42 results about "Anatomical location" patented technology

The invention discloses an intelligent planning method for a personalized scanning path of an ultrasonic robot, and relates to the field of ultrasonic robos.The intelligent planning method comprises the steps that anatomical structure information and clinical scanning demand information of a to-be-scanned part of a target patient are collected, a patient exclusive basic information set is established, and the anatomical structure information comprises spatial distribution characteristics of all tissues of the part; clinical demand information comprises a preset focus region and a scanning precision index, an exclusive basic information set is established by collecting an anatomical structure of a to-be-scanned part of a patient and the clinical scanning demand information, spatial correlation characteristics are analyzed, and a real-time physiological and anatomical position dynamic correlation rule is embedded to construct a dynamic anatomical correlation model; and individual anatomical dynamic changes of the patient can be accurately matched.

A method for assessing a risk associated with a skin abnormality is disclosed. The method involves using a skin abnormality detection model to analyze a current image dataset captured by a skin inspection device to identify a current instance of a feature of interest and determine an associated risk level. The model is trained using a process that generates enriched training data. The process comprises receiving user-generated annotations for a feature of interest across a series of historical image datasets captured over time. These historical datasets are aligned using an image registration process to establish a consistent anatomical location for the feature. A set of training data records is then generated, with each record comprising an annotation, a set of image acquisition parameters used for the corresponding image, and data defining the consistent anatomical location. The skin abnormality detection model is subsequently trained using this set of training data records.

This invention discloses a personalized intelligent planning method for ultrasound robot scanning paths, relating to the field of ultrasound robots. The method includes: collecting anatomical structure information and clinical scanning needs information of the target patient's area to be scanned; establishing a patient-specific basic information set; wherein the anatomical structure information includes the spatial distribution characteristics of various tissues in the area; and the clinical needs information includes preset key areas of focus and scanning accuracy indicators. This invention establishes a patient-specific basic information set by collecting anatomical structure information and clinical scanning needs information of the patient's area to be scanned, analyzes spatial correlation features, and embeds real-time physiological and anatomical position dynamic correlation rules to construct a dynamic anatomical correlation model, which can accurately match the individual anatomical dynamic changes of the patient.

The application discloses a kind of operating room consumable use analysis method and system, it is related to medical instrument management technical field, method includes: obtaining operation video and force data in surgical instrument operation process;According to the force data, generate operation reference;According to the force data and the operation reference, identify key mechanical event and target timestamp;According to the target timestamp, extract the video frame corresponding to the key mechanical event from the operation video;Determine the two-dimensional position of surgical instrument tip on the video frame;According to the two-dimensional position of surgical instrument tip, generate target anatomical position in three-dimensional medical image model;According to the target timestamp, the force data and the target anatomical position, generate event evidence chain.The application realizes consumable use analysis, and improves accuracy.

This application provides a method for manufacturing a multi-zone integrated silicone orthodontic appliance, comprising: acquiring three-dimensional data of a user's jaw and constructing a three-dimensional digital model of the appliance based on the three-dimensional data of the user's jaw; obtaining anatomical location data of teeth and orthodontic biomechanical design data, and dividing the target appliance into functional zones with at least two different hardnesses based on the three-dimensional digital model of the appliance, and assigning a zone hardness value to each functional zone; performing mechanical simulation on the three-dimensional digital model of the appliance, analyzing the stress distribution information of each functional zone acting on the teeth and periodontal tissues under the zone hardness value configuration of the virtual appliance constructed by the three-dimensional digital model of the appliance, so as to optimize the three-dimensional digital model of the appliance based on the stress distribution information feedback; converting the manufacturing data output by the optimized stage three-dimensional digital model of the appliance into manufacturing instructions, and driving a printing device to manufacture the corresponding appliance mold or print the corresponding appliance according to the manufacturing instructions.

This invention discloses an injection method for CaHA microsphere products that enhance facial three-dimensionality through multi-layering, relating to the field of cosmetic technology. The method includes: precise injection techniques and area division based on specific needs; and a combination of needle angle, injection depth, layer, and dosage for specific anatomical locations. Specifically designed for Asian facial structures, it provides a strong, naturally integrated solution. By using CaHA microsphere injection products, the following effects can be achieved: Facial volume replenishment: effectively replenishing facial soft tissue atrophy and fat loss caused by aging, skin aging, or genetic factors, restoring facial fullness. It is commonly used to fill sunken areas of the face, such as tear troughs, nasolabial folds, sunken temples, and sunken cheeks.

A wearable device for providing sensory feedback in an extended reality environment is disclosed. The device includes a band configured to enclose an anatomical location, such as a wrist, adjacent to a distal extremity, such as a hand. The band houses a plurality of electrodes that deliver transcutaneous electrical nerve stimulation (TENS) to the distal extremity, eliciting distally referred sensations corresponding to the user's interaction with a digital object. A controller receives sensor signals indicative of movement of the distal extremity, determines the user's motor intent, and generates a sensory feedback signal to control the delivery of TENS pulses. The device allows for anatomically congruent sensory feedback in augmented reality environments without inhibiting natural interactions with physical objects.

This invention relates to the field of medical control technology, specifically to a glioma ECoG electrode localization system based on the fusion of multimodal MRI and intraoperative photographs. It includes a brain imaging data acquisition module for preoperative acquisition of multimodal brain imaging data and an acquisition module for acquiring intraoperative craniotomy photographs for ECoG electrode placement. The system further aligns the multimodal brain imaging data to the same three-dimensional spatial coordinate system; then segments and corrects the multimodal brain imaging data into different regions; and establishes an individualized brain region atlas based on a healthy brain tissue texture model and segmented brain regions. Vascular structures are extracted, and a three-dimensional spatial coordinate system for vascular feature points and electrode contacts is established and transformed. The minimum Euclidean distance is calculated based on the electrode contacts, and the individualized brain region atlas is labeled. This invention addresses the problems of insufficient spatial positioning accuracy of electrode contacts and difficulty in quantifying tumor-electrode distance during surgery for glioma patients, facilitating intraoperative determination of the anatomical location of the epileptogenic focus by surgeons.

According to an aspect of the present inventive concept there is provided an ingestible device for analysis of the gastrointestinal tract, the ingestible device comprising: at least one sensor configured for sensing a biochemical parameter, wherein the at least one sensor is configured to generate biochemical parameter data for identifying an anatomical localization of the ingestible device in the gastrointestinal tract, and a magnetic field sensor configured to receive an alternating magnetic field for generating spatial location data representing a spatial localization of the ingestible device, and wherein the ingestible device is configured to provide a combined localization of the ingestible device in the gastrointestinal tract based on the anatomical localization and the spatial localization.

The application discloses an anorectal manometry anatomical position positioning method and device, equipment and medium, and relates to the technical field of medical instruments, and comprises the following steps: determining the pressure average value of each channel in an anorectal pressure cloud chart within a preset time period; finding a first peak channel and a second peak channel with the maximum pressure average value in each channel within a rectal range and an anal range respectively to determine a rectal peak position and an anal peak position; interpolating the pressure values of the channels to obtain each interpolated pressure value; determining a rectal characteristic value within the rectal range and an anal characteristic value within the anal range based on the interpolated pressure values and the pressure average values; the characteristic values comprise the interpolated pressure average value, a peak value ratio, a minimum pressure ratio and a pressure difference; and determining a rectal edge channel and an anal edge channel from the anorectal pressure cloud chart according to the rectal characteristic value and the anal characteristic value to determine a rectal edge position and an anal edge position. The application realizes accurate positioning of an anorectal manometry anatomical position.

The invention discloses a method for generating an endoscopic image-text report of a tumor under a digestive tract mucous membrane. The method comprises the following steps: receiving real-time inspection videos of a white light endoscope and an ultrasonic endoscope; carrying out image preprocessing, and screening qualified white light endoscopic images and ultrasonic endoscopic images; identifying an endoscopic image containing a lesion, and carrying out redundancy deletion on the image; lesion segmentation and classification are carried out to obtain pathological properties and lesion features of lesions; identifying an anatomical position through a white light endoscopic image, and determining a lesion anatomical position; performing quality evaluation on the white light endoscopic image and the ultrasonic endoscopic image after redundancy deletion, and selecting a typical image of a lesion; and outputting pathological properties, lesion features, anatomical positions, the typical white light endoscopic image and the ultrasonic endoscopic image of the same lesion to generate a standardized image-text report. According to the method, the standardized image-text report can be automatically generated in the endoscopic examination process of the tumor under the digestive tract mucosa, the report accuracy and consistency are improved, and the writing workload of an endoscopic physician is reduced.

The present application proposes a system for differential recording in real-time navigation, connectable to an electrical lead having at least two electrodes, comprising: at least one electrical lead having a longitudinal axis and a distal end and at least two electrodes for recording electrical signals; a memory configured for storing a differential signal between the at least two electrodes and a reference indication of electrical signals associated with a neural tissue; a processing circuit, wherein the processing circuit: during advancement of the at least one electrical lead, computes in real-time a differential signal between electrical signals recorded from the at least two electrodes, processes the differential signal and the reference indication of electrical signals associated with the neural tissue; and computes an anatomical position of the electrical lead based on the processing, wherein the computing an anatomical position comprises estimating a proximity between at least one of the electrodes or the distal end of the electrical lead and a boundary between anatomical regions.

This invention discloses a cross-modal cell alignment method for pathological images in non-registerable scenarios. The method acquires H&E and mIF stained images and their data-augmented views corresponding to spatial anatomical locations; constructs H&E modality processing branches, mIF modality processing branches, and a reverse self-attention module; and sequentially performs three-stage progressive training: intra-modal contrastive pre-training based on the data-augmented view; using mIF modality-level semantic aggregation tokens from the same spatial anatomical location as positive samples and mIF modality-level semantic aggregation tokens from different spatial anatomical locations as negative samples, achieving H&E to mIF modality-level semantic alignment through contrastive learning; inputting the aligned H&E modality-level semantic aggregation tokens and the projected H&E cell feature embedding set into the reverse self-attention module, and achieving unsupervised cell-level semantic alignment by minimizing the Hungarian matching loss; finally, the output H&E cell features semantically aligned with mIF are embedded into a refined set, which can be directly used for downstream pathological analysis tasks such as high-precision cell clustering.

A method may be provided to operate a surgical robotic system including a robotic arm configured to position a surgical end-effector with respect to an anatomical location of a patient. Position information may be received where the position information is generated using a sensor system remote from the robotic arm and remote from the patient. The position information may include position information relating to a tracking device affixed to the patient and position information relating to the surgical end-effector. The robotic arm may be controlled to move the surgical end-effector to a target trajectory relative to the anatomical location of the patient based on the position information generated using the sensor system.

This document discloses a medical system (100, 300, 400). Execution of machine-executable instructions (120) causes a computing system (110) to repeatedly perform the following operations: receive (204) segmentation editing data (130) from a segmentation entry tool (128); collect (206) location-specific segmentation tool usage metadata (134) describing the use of the segmentation entry tool to enter segmentation editing data at the editing location; determine (208) an anatomical location by using the editing location; retrieve (210) expected segmentation tool usage metadata from a segmentation tool usage database (122) by using the anatomical location; determine (212) the presence of anomalous segmentation editing data (806) by comparing the location-specific segmentation tool usage metadata with the expected segmentation tool usage metadata; provide (214) signals (142, 404, 808) if anomalous segmentation editing data is determined to exist; and construct (216) a segmentation result of a medical image (144) by using the segmentation editing data.

A method and a system include obtaining calibration scan data or low resolution images of a subject acquired with a magnetic resonance (MR) scanner of an MR imaging system. The method and the system also include inputting the calibration data or the low resolution images into a trained deep learning-based multi-mask segmentation network. The method and the system further include outputting labeled mask images for different anatomical stations, wherein a respective mask of a respective labeled mask image highlights an anatomical landmark of interest in each respective anatomical station of the different anatomical stations. The method and the system even further include determining an extent of a respective anatomical landmark of interest in each respective anatomical station of the different anatomical stations for a respective localizer scan for each respective anatomical station based at least on a respective label mask image for each respective anatomical station.

Techniques are described for augmenting chest X-ray (CXR) images with synthetic lesions using a lesion-aware machine learning framework in association with optimizing thoracic disease detection models. In an example, a system can comprise a lesion augmentation component that generates synthetic lesion images comprising synthetic lesion image data objects integrated on or within medical images using lesion generator, trained to generate the synthetic lesion image data objects, and tailor the synthetic lesion image data objects to account for different types of lesions and different anatomical locations of the lesions. The system can further comprise a training component that trains a lesion detector using the synthetic lesion images. The training component can further perform an alternate training strategy to integrate the training of the lesion generator and the detector for mutual boosting.

The system includes a display and a processor. The display is configured to present a rendering of at least a portion of a septum and left atrium of a patient's heart. The processor is configured to (a) identify in the rendering (i) the septum and (ii) a target anatomical location for a probe to reach through the septum, (b) calculate a trajectory of the probe between the septum and the target anatomical location, including identifying an entry location for the probe to cross the septum to reach the target anatomical location, and (c) present the entry location for penetrating the septum to a user.

This invention relates to the field of medical imaging diagnostic technology, specifically to a method and system for precise localization of small lesions in the breast and thyroid glands based on AI multimodal imaging. The method includes the following steps: acquiring ultrasound, X-ray, and magnetic resonance images of the breast and thyroid glands; extracting anatomical spatial location and imaging direction; calculating and mapping multimodal pixel spatial offsets; calculating scale consistency and normalizing response intensity based on co-located pixels; comparing multimodal intensity differences to assess consistency and form a scoring distribution; combining the score and intensity to adjust conditional probabilities; calculating joint probabilities and selecting spatial aggregates to locate small lesions. In this invention, by uniformly calibrating the spatial location and imaging direction of multimodal images, precise correspondence between different images at the same anatomical location is achieved. Multimodal response intensities are fused and consistency assessment is performed. Combining probability weight adjustment and spatial aggregation weakens the influence of noise in single images, enhances the common expression of small abnormal areas, and improves the stability and consistency of lesion localization.

This invention relates to the field of imaging detection technology, specifically to a method and system for target detection in urological stone images. The method includes: acquiring each urological stone image data input by a user; identifying potential target regions with specific imaging features in each urological stone image data; performing preliminary region assessment on the potential target regions to determine target marker regions; performing image analysis on the target marker regions to obtain image enhancement feature coefficients, anatomical location weighting coefficients, and subtle feature analysis coefficients; and performing stone feature analysis on the target marker regions based on the image enhancement feature coefficients, anatomical location weighting coefficients, and subtle feature analysis coefficients to obtain auxiliary detection results for urological stones. The purpose of this invention is to solve the problem that existing stone imaging detection methods are unable to accurately identify small stones, resulting in low detection accuracy.

A moulage task trainer simulation wound device provides a plurality of tissue layers having differential densities so as to replicate the dynamics and feedback of a real wound. The simulation wound enables definition of a cavity simulating a puncture wound or the like. Four-way stretch material may be integrated into one or more of the plurality of tissue layers to further enhance the fidelity of the training process. The carrier of the simulation wound provides fixation mechanisms that enable adjustability for attaching the task trainer device on a wide array of body types and anatomical locations of a role-player or mannequin.

This invention discloses a method for generating virtual ultrasound images from magnetic resonance images based on a physics-guided Transformer adversarial network, belonging to the field of medical imaging diagnostic technology. The method includes: acquiring three-dimensional magnetic resonance images and real ultrasound images of the same anatomical location to construct a training dataset; building a dual-branch generator containing a semantic coding branch, an acoustic physical simulation branch, and a feature fusion decoder; the semantic coding branch extracts global anatomical features, and the acoustic physical simulation branch generates physical prior features; combining a dual-domain discriminator with a hybrid objective function containing adversarial and perceptual losses for end-to-end joint training; inputting the magnetic resonance image to be processed into the trained generator; and outputting a virtual ultrasound image for medical diagnosis after dual-branch collaborative processing. This method overcomes the shortcomings of traditional methods, such as insufficient physical consistency and poor feature fusion effects. The generated virtual ultrasound image possesses both anatomical accuracy and texture realism, providing reliable support for clinical diagnosis and related medical scenarios.

An artificial intelligence (AI) measurement system for an X-ray image is employed either as a component of the X-ray imaging system or separately from the X-ray imaging system to automatically scan post-exposure X-ray images to detect and locate various landmarks of the anatomy presented within the X-ray image. A set of key image features approximating the locations of the landmarks having known distance relationships to one another is overlaid onto the X-ray image. The positions of the key image features are then adjusted to correspond to the landmarks within the X-ray image. These adjustments are made relative to the prior known distance relationships between the key features, which enables the measurement system to readily calculate desired angular and length measurements between landmarks as a result.

This medical image analysis method performed by a medical image analysis device comprises the steps of: acquiring medical images of a patient captured at different time points; analyzing at least one medical image captured at each time point, so as to detect a lesion, and recognizing location identification information inserted in the corresponding medical image so as to identify an anatomical location of the lesion; clustering, on the basis of the anatomical location of each lesion detected from the medical images, lesions detected from the medical images, so as to generate lesion clusters; and identifying lesions included in each lesion cluster as the same lesion, and comparing images at different time points including the same lesion so as to track changes in the lesions over time.

The application relates to the technical field of image processing, in particular to a full-coverage face subregion method and system. The method comprises the following steps: collecting multiple sets of face images, wherein the face images comprise front face images and side face images; labeling face key points and face skin region masks on each face image to form an image-point set-mask triple data set, wherein the face key points comprise predetermined anatomical positions; taking the triple data set as sample data, generating an initial analysis model through a deep neural network model; integrating a pre-defined grid partition generation rule set into the initial analysis model to generate a face subregion model, wherein the face subregion model is used for outputting face key point coordinates, face skin region masks and a grid partition generated based on the key points. The application can improve the accuracy of face subregion division.

The invention provides a subject body position guiding method and device, a storage medium and equipment, and the method comprises the steps: obtaining a current body position of a subject according to sensor data collected by a plurality of inertial measurement units arranged at the trunk key anatomical positions of the subject, evaluating the difference between the current body position and a target body position, and determining the body position of the subject according to the difference between the current body position and the target body position; therefore, a body position adjusting strategy is generated, and the target object is rendered in real time on the guide interface based on the body position adjusting strategy so as to display the poses of different parts of the body of the subject and the directions needing to be adjusted. Therefore, by monitoring the body position of the user in real time and providing the graphic guiding interface to display the body position adjusting suggestion, the user can be helped to better cooperate in the capsule endoscopy process, and therefore the examination quality and efficiency are effectively improved.

This invention relates to the fields of computer vision and medical image processing technology, and discloses a method for extracting lesion structures from cardiac magnetic resonance imaging (MRI) images based on deep learning. The method first retrieves dynamic cardiac cine sequences and delayed gadolinium-enhanced sequences from the same anatomical location, extracts the centroid trajectory of the left ventricular blood pool, and performs cross-sequence axial alignment and non-rigid deformation correction. Subsequently, a local orthogonal coordinate system based on the myocardial anatomical topology is constructed, and pixel-level motion vector fields with physical constraints are calculated in the dynamic cardiac cine sequences. By projecting the motion vectors onto the local orthogonal coordinate system and performing vector clipping according to the centripetal contraction physical constraint, an effective motion vector field is generated. Finally, dynamic features and image grayscale features are fused, and a logic gating operator is used to extract the lesion structure mask.

The invention relates to the technical field of image recognition, and discloses an image value analysis method and system for a large number of images in an alimentary canal, and the method comprises the steps: obtaining an alimentary canal image sequence, calculating an optical flow field between adjacent frames, and classifying the alimentary canal image sequence based on the rotation and divergence of the optical flow field, obtaining a motion type label of the alimentary canal image sequence; performing redundancy processing on the alimentary canal image sequence by using the optical flow field to obtain a screened alimentary canal image sequence; performing feature extraction on the screened alimentary canal image sequence, determining an anatomical structure turning point of the screened alimentary canal image sequence and labeling an anatomical position label; carrying out image value analysis on the screened alimentary canal image sequence according to a motion type label, and identifying a lesion area; and carrying out value scoring on the screened alimentary canal image sequence based on the lesion area and the anatomical position label to obtain a value analysis result. And the analysis efficiency and accuracy of the digestive tract image are improved.

This invention provides a garment and monitoring method for visualizing muscle activation. The garment includes a main body made of elastic fabric that fits snugly against the wearer's body; at least one muscle activation indicator area disposed on the main body corresponding to the anatomical location of a specific target muscle group; and a smart color-changing material layer covering the muscle activation indicator area. The smart color-changing material layer undergoes reversible optical property changes in response to changes in the physical field generated during contraction or metabolism of the target muscle group. This technology utilizes the physical properties of materials to convert internal physiological signals into visible optical signals, possessing advantages such as zero energy consumption, no electronics, comfort and durability, low cost, and effective muscle activation feedback, and has high market premium potential. This smart garment based on novel materials not only enhances the user's exercise experience but also provides an innovative solution for the fitness and rehabilitation fields.

Endoscopic systems and methods capture images inside a body including a lung airway using a multi-view camera tool, and estimate image depth and generate a three-dimensional (3D) point cloud volume of a patient anatomy using a combination of positional information from EM and / or IMU sensors of the multi-view camera tool. The 3D point cloud volume is generated from known favorable points using stereoscopic images captured by the camera tool. Such 3D structure generation may use a stereoscopic image correction algorithm. And a machine learning algorithm can also be applied to replace an image correction algorithm or be combined with the image correction algorithm, so that the calculation efficiency is improved.