329 results about "Anatomical landmark" patented technology

A method for aligning 3D objects in an extended reality (XR) system involves obtaining 3D objects from a volumetric imager or surface scanner, where the volumetric imager captures a three-dimensional voxel array representing an anatomical structure and the surface scanner generates a polygonal mesh or point cloud of the same structure. The method includes detecting a set of points on the 3D objects by identifying distinct anatomical landmarks, such as facial landmarks for soft tissue or tooth landmarks for hard tissue, and assigning each point a unique identifier. A video feed from XR goggles or a prerecorded video is obtained, providing real-time or prerecorded imagery of the patient's anatomical structure from various angles. Corresponding points on the video feed that align with the set of points detected on the 3D objects are identified using image processing techniques, ensuring accurate correspondence. The registered 3D objects are rendered to match the corresponding points on the patient's anatomical structure, with the alignment switching between soft tissue points when the patient's mouth is closed and hard tissue points when the patient's mouth is open for enhanced precision.

The invention provides a vascular calcification analysis method and device based on a non-enhanced CT image, and relates to the technical field of artificial intelligence. The method comprises the following steps: analyzing scanning parameter information of a non-contrast enhanced scanning image set; standardizing the non-contrast enhanced scanning image set based on the scanning parameter information to obtain a preprocessed image sequence; inputting the preprocessed image sequence into a multi-modal registration network, and generating a fused image based on the obtained key anatomical mark points; inputting the fused image into an attention-enhanced segmentation network, and determining blood vessel segmentation masks of the thoracic aorta, the common carotid artery and the intracranial artery; a candidate voxel set is extracted from the fused image based on the blood vessel segmentation mask, three-dimensional connected domain analysis is carried out on the candidate voxel set, and a blood vessel calcification three-dimensional area is determined; and inputting the vascular calcification three-dimensional region into the multi-task prediction model, determining a quantitative score of the vascular calcification degree, and improving the accuracy and reliability of a vascular calcification evaluation result.

The invention relates to the technical field of human body health assessment, in particular to a health assessment method and system based on a 3D human body posture image model. The method comprises the following steps: acquiring human body annular scanning data; extracting a dynamic posture sequence of a vertical position, a forward bending position and a lateral bending position of the human body annular scanning data, and performing phase shift fringe projection on the human body annular scanning data according to the dynamic posture sequence to obtain human body three-dimensional point cloud data; a sagittal plane, a coronal plane and a cross section of the human body three-dimensional point cloud data are analyzed for orthogonal projection, and a human body multi-angle projection image group is generated; and identifying skeleton contour features of the human body multi-angle projection image group so as to establish a dynamic marking system of the anatomical mark points. Through high-precision dynamic acquisition and multi-dimensional space analysis, precision, standardization and individuation of 3D human body posture health assessment are realized, and the accuracy and comprehensiveness of human body posture health assessment are improved.

The invention provides an electronic clinical medical assessment method and system for an ophthalmology diagnosis and treatment scheme, and relates to the technical field of medical information, and the method comprises the steps: 1, carrying out the multi-scale edge detection calculation of an input ophthalmology image-text mixed medical record image, and obtaining a medical record image; the method comprises the following steps: extracting a boundary contour of a text labeling area and a hand-drawn lesion schematic diagram through adaptive threshold segmentation and morphological closed operation, and establishing a coordinate mapping table containing text block circumscribed rectangular coordinates, geometric positions of key anatomical mark points of the schematic diagram and symbol spacing characteristics; 2, constructing a two-channel attention gating network based on the coordinate mapping table, fusing the semantic features of the text region and the morphological features of the schematic diagram through a dynamic weight distribution strategy, and generating a multi-modal fusion feature matrix with a spatial alignment relationship; according to the method, through multi-modal feature fusion, triangular verification region confidence regulation and control and three-dimensional topology modeling, analysis and structured output of ophthalmology image-text medical records are realized, and the spatial alignment of diagnosis and treatment information is improved.

Systems and methods for ascertaining a position of an orthopedic element in space comprising: capturing a first and second images of an orthopedic element in different reference frames using a radiographic imaging technique, detecting spatial data defining anatomical landmarks on or in the orthopedic element using a deep learning network, applying a mask to the orthopedic element defined by an anatomical landmark, projecting the spatial data from the first image and the second image to define volume data, applying the deep learning network to the volume data to generate a reconstructed three-dimensional model of the orthopedic element; and mapping the three-dimensional model of the orthopedic element to the spatial data to determine the position of the three-dimensional model of the orthopedic element in three-dimensional space.

A hip joint navigation system is provided that includes a base having at least one channel disposed therethrough for receiving a pin for mounting the base to the pelvis. A mount feature is disposed on a top surface. A registration jig is configured to couple with the base and to engage anatomical landmarks. In some aspects, a patient specific jig system for hip replacement is provided including an engagement surface formed to closely mate to acetabular bone contours of a specific patient and a registration feature configured to be in a pre-determined orientation relative to an acetabulum the patient when the jig is coupled with acetabular bone contours of the specific patient. In other aspects, methods of using the systems are provided.

The invention provides a control method of an intestinal tumor resection robot combining preoperative images and intraoperative navigation, and relates to the technical field of medical instrument control. The method comprises the following steps: acquiring preoperative multi-modal image data of the intestinal tract of a patient, and performing three-dimensional reconstruction on the image data to generate an initial three-dimensional model; dividing a navigation area in the enteric cavity according to the initial three-dimensional model; based on the core area, collecting a dynamic image of the intestinal tract inner cavity in real time through intraoperative navigation equipment, extracting anatomical mark points in the dynamic image, registering the dynamic image with the initial three-dimensional model, updating tissue deformation parameters in the three-dimensional model, and obtaining an updated three-dimensional model; on the basis of the updated three-dimensional model, obstacles in the intestinal tract are recognized, and the minimum space distance between the obstacles and the robot tail end instrument is calculated; and according to the spatial distribution and the minimum spatial distance of the obstacles, an initial motion path of the robot tail end instrument is planned. According to the invention, the efficiency and accuracy of the intestinal tumor resection operation can be improved.

Systems and methods for generating patient-specific surgical guides comprising: capturing a first and second images of an orthopedic element in different reference frames using a radiographic imaging technique, detecting spatial data defining anatomical landmarks on or in the orthopedic element using a neural network, applying a mask to the orthopedic element defined by an anatomical landmark, projecting the spatial data from the first image and the second image to define volume data, applying the neural network to the volume data to generate a reconstructed three-dimensional (“3D”) model of the orthopedic element; and calculating dimensions for a patient-specific surgical guide configured to abut the orthopedic element.

The invention belongs to the technical field of acupuncture and moxibustion, and provides an acupuncture point recognition method, system and device for an acupuncture part and a medium, the method comprises the following steps: establishing a body surface anatomical marker coordinate system, and determining body surface coordinates of human body acupuncture points in the body surface anatomical marker coordinate system; determining coordinates of an acupuncture part in the body surface anatomical marker coordinate system; human body acupuncture point body surface coordinates near the acupuncture part coordinates are determined; calculating the distance between the coordinates of the acupuncture part and the body surface coordinates of the acupuncture points of the human body near the acupuncture part; determining an acupuncture point of the acupuncture part according to the distance and a preset acupuncture point weight; by calculating the spatial distance between the recognized coordinates of the acupuncture part and the body surface coordinates of the human body acupuncture points near the body surface of the patient and weighting the scores of the corresponding acupuncture points according to the acupuncture point use frequency, the acupuncture points of the acupuncture part are determined according to the scores, clinical experience factors are added for acupuncture point recognition, and the recognition accuracy is improved.

Systems / techniques that facilitate explainable confidence estimation for landmark localization are provided. In various embodiments, a system can access a three-dimensional voxel array captured by a medical imaging scanner and can localize, via execution of a first deep learning neural network, a set of anatomical landmarks depicted in the three-dimensional voxel array. In various aspects, the system can generate a multi-tiered confidence score collection based on the set of anatomical landmarks and based on a training dataset on which the first deep learning neural network was trained. In various instances, the system can, in response to one or more confidence scores from the multi-tiered confidence score collection failing to satisfy a threshold, generate, via execution of a second deep learning neural network, a classification label that indicates an explanatory factor for why the one or more confidence scores failed to satisfy the threshold.

The invention discloses a risk assessment system and method for analyzing abnormal sleep breathing of children based on CBCT dual-channel, and the method comprises the steps: collecting local three-dimensional image data of a maxillofacial region through CBCT, and extracting the sagittal area, volume and multi-dimensional angle and distance parameters of an upper airway through head correction and anatomical mark positioning; and constructing a logistic regression clinical prediction model combining single-factor and multi-factor logistic regression to realize risk prediction. Standardization and equal-interval sampling are synchronously carried out on image data, a multi-channel three-dimensional image data cube is generated, and deep learning classification is realized by inputting the multi-channel three-dimensional image data cube into a 3D ResNet network based on identical fast connection. And finally, performing weighted collaborative analysis on results of the two diagnosis channels, and outputting children obstructive sleep apnea risk classification. Through the parameter driving and image learning dual-channel fusion design, the accuracy and applicability of early screening and risk assessment of children obstructive sleep apnea are improved.

The invention relates to a body position auxiliary detection method and system based on DR equipment. The method comprises the following steps: performing coordinate alignment on pressure distribution data and three-dimensional depth information of a patient to generate an initial body position three-dimensional point cloud model; matching a corresponding reference skeleton-muscle model based on the registration information, generating a registered body position three-dimensional model through non-rigid registration, and extracting coordinates of key anatomical mark points; a target structure theoretical projection area is calculated by combining a DR equipment C arm projection angle, an angle deviation vector is acquired by comparing an actual projection boundary, and a dynamic correction mark is generated. According to the method, through space coordinate alignment, personalized model registration and real-time projection angle deviation correction, the accuracy and efficiency of body position placement are remarkably improved, radiation exposure caused by repeated shooting is reduced, and therefore the medical image quality and diagnosis reliability are remarkably improved.

A method comprises obtaining ear modeling data representing a 3-dimensional (3D) impression of an ear surface of an ear of a patient; determining, based on the ear modeling data, values of landmarks of the ear, wherein the landmarks include ear canal landmarks of an ear canal, and determining the values of the landmarks comprises: predicting an ear aperture plane of an aperture of the ear; determining a plurality of cross-sectional planes that are aligned with the ear aperture plane; for each of the cross-sectional planes: determining an intersection boundary of the cross-sectional plane representing a line of intersection between the cross-sectional plane and the ear canal; and determining a centroid of the intersection boundary of the cross-sectional plane; and determining values of the ear canal landmarks based on the centroids.

Systems and methods for generating patient-specific surgical guides comprising: capturing a first and second images of an orthopedic element in different reference frames using a radiographic imaging technique, detecting spatial data defining anatomical landmarks on or in the orthopedic element using a neural network, applying a mask to the orthopedic element defined by an anatomical landmark, projecting the spatial data from the first image and the second image to define volume data, applying the neural network to the volume data to generate a reconstructed three-dimensional (“3D”) model of the orthopedic element; and calculating dimensions for a patient-specific surgical guide configured to abut the orthopedic element.

Systems and methods for fully automated anatomical analysis of an anatomical structure are provided to facilitate pre-operative planning. The computerized method may include obtaining a plurality of images, e.g., MSCT images, of patient-specific cardiovascular anatomy, and analyzing the MSCT images with a trained artificial intelligence module to identify one or more anatomical landmarks and to construct a virtual three-dimensional model of the anatomical structure. For example, the trained artificial intelligence module may execute segmentation, point detection, curve detection, or plane detection deep learning modules, independently or in combination, to identify the anatomical landmarks. The method further may include deriving anatomical measurements of the one or more identified anatomical landmarks, and displaying the virtual three-dimensional model alongside the anatomical measurements of the one or more identified anatomical landmarks.

The invention provides a gastroscope image recognition and auxiliary diagnosis system and method based on deep learning and large language model fusion, and the system comprises a data collection and processing module which is used for obtaining and preprocessing various types of gastroscope images; the gastroscope picture recognition and classification module is used for inputting the preprocessed gastroscope pictures into a GastroHAF model for training and outputting recognition and classification results of the gastroscope pictures, and the GastroHAF model comprises a weak supervision anatomical mark positioning sub-module, a space co-occurrence context reasoning sub-module and an anatomical atlas guided hierarchical classification sub-module; and the auxiliary diagnosis module is used for outputting stomach diagnosis results and examination suggestions based on the fine-tuned Chinese medical big language model. The GastroHAF model provided by the invention can quickly, effectively and accurately judge the position of the gastroscope in combination with prior anatomical knowledge, and can effectively assist doctors in stomach examination in combination with a large language model.

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.

The invention relates to the technical field of artificial intelligence, in particular to an acupuncture point visual detection and positioning method and system, and the method comprises the steps: synchronously collecting multi-view three-dimensional image data and flexible multi-parameter deformation data of a target area, and the multi-view three-dimensional image data comprises body surface textures and dynamic tracks of anatomical mark points; according to the method, multi-view three-dimensional image data and flexible multi-parameter deformation data of a target area are synchronously collected, an individual deformation feature model is constructed through mark point track purification processing, then a comprehensive feature set containing real-time state and trend prediction is generated through time sequence multi-mode fusion, and after initial acupuncture point coordinates are output through an AI basic matching model, the multi-view three-dimensional image data and flexible multi-parameter deformation data of the target area are obtained. Point-by-point correction is carried out by combining an AI self-adaptive correction model with a differential deformation compensation coefficient, irregular offset and predicted offset, and model parameters are dynamically adjusted through real-time feedback calibration, so that the problem that in the prior art, only static body shape proportional positioning is relied on, and dynamic deformation of soft tissue is not considered, so that acupuncture point offset is caused is effectively solved.

Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical landmark detection algorithm leveraging Deep Reinforcement Learning (DRL) techniques to automatically locate one or more anatomical landmarks associated with the patient specific anatomical feature within the medical images. A 3D surface mesh model may be generated representing the patient specific anatomical features including the located one or more anatomical landmarks. The located one or more anatomical landmarks may be used to guide placement of a 3D model of a medical device that may be fused with the 3D surface mesh model to generate a patient specific 3D model of the medical device.

There is provided a method for designing an auditory device comprising collecting ear shape data relating to an ear geometry, the ear shape data comprising a plurality of data sets associated with a respective plurality of subjects, identifying, based on the ear shape data, one or more anatomical landmarks of the ear geometry, determining, based on the one or more landmarks, at least one region of interest of the ear geometry, selecting cross- sections of the at least one region of interest based on a minimum area criterion, aligning the plurality of datasets based on the cross-sections, generating, based on the aligned datasets, one or more statistical shapes of the ear geometry, and outputting a design of the auditory device based on the one or more statistical shapes.

Various embodiments include methods for generating patient-specific heart and thorax models using anatomical landmarks and segmentation data, optimized through the application of trained neural network models. Three-dimensional medical imaging data may be processed by a trained neural network to automatically segment and isolate the heart, blood cavities, and thorax to extract feature maps from the segmented images. Anatomical landmarks, such as the heart apex and valve centers, are identified and the alignment of heart axes is verified. Reference heart and thorax models are selected and adapted to fit the patient-specific landmarks through scaling, translating, and rotating. Best adapted heart and thorax models may then be used for conducting one or more medical procedures. Neural network models, trained on historical patient data sets, may be refined through machine learning from new patient data, thereby improving accuracy.

Surgical systems and methods involve a first and second trackers with trackable elements and which are independently secured to a bone and separated by a distance. A localizer tracks the trackable elements. Controller(s) define a tracking arrangement based on a combination of the trackable elements from the trackers. The controller(s) register a geometry of the tracking arrangement relative to the bone and track the bone by detection of the registered geometry. The controller(s) detect a condition wherein at least one trackable element has been displaced relative to the registered geometry. The controller(s) register a position of an anatomical landmark of the bone and use the landmark to identify the at least one trackable element that has been displaced.

The invention belongs to the technical field of data processing, and particularly relates to a dental operation scheme generation method and device, a computer device and a storage device. The method comprises the following steps that at least two kinds of three-dimensional image data of the same patient are obtained, the first kind of three-dimensional image data at least comprises hard tissue information, and the second kind of three-dimensional image data at least comprises soft tissue surface information; performing spatial registration on the first three-dimensional image data and the second three-dimensional image data in the same world coordinate system to generate a unified three-dimensional fusion model; identifying a key anatomical marker in the three-dimensional fusion model; and generating a dental operation scheme in the three-dimensional fusion model according to the identified key anatomical markers. A dental operation scheme is intelligently generated through four steps of data, registration, recognition and generation.

The invention provides a human body acupoint searching and positioning method based on a digital traditional Chinese medicine large-size model, which comprises the following steps of: establishing a standard meridian template by taking national standard meridian and acupoint positioning as a reference, and mapping the template to a parameterized three-dimensional human body grid to form a standard digital traditional Chinese medicine large-size model; the method comprises the following steps: collecting body surface point cloud data of an acupuncture part of a patient, carrying out denoising, hole filling and down-sampling preprocessing on the point cloud data, and then carrying out non-rigid registration on the point cloud data and a standard digital traditional Chinese medicine human big model to obtain an individualized meridian-acupoint model consistent with the current posture and body type of the patient; marking anatomical mark points in the individualized meridian-acupoint model as reference feature points, and establishing a relative position function relationship between the reference feature points and a target acupoint according to a traditional Chinese medicine bone degree division method; a real-time image of the body surface of the patient is obtained through the visual sensing unit, the actual pixel coordinates of the reference feature points are recognized, and the actual three-dimensional coordinates of the target acupuncture point on the body surface of the patient are calculated and output in combination with the relative position function relation.

Some embodiments provide systems, assemblies, and methods of analyzing patient anatomy, including providing an analysis of a patient's spine, and also analyzing the biomechanical effects of implants. In some embodiments, the systems, assemblies, and / or methods can include obtaining initial patient data, acquiring spinal alignment and contour information, acquiring flexibility and / or biomechanical information, registering patient anatomical landmarks of interest relative to fiducial markers, analyzing databases of measurements and patient data to predict postoperative patient outcomes. Further, in some embodiments, the systems, assemblies, and / or methods can assess localized anatomical features of the patient, and obtain anatomical region data. In some embodiments, the systems, assemblies, and / or methods can also analyze the localized anatomy and therapeutic device location and contouring. Further, the systems, assemblies, and / or methods can output localized anatomical analyses and therapeutic device contouring data and / or imagery on a display according to some embodiments.

To provide systems and methods for automatic image alignment of three-dimensional (3D) medical image volumes.SOLUTION: The methods include: pre-processing the 3D medical image volume by selecting a sub-volume of interest; detecting anatomical landmarks in the sub-volume using a deep neural network; estimating transformation parameters based on the anatomical landmarks to adjust rotation angles and translation of the sub-volume; adjusting the rotation angles and translation to produce a first aligned sub-volume; determining confidence in the transformation parameters based on the first aligned sub-volume; and iteratively refining the transformation parameters if the confidence is below a predetermined threshold. The disclosed methods reduce the need for manual alignment and increases a probability of the 3D image volume converging to a desired orientation compared to conventional approaches.SELECTED DRAWING: Figure 1

The invention relates to the technical field of intelligent medical treatment, and provides a nasointestinal tube ultrasonic recognition and positioning method and system based on artificial intelligence, and the method comprises the steps: enabling an ultrasonic scanning probe to synchronously scan the body part of a patient in the process of placing the tip of the nasointestinal tube into the body of the patient, and obtaining an ultrasonic image used for observing the position of the nasointestinal tube in the body of the patient; performing artifact removal processing on the ultrasonic image to obtain a clear image; extracting a nasointestinal tube image according to the clear image; detecting whether an anatomical marker image exists in the clear image with the nasointestinal tube image or not based on an attention mechanism, and when the anatomical marker image exists, performing edge segmentation on the anatomical marker image and extracting the anatomical marker image; and matching the extracted anatomical marker image with a preset anatomical marker image of an organ or tissue, and when matching succeeds, outputting the corresponding organ or tissue as the position of the nasointestinal tube. According to the invention, the nasointestinal tube can be quickly and accurately positioned in the body.

A system for performing a surgical procedure includes a catheter including a camera and an electromagnetic sensor and a workstation operably coupled to the catheter, the workstation including a memory storing instructions, which when executed cause a processor to receive pre-procedure images of a patient's anatomy, generate a 3D representation of the patient's anatomy, identify first anatomical landmarks within the generated 3D representation, identify a location of the EM sensor within a reference coordinate frame, receive real-time images from the camera, identify second anatomical landmarks within the received real-time images corresponding to the identified first anatomical landmarks, identify a location of the camera within the reference coordinate frame using the identified second anatomical landmarks corresponding to the identified first anatomical landmarks, and register a location of the catheter to the 3D representation using the identified locations of the EM sensor and the camera within the reference coordinate frame.

A hip joint navigation system is provided that includes a base having at least one channel disposed therethrough for receiving a pin for mounting the base to the pelvis. A mount feature is disposed on a top surface. A registration jig is configured to couple with the base and to engage anatomical landmarks. In some aspects, a patient specific jig system for hip replacement is provided including an engagement surface formed to closely mate to acetabular bone contours of a specific patient and a registration feature configured to be in a pre-determined orientation relative to an acetabulum the patient when the jig is coupled with acetabular bone contours of the specific patient. In other aspects, methods of using the systems are provided.

Systems and methods for anatomical identification using ultrasonic imaging and acoustic shadow detection methods are provided. At least some embodiments of the disclosure comprise the following steps: acquiring ultrasound image; detecting shadow region; extracting shadow profile; filtering shadow profile with matched filter; identifying anatomical landmarks within shadow; extracting features of anatomical landmarks; classifying anatomy, and determining with a high degree of confidence that the target anatomy is depicted in the image. A determination is made as to the degree of confidence that the target anatomy is depicted in the image. Conditionally, graphics indicating presence and position of target anatomy is displayed including disposition, location and orientation thereof.