dynamic space-time attention correction on the detected image artifacts; and establishing a three-level loss function system to carry out multi-task joint optimization. By integrating sMRI and fMRI information and based on image processing and deep learning, the defect that various complex head movement conditions cannot be completely handled in the prior art is overcome, and the technical problems of information loss between image modalities and insufficient feature utilization are solved.

The present disclosure relates to diagnosis of ocular diseases by leveraging an attention-based multimodal graphical neural network (AM-GNN) based on multimodal data that includes one or more image-text pairs. Each image of the image-text pair may depict a retinal scan of a subject that may correspond to one or more imaging modalities. A multimodal dataset may be collected from different imaging modalities by data crawling to automatically collect relevant data for the targeted diseases from identified sources. In some aspects, a concept bank, comprising concepts or terminologies associated with the targeted diseases, may be provided to the AM-GNN to generate a multimodal graph. The AM-GNN may be configured to find an association between a set of nodes including image-text pairs, a set of diseases, and the concepts using an attention mechanism. Based on the multimodal graph, an output may be generated, including a predicted disease and related concept(s).

The invention discloses an acoustic imaging positioning method for intelligent monitoring of electric power engineering regional faults, which relates to the technical field of fault monitoring, and comprises the following steps: arranging a microphone array to collect sound wave signals, and monitoring and recording sound data in real time; determining the spatial position of a sound source by using an ultrasonic sound source positioning technology; sound source distribution generated by the equipment fault is displayed; displaying the relationship between the three-dimensional position of the equipment fault point and the surrounding environment; fault sound sources are classified, and types and reasons are determined; a diagnosis result is fed back in real time, a fault position is displayed, and inspection and repair suggestions are provided; and detecting the health condition of the equipment in combination with a machine learning model, predicting potential faults and giving out early warning. According to the invention, a high-precision microphone array and an ultrasonic sound source positioning technology are utilized, the distribution state of a sound source in a space is displayed in real time through an imaging mode of superposing an acoustic image and a visible light image, a partial discharge point, a gas leakage point and an equipment abnormal sound site are rapidly determined, and the inspection efficiency and the maintenance speed are improved.

The invention relates to the technical field of image detection and quality assessment, and discloses an image-based cable stranded wire quality assessment method and system, and the method comprises the steps: obtaining an illumination reflection image set of a cable stranded wire through employing a multi-angle annular illumination imaging mode; constructing a multi-angle reflection image sequence; generating a defect suppression highlight image; carrying out image enhancement and three-dimensional normal estimation; and outputting a quality grade evaluation result. In the prior art, fine scratches, burrs and distortion defects are difficult to accurately identify under the condition of strong reflection interference, and particularly, high-resolution quality evaluation cannot be realized under the conditions of complex surface structure of a cable stranded wire and non-uniform illumination response. According to the method, the multi-angle illumination model and the angle domain sparse unmixing mechanism are constructed, and the defect enhancement driven by the normal disturbance and the multi-modal fusion classification are combined, so that the precise positioning and grade evaluation of the tiny defect area are realized, and the precision of the quality detection of the stranded cable is improved.

A platform for characterizing biomolecules, biological tissue sections, viruses, or other particles of interest in a non-invasive, high-throughput, cost-effective, and label-free manner. The platform includes a merged set of imaging modalities including and capable of interferometric microscopy and wide-field or confocal microscopy (such as, for example, a wide-field super-resolution surface enhanced Raman spectroscopy, SERS), although some aspects may be employed with data sets only received from wide-field or confocal microscopy. Related devices, systems, methods, techniques, and variations to the same are also provided.

A method is provided for optimizing workflow for a vascular intervention based on a three-dimensional dataset of a vessel, particularly a coronary artery, acquired from a 3D imaging modality, which involves:determining vessel centerline;performing segmentation in the dataset to identify the vessel lumen;performing plaque segmentation in the dataset to identifying the plaque in the vessel wall;defining a parameter related to the plaque severity in the vessel wall; andcreating a two-dimensional image spanning the length of the segmented vessel to illustrate both the plaque severity parameter and its spatial distribution in relation to the vessel wall circumference using the position of points along the centerline in the segmented vessel as primary axis.Other aspects are described and claimed.

The inventions provided herein relate to injection applicators, tissue markers and uses thereof, e.g., to mark a target tissue site (e.g., a biopsy site in a breast tissue) or to produce a cell scaffold. The tissue markers described herein are designed to be resistant to fast migration (e.g., immediate migration after implantation through a needle track) and slow migration (e.g., over an extended period of time) upon implantation at a target tissue site (e.g., a biopsy site in a breast tissue), without using an adhesive. Additionally or alternatively, the tissue markers described herein can be readily detectable by at least one imaging modality, e.g., but not limited to magnetic resonance imaging, X-ray imaging, ultrasound imaging, or a combination thereof.

An exemplary system accesses imagery of a surgical space captured by different imaging modalities and, based on the accessed imagery, generates composite imagery that includes integrated representations of the surgical space as captured by the different imaging modalities. An exemplary composite image includes a representation of the surgical space as captured by a first imaging modality augmented with an integrated representation of the surgical space as captured by a second imaging modality. The integrated representation of the surgical space as captured by the second imaging modality may be selectively movable and may be generated based on first imagery of the surgical space captured by the first imaging modality and second imagery of the surgical space captured by the second imaging modality in a manner that provides a visually realistic appearance of depth.

The invention provides a cross-modal fundus image registration method and device, equipment and a medium, and the method comprises the steps: converting a to-be-matched multi-modal image into a blood vessel image through a wide-area blood vessel segmentation network, the multi-modal image being two fundus images of different modals obtained by the same fundus of the same patient at different times and in different imaging modes; performing feature matching on the vascular image of the to-be-matched multi-modal image through the key point detection model to obtain a matching set; and realizing coordinate transformation of the to-be-matched multi-modal image by using a double-fitting strategy based on the matching set, and completing image registration. Therefore, according to the registration strategy, the best registration performance at present is achieved, the registration performance of other existing models is greatly improved through the cutting alignment strategy and the double-fitting strategy, and the cross-modal fundus image registration method can be widely applied to cross-modal fundus image registration under the large-view-difference condition.

Methods and systems for providing a dataset of human in-bed poses include simultaneously gathered images of in-bed poses of humans from imaging modalities including red-green-blue (RGB) and one or more of long wavelength infrared (LWIR), depth imaging, and pressure mapping. The images are obtained under a lighting condition and a cover condition. The dataset can be used to train a model of estimating human in-bed poses and for methods of estimating human in-bed poses. Methods and systems of estimating three-dimensional human poses from two-dimensional input images are provided.

Embodiments of the invention provide for a multimodal microscopy system. The multimodal microscopy system may include an illumination module configured to provide a light beam to a sample under test via a delivery path; and an optical arrangement configured to receive the light beam from the sample through a detection path to generate a microscopy image of the sample. The optical arrangement may be operable to switch among a plurality of imaging modalities using the detection path shared by the plurality of imaging modalities. The plurality of imaging modalities may include brightfield microscopy, fluorescent microscopy, and endoscopic microscopy. The multimodal microscopy system may be a multimodal hyperspectral microscopy system.

The present invention provides a method, including: obtaining a first image from a first imaging modality; identifying on the first image from the first imaging modality obtaining a second image from a second imaging modality; generating a compatible virtual image from the first image from the first imaging modality; mapping planning data on the compatible virtual image; coarse registering of the second image from the second imaging modality to the first image from the first imaging modality; identifying at least one element of the mapped planning data from the compatible virtual image; identifying at least one corresponding element on the second imaging modality; mapping the at least one corresponding element on the second imaging modality; fine registering of the second image from the second imaging modality to the first image from the first imaging modality; generating a third image.

The invention discloses a long-distance rail identification and obstacle detection method and system, and the method comprises the steps: combining a long-focus optical system with an ultra-high-speed industrial camera at a hardware level, and building a high-collection-frame-rate and long-distance optical detection system; on the software level, a region of interest for obstacle detection is delimited according to recognition and visual segmentation of left and right rail lines of a rail, advanced visual tasks such as a rail obstacle intrusion detection algorithm based on target detection are executed, and finally high-frame-rate recognition and detection of long-distance rails and rail obstacles are achieved. According to the invention, a Schmidt-Cassegrain optical system with a focal length of 2000mm is used, and an ultra-high-speed industrial camera with a resolution of 1920 * 1080 and 3000 frames per second is combined to build a long-distance high-frame-rate optical detection platform which is erected on a driving position of a train to detect railway conditions within a range of 6km in real time; serious accidents caused by overlong braking distance due to high-speed running of the train can be prevented. Compared with a traditional train driver eye observation and laser radar-based scanning imaging mode, a farther detection distance, better detection and recognition robustness and a higher frame rate can be realized through the long-distance optical observation platform and the rear-end real-time detection system.

The invention discloses an unmanned aerial vehicle airborne forest fire detection method based on double light (visible light and infrared light). The accuracy and reliability of fire detection are remarkably improved through a multiple confirmation mechanism. According to the method, the high maneuverability of the unmanned aerial vehicle and the multi-mode imaging capability of the dual-light camera are combined, so that the accuracy and efficiency of forest fire detection are remarkably improved. According to the method, a dual-light camera carried by an unmanned aerial vehicle is used for cruising in a forest area, and visible light and infrared light images are collected at the same time. The visible light image is used for capturing detail information of a fire scene, such as flame color and smog form, and the infrared light image can penetrate smog to directly detect heat distribution of a fire area, so that the problem of smog shielding is effectively solved. Through combination of an advanced image processing algorithm and a machine learning model, the method can automatically analyze image data, and through combination of visible light and infrared light imaging, defects of a single imaging mode are made up, and comprehensiveness of fire detection is improved.

The invention discloses a high-speed high-resolution compressed sensing imaging system and an imaging method. Light of an object is emitted to the polarization splitting prism to be transmitted and reflected after passing through the first-stage imaging module, the transmitted light is reflected back to the polarization splitting prism after being coded and modulated by the spatial light modulator, and then the transmitted light enters the target surface of the camera through the second-stage imaging module to form a coded imaging image; the reflected light is transmitted to the hollow ridge reflecting prism through the quarter-wave plate, is reflected back to the quarter-wave plate through the hollow ridge reflecting prism, then sequentially passes through the quarter-wave plate, the polarization splitting prism and the secondary imaging module and then enters the target surface of the camera to form a common imaging image, and then coded imaging data is matched with common imaging data; and decoding reconstruction is carried out by adopting an alternating direction multiplier method of a point spread function integrated with the first-stage imaging module and the second-stage imaging module. Compared with a traditional imaging mode, the method can break through the imaging frame rate limit of a camera, and has the advantages of high imaging resolution and small data volume.

The invention discloses a sonar data processing and image fusion method, which belongs to the technical field of image processing, and comprises the following steps of: performing geometric correction and registration processing on forward-looking sonar data by using respective advantages of side-scan sonar data and forward-looking sonar data and taking the side-scan sonar data as a benchmark reference source, so that the imaging mode of the forward-looking sonar data is matched with that of the side-scan sonar; multiple frames of images of the forward-looking sonar are integrated through an image splicing algorithm, and a water bottom image of a complete area is constructed; and finally, the blind area of the side-scan sonar is compensated by using a splicing result, so that complete reconstruction of underwater topography information is realized. The method is easy and convenient to operate and wide in application prospect, blind area compensation of the side-scan sonar image blind area can be achieved, and a complete underwater image is obtained.

The invention relates to the technical field of biological detection, and discloses an underwater organism detection method, device and equipment, a storage medium and a program product.A big language model is used for processing the category name of an underwater organism to be detected to obtain category description, and the name, a picture and the description are input into a target multi-modal detection model together for processing; the dependence on manual annotation is reduced, and the data processing efficiency is improved. Meanwhile, data information of two modes of text and image is fully utilized, the limitation of single-mode data is avoided, and the detection accuracy is improved. Furthermore, by calculating the similarity between the visual features of the target and the false detection clustering center and performing result judgment according to a similarity threshold value, the target possibly causing false detection can be effectively identified, so that the omission ratio can be better balanced, the detection performance reduction caused by improper false detection processing measures is avoided, and the detection accuracy is improved. The problem that the recall rate of the positive sample is reduced is solved, and the reliability of underwater biological detection is improved.

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging, and can be used to stratify populations to predict certain age related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retina or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. The methods disclosed herein teach a multi-modal Transformer-based architecture with cross-attention which was able to combine facial, tongue and retina images to estimate BA. The model was trained using facial, tongue and retina images from 11, 223 healthy subjects, and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. The approach was validated on a test population of 2,840 individuals with six chronic diseases, and obtained significant difference between chronological age (CA) and BA (AgeDiff) than that of healthy subjects. AgeDiff has the potential to be utilized as a standalonfe biomarker, or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. The results therefore highlight the feasibility of using multi-modal images to estimate and interrogate the aging process.

A real-time artificial intelligence (AI) framework is provided for the automated analysis of radiological images and detection of disease, such as extracapsular extension (ECE) in prostate cancer. The system includes a dual deep learning architecture comprising a first convolutional neural network (CNN) for identifying diagnostically relevant image slices from three-dimensional MRI data, and a second CNN for classifying disease presence based on those slices. A preprocessing pipeline standardizes and harmonizes image input, and cropping algorithms isolate the region of interest for enhanced model performance. This framework enables scalable, high-accuracy diagnosis across various imaging modalities including but not limited to MRI, CT, PET, ultrasound, and diverse disease types, improving clinical decision-making and supporting integration into real-time radiology workflows.

Systems and methods of detecting and monitoring edema in a subject are described. The systems and methods include light sources, image detectors, filters, and processors that determine whether a subject has edema and the degree of severity. In some embodiments, the processor fuses data from more than one imaging modality.

Methods and systems for identifying a region of interest in breast tissue utilize artificial intelligence to confirm that a target lesion identified during imaging the breast tissue using a first imaging modality (e.g. x-ray imaging) has been identified using a second imaging modality (e.g. ultrasound imaging). A computing system operating a lesion matching engine utilizes a machine learning classifier algorithm trained on cases of x-ray images and corresponding ultrasound images in which lesions were identified for further analysis. The lesion matching engine analyzes a target lesion identified with x-ray imaging and a potential lesion identified with ultrasound imaging to determine a likelihood that the target lesion is the same as the potential lesion. A confidence level indicator for the lesion match is presented on a display of a computing device to aid a healthcare provider in locating a lesion in breast tissue.

Disclosed herein is a method for generating one or more synthetic electron density, sED, images. The method comprises obtaining a first image of a first imaging modality, the first image depicting an anatomical region of a subject. The method also comprises generating, using a trained machine learning model and the first image, a sED image depicting the anatomical region, wherein the trained machine learning model has been trained using a set of training images comprising a first subset of training images of the first imaging modality, and a second subset of training images in which each training image comprises electron density, ED, information.

Planning cardiac surgery is a complex task. Current heart models are mostly static and provide no functional information or personalization apart from structural information from imaging modalities like MRI, CT etc. The embodiments of present disclosure addresses unresolved problem of generating a personalized four dimensional cardiac model that integrates blood flow velocity, pressure profile and volume on three-dimensional cardiac structure, providing a comprehensive tool for pre or post-surgical planning and analysis. Unlike existing techniques, which often rely on separate assessments of cardiac anatomy and hemodynamics, using invasive methods, the present disclosure provides a 4D cardiac model that captures the person specific dynamic interaction between cardiac structures and blood flow throughout the cardiac cycle. Further, the 4D cardiac model enables clinicians to visualize and quantify complex flow patterns and pressure distributions within the region of interest in the heart, facilitating precise surgical planning and outcome prediction especially in pediatric heart surgery.

The invention discloses a human body peripheral blood vessel photoacoustic three-dimensional imaging device and an imaging method thereof, and belongs to the field of blood vessel imaging. Through high resolution and functional monitoring capability of photoacoustic imaging, detailed blood vessel structure and blood flow dynamic information are provided, and early recognition and treatment effect evaluation of common lesions such as diabetic foot and peripheral artery diseases are promoted; meanwhile, a mechanical system is adopted, so that the detection front end with relatively high quality can be borne, and stable operation is realized, and a relatively good photoacoustic imaging result is obtained; the system has a highly stable accurate scanning imaging mode, and is convenient for multiple imaging modes of clinical application; through the arrangement of the imaging water tank and the lifting structure of the imaging water tank, switching of imaging modes in a limited space can be ensured, so that the device can be suitable for the layout of a portal type three-dimensional moving device. The system is expected to promote the portability of equipment, reduce the research and development cost of the equipment, be widely applied to clinical practice and community medical treatment, and improve the disease management level.

The invention belongs to the technical field of medical ultrasound, and discloses a stretchable double-frequency flexible ultrasonic transducer which comprises a front packaging layer, a back packaging layer, a flexible electrode substrate, a flexible electrode-positive electrode, a transducer array, a backing layer, a flexible electrode-negative electrode and a shielding layer. Wherein the material strength and the tensile fatigue resistance of the wave-shaped flexible electrode are far better than those of the current mainstream flexible probe electrode with an island bridge structure and other material structures, so that the overall mechanical property of the probe is improved; according to the imaging mode of the double-frequency transducer array, imaging of tissues with different depths can be achieved, and higher-quality imaging of deep tissues can be achieved through harmonic imaging. Compared with other flexible ultrasonic transducers, the flexible ultrasonic transducer has better mechanical performance, better deep tissue imaging quality and more application scenes.

A method of virtual staining of a tissue sample includes obtaining multiple sets of imaging data. The imaging data depicts a tissue sample and has been acquired using multiple imaging modalities. Further, the method includes fusing and processing the multiple sets of imaging data in a machine-learning logic. The machine-learning logic is configured to provide at least one output image. Each one of the at least one output image depicts the tissue sample comprising a respective virtual stain.

Methods and systems are provided for generating images of a modality other than the modality of the sensor from which the image was acquired. For example, methods and systems described herein may acquire a temporal sequence of frame data captured by the image sensor. Once the temporal sequence of frame data is acquired, a desired imaging modality may be determined and a projection operation on the frame data may be performed. Based on the results of the projection operation, a post-capture emulation may be generated, corresponding to the desired imaging modality. An image may be outputted corresponding to the post-capture emulation.

The invention relates to the technical field of multi-modal ophthalmology image analysis, and particularly discloses a multi-modal ophthalmology image analysis method, model and system based on optimal transmission image diffusion and a medium thereof.The method at least comprises the steps that S100, the distance between fundus color photo and OCT image modals is explicitly calculated through an optimal transmission algorithm, and the distance between fundus color photo and OCT image modals is calculated; carrying out collaborative evolution on the features in a continuous time domain in combination with a graph neural network and a Sheng differential equation model; and step S200, applying self-attention and cross attention in parallel, reserving modal specificity with low-layer fine granularity, and progressively fusing the color photo texture and the OCT depth structure in a high layer to realize cross-layer long-range dependent depth semantic fusion. The method not only promotes the leading-edge development of multi-modal medical image analysis, but also provides important scientific basis and technical support for constructing a high-precision and high-robustness intelligent ophthalmology diagnosis system.