213 results about "Anatomical landmark" patented technology

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

In another embodiment, a hip joint navigation jig is provided that includes an anatomical interface comprising a bone engagement portion. A registration jig is also provided that is coupled, e.g., removeably, with the anatomical interface. A rotatable member is provided for rotation about an axis that is not vertical when the jig is mounted to the bone adjacent to a hip joint and the registration jig is coupled with the anatomical interface. An anatomy engaging probe is coupled with the rotatable member for rotation about the axis and is translatable to enable the probe to be brought into contact with a plurality of anatomical landmarks during a procedure. An inertial sensor is coupled with the probe to indicate orientation related to the landmarks, the sensor being disposed in a different orientation relative to horizontal when the probe is in contact with the landmarks.

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 relates to the technical field of data processing, and discloses a tooth three-dimensional model parameterization description method, which comprises the following steps: carrying out standardization processing on local point cloud data of teeth to obtain three-dimensional model data of the teeth; meshing the three-dimensional model data to obtain triangular mesh model data of the teeth; integrating a dental crown edge, an occlusal surface and a root boundary region in the triangular mesh model to obtain a key feature region of the tooth; according to curvature change characteristics in the key feature region, identifying anatomical mark points of the teeth, and removing noise points in the anatomical mark points to obtain a feature point set of the teeth; mapping the feature point set to a two-dimensional parameter space, generating a continuous parameterized surface through radial basis function interpolation, and performing geometric reconstruction on the parameterized surface to obtain a parameterized description model of the tooth; outputting the parameterized description model as parameterized description of the teeth; according to the method, the parameterized description efficiency of the tooth three-dimensional model can be improved.

A coordinate locating device comprising a support structure removably connectable to a wearable sensor device being worn by an individual. The support structure comprising a planar surface. The device includes a plurality of different fiducial marker components connected to the planar surface. The plurality of different fiducial marker components includes a set of fiducial markers connected to the planar surface in a non-collinear configuration relative to each other to define a three-dimensional (3D) space of pixels in an image. The plurality of different fiducial marker components includes a distance calibration fiducial marker connected to the planar surface and being configured to define a distance calibration length of pixels in the image, the distance calibration fiducial marker being perpendicular to the planar surface and defining a calibration length to locate a point of origin of motion sensing by the wearable sensor device. A system and method are also provided.

This invention provides a non-contact method, device, equipment, and storage medium for measuring trunk respiratory parameters. It synchronously acquires three-dimensional motion data of optical reflective markers during respiration, with these markers positioned at multiple pre-defined anatomical landmark locations on the subject's trunk. Based on the three-dimensional motion data, it determines the volume change of the spatial envelope formed by the optical reflective markers. According to a pre-established mapping relationship between the volume change and tidal volume, it converts the volume change into tidal volume. Based on the signal of the volume change over time, it extracts the respiratory rate. Finally, it calculates the total respiratory volume based on the tidal volume and the respiratory rate. By combining optical motion capture with spatial geometric modeling, it achieves non-contact, continuous, and high-precision measurement of trunk respiratory motion, effectively overcoming the shortcomings of traditional methods, such as reliance on patient cooperation, invasiveness, expensive equipment, and complex operation.

A ligament retractor system may include a ligament retractor, one or more sensors configured to identify a location of one or more anatomical landmarks or instruments, and a processing unit configured to receive and process data from the one or more sensors.

Training framework for creating an artificial intelligence (AI) system for estimating a position of an anatomical landmark by text inputs. The training framework includes providing a context-set comprising a list of names of anatomical landmarks, and a position-list comprising position-tokens being expressions referring to relative positions. A plurality of question-prompts asking for the relative position of a landmark are generated by using varying combinations of the landmarks and position-tokens of the context-set and the position-list. A number of target-landmarks to each question-prompt are generated by inputting the question-prompts in a large language model. The answer is parsed for landmarks and the found landmarks are defined as target-landmarks. A plurality of training-datasets are formed, wherein each training-dataset comprises the landmark, the position-token from a question-prompt and the target-landmark from the answer to this question-prompt. The AI-system is trained with the training-dataset and additional spatial coordinates of a part of the landmarks of the context-set.

The application relates to the field of traditional Chinese medicine diagnosis and treatment, and discloses a disease acupoint rash image analysis system based on a body surface gridding coordinate system. The system comprises a system data acquisition module, a body surface gridding coordinate system construction module, a rash image feature extraction and analysis module, a dynamic pathological correlation analysis module and a dynamic risk early warning module. The back body surface image, physiological signals, clinical indexes and environmental data of a patient are collected. Then, the image data is used to calibrate anatomical mark points, and grid unit codes are stored by division. The gridded body surface data is subjected to multi-dimensional color space conversion and cross-modal feature fusion to obtain quantitative rash image feature data. The rash image dynamic deviation feature set is obtained by matching a preset database according to the rash image feature data, physiological signals, clinical index data and current environmental data, the network is constructed to predict disease conversion probability and analyze acupoint correlation. Finally, the visual analysis data is obtained, a heat map and ranking are generated, and early warning information is output according to the comparison result, thereby providing strong support for traditional Chinese medicine diagnosis and treatment.

The present invention relates to a method for using a fiducial marker in a surgical navigation system, comprising: a computed tomography (CT) process step of obtaining a CT image through CT scanning of a patient; a CT image-based 3D reconstruction step of generating, on the basis of the CT image, a CT 3D model by using 3D reconstruction technology; a fluorescence process step of generating a fluorescent image by capturing, using a multi-camera, an anatomical landmark in which an anatomical structure near a surgical target of the patient is highlighted with a fluorescent contrast agent; a multi-camera-based 3D reconstruction step of generating a fluorescent 3D model from the fluorescent image by using the 3D reconstruction technology; and a registration step of registering the CT 3D model and the fluorescent 3D model into one model.

This invention provides a method for determining a global confidence index for 2D ultrasound images extracted from a 3D ultrasound volume, wherein the global confidence index indicates the suitability of the 2D ultrasound images for medical measurements. The method includes obtaining a 3D ultrasound volume of an object and extracting a set of at least one 2D ultrasound image from the 3D ultrasound volume. Then, a set of geometric indicators is obtained using a first neural network, wherein each geometric indicator indicates a geometric feature of an anatomical structure of the object. The set of 2D ultrasound images is then processed using a second neural network, wherein the output of the second neural network is a set of anatomical indicators, and wherein the anatomical indicators at least indicate the presence of anatomical landmarks. A global confidence index is determined for each 2D ultrasound image in the set of 2D ultrasound images based on the geometric indicators and the anatomical indicators.

A computer-implemented method of automatically identifying a reference vessel in an ultrasound image, the method comprising: segmenting the image to identify an organ; locating a predetermined anatomical landmark specific to the organ; identifying appropriate vasculature for the organ; and selecting a reference vessel from the vasculature that has a size within a predetermined size range and a location with a predetermined distance range from the predetermined interface.

A method for assessing a patient's brain disease state from brain images includes acquiring the brain images, partitioning them into predefined regions based on anatomical landmarks, extracting disease-indicative features, using a pretrained model to generate a disease-associated biomarker from the features, and determining the patient's brain disease state based on the biomarker.

The application provides a laparoscopic surgery stage automatic identification method and system based on deep learning, and the method specifically comprises the following steps: defining the start and end points and key anatomical landmarks of each stage of laparoscopic surgery; collecting continuous video data in the surgery process through a laparoscopic imaging system, wherein the video data completely covers the whole surgery process; pre-processing the collected video data; constructing a double-branch deep neural network model, fusing short-time scale features and long-time scale features in a multi-time scale feature fusion module to generate joint feature representation, which simultaneously reflects local operation state and overall process semantics; and identifying each surgery stage by using the trained double-branch deep neural network model. The technical scheme of the application overcomes the problems of insufficient surgery stage identification granularity, insufficient long-range context utilization, and insufficient real-time performance, robustness and cross-patient generalization capability in clinical application in the prior art.

The application discloses an oval foramen automatic detection and fine segmentation method based on an anatomic landmark gravity field and a tubular topology enhancement, and specifically relates to the technical field of medical image processing. Firstly, the head CT or MRI image to be processed is subjected to multi-modal space alignment and adaptive contrast enhancement; subsequently, the image is input into an anatomic landmark gravity field network, macroscopic landmarks of a skull base are extracted, a spatial gravity field matrix is constructed, and the model attention is adaptively focused on a small oval foramen high probability area; then, a multi-view tubular structure enhancement encoder is used to extract features of a three-dimensional hole track of the oval foramen, and the interference of non-penetrating bone depressions is inhibited; finally, a three-dimensional penetrating topology constraint mechanism is introduced in the decoding stage, so that the generated three-dimensional model of the oval foramen has correct anatomic connectivity. The application can effectively solve the detection problem of the oval foramen caused by the large individual variation, the small volume and the easy confusion with surrounding holes and fissures, and significantly improves the preoperative planning accuracy of trigeminal neuralgia puncture and other surgeries.

Disclosed is a computer-implemented method of establishing a spatial transformation between a two-dimensional digital camera image and a three-dimensional digital model of an anatomical body part. The disclosed method encompasses transformation of an RGB camera image of an anatomical body part of patient taken with a head-mounted device into a point cloud, scaling the point cloud according to a relation of distances between anatomical landmarks in the camera image and / or the point cloud to distances between corresponding anatomical landmarks in a three-dimensional planning image of the anatomical body part, and establishing a spatial transformation between the scaled point cloud and the planning image. This spatial transformation can be used for augmenting an image or real scene viewed by a user of the head-mounted device with the planning image as an overlay on the position of the anatomical body part viewed by the user. Thereby, user guidance is improved.

A method of generating a correction plan for correcting a deformed bone includes inputting to a computer system a first image of the deformed bone in a first plane and inputting to the computer system a second image of the deformed bone in a second plane. Image processing techniques are employed to identify a plurality of anatomical landmarks of the deformed bone in the first image. The first image of the deformed bone is displayed on a display device. A graphical of the deformed bone is autonomously generated and graphically overlaid on the first image of the deformed bone on the display device, the graphical template including a plurality of lines, each line connected at each end to a landmark point corresponding to one of the anatomical landmarks. A model of the deformed bone may be autonomously generated based on the graphical template.