159 results about "Anatomical landmark" patented technology

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 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 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.

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.

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.

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 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.

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.

A medical system, ablation device, and method for delivering PF A treatment to patients. In one example, the ablation device includes a jacket configured to harness around a heart. The jacket includes a set of anatomical anchors configured to mate with respective anatomical landmarks of the heart for reduced error, and in some cases, substantially error-proof positioning of the jacket on the heart. The ablation device also includes first electrodes attached to the jacket and arranged in a pattern corresponding to a lesion set to be formed in the heart by passing pulsed energy through selected sets of the first electrodes. In some examples, the pattern corresponds to a Cox-maze IV lesion set. In some examples, the ablation device also includes second electrodes attached to the jacket and configured for collecting electrogram signals from the heart or for confirming an exit or entrance block in an isolated area of the heart.

The invention requests to protect an unsupervised anatomical mark point accurate positioning method based on double-layer alignment and a super-resolution head, and aims to solve the problem of domain migration caused by differences of different clinical center imaging devices. Comprising the following main steps: S1, constructing an input layer alignment module, generating a target style image by adopting adaptive instance normalization, and meanwhile, retaining an original spatial anatomical structure; s2, constructing an output layer alignment module, adopting a mean value teacher framework, and guiding student model learning through a high-quality pseudo tag and a self-adaptive mark point shielding strategy; and S3, introducing a lightweight super-resolution head, fusing the multi-scale features extracted by the backbone network, and generating a high-resolution heat map by adopting pixel rearrangement. Compared with an existing method, the method has the following advantages that (1) the cross-domain adaptive capacity is effectively improved through input-output double-layer alignment; (2) the super-resolution head can directly decode the coordinates without tedious post-processing, so that the quantization error and the calculation complexity are reduced;

A method for using anatomical landmarks of a balance organ to construct a three-dimensional cephalometric coordinate system includes acquiring a CT image of a craniofacial region to be measured. The anatomical landmarks of the CT image are determined. On the basis thereof, a horizontal plane, and then a coronal plane and a median sagittal-plane plane are determined. The horizontal plane is a plane formed by certain of the anatomical landmarks. The coronal plane is determined using the horizontal plane and anatomical landmarks, and the median sagittal plane is determined using an anatomical landmark and both the horizontal and coronal. An intersection point of the three planes is used as the origin of a three-dimensional cephalometric coordinate system.

An ultrasound imaging system (110) may include a transducer configured to transmit and receive an ultrasound signal; a matching layer configured to have an acoustic impedance between the tissue to be imaged and the material of the transducer; a damping block configured to absorb ultrasonic energy; and a processing circuit (316). A processing circuit (316) may acquire medical imaging data of an anatomical feature of a subject. A processing circuit (316) may determine a position of an anatomical structure relative to an anatomical feature of a subject. A processing circuit (316) may generate a four-dimensional (4D) model of an anatomical feature of a subject, the four-dimensional (4D) model including a visual indicator identifying a location of an anatomical structure relative to the anatomical feature of the subject. The processing circuitry (316) may display the 4D model via the user interface.

The invention provides a method for determining a global confidence index for a 2D ultrasound image extracted from a 3D ultrasound volume, wherein the global confidence index indicates the suitability of the 2D ultrasound image for medical measurements. The method comprises obtaining a 3D ultrasound volume of a subject and extracting a set of at least one 2D ultrasound image from the 3D ultrasound volume. A set of geometrical indicators is obtained with a previously trained first neural network, and a set of 2D ultrasound images is processed with a second neural network, wherein the output of the second neural network is a set of anatomical indicators and wherein the anatomical indicators indicate at least the presence of anatomical landmarks. A global confidence index is then determined for each one of the set of 2D ultrasound images based on the geometrical indicators and the anatomical indicators.

A non-invasive device for estimating the Cobb angle, consisting of an adjustable frame with telescopic internal rods, contact needles with length-adjustable screws for orientation to dorsal anatomical landmarks, integrated protractors for measuring relative arm angles and an ergonomic handle, the device enabling radiation-free angle measurement that provides an indication of the extent of spinal curvature.

A method and a system for navigation and / or control support for transurethral prostate resection, the method including: analyzing endoscopic images provided by a resectoscope and / or additional sensory data from within the resectoscope during TURP for one or more of several options, and at least one of: automatically disabling HF current to the resection electrode under several different outcomes of the analyzing, and outputting visual and / or acoustic information to an operator performing the TURP. The information including at least one of: a visual indication of an anatomical landmark inside and / or outside of the endoscopic image, a visual indication of a distance of the resection electrode to an anatomical landmark, and a visual or acoustic warning signal when the resection electrode comes closer than a predetermined allowable distance to one or more of the bladder neck, the verumontanum and the internal and / or external sphincter muscles.

The application provides a depth acupoint acupuncture navigation system based on CT-vision fusion, and relates to the technical field of medical devices. The operation steps of the system include: obtaining head CT scan data and head and face image data of a patient; constructing a body surface reference coordinate system based on the body surface anatomical landmark points in the head and face image data; combining the head and face image data to reconstruct the patient's body surface and determine a body surface three-dimensional model; mapping the body surface reference coordinate system to the body surface three-dimensional model; generating an internal anatomical three-dimensional model containing the spatial position of a target deep acupoint by three-dimensional reconstruction of the head CT scan data; performing spatial registration processing on the body surface three-dimensional model and the internal anatomical three-dimensional model to determine needle insertion space parameters; determining a needle insertion path based on the needle insertion space parameters; combining key anatomical structure information to perform safety constraint analysis on the needle insertion path and determine a needle insertion angle and a needle insertion depth that meet safety conditions.

A computer-implemented method for determining a position of an X-ray emitter of a mobile X-ray device to obtain high-quality mobile X-ray images is provided, the method comprising the steps of receiving a camera-acquired image resulting from a camera monitoring a patient during an X-ray imaging session, a reference X-ray image of an internal body structure to be examined in the X-ray examination, and X-ray detector position information, detecting and locating a plurality of anatomical landmarks in the camera-acquired image, determining a distance from the X-ray emitter to the patient based on the camera-acquired image, generating a virtual X-ray image of the internal body structure based on the detected plurality of anatomical landmarks, the distance from the X-ray emitter to the patient, and the X-ray detector position information, registering the virtual X-ray image of the internal body structure to the reference X-ray image of the internal body structure, and determining at least one parameter for adjusting the position of the X-ray emitter based on the registration result.

The invention provides a shoulder joint semi-dislocation image data processing method and device and a medium. According to the method, medical image data of a first area of a shoulder joint of a to-be-detected object is obtained, then motion artifact detection processing is performed on the medical image data of the first area, motion artifact level information of the medical image data is obtained, and under the condition that the motion artifact level information indicates that the medical image data meets a preset quality condition, motion artifact detection is performed on the shoulder joint of the to-be-detected object. And performing shoulder joint anatomical mark detection and distance measurement processing on the medical image data to obtain a first distance parameter between the acromion and the humeral head and / or a second distance parameter between the acromion and the humeral greater tubercle, and finally, determining a shoulder joint subluxation evaluation result of the to-be-detected object according to the first distance parameter and / or the second distance parameter. Therefore, accurate and repeatable quantitative measurement of the distance between the acromion and the humeral head and the distance between the acromion and the humeral greater tubercle can still be realized in a high-exercise-risk population, namely a stroke patient.

This invention relates to the field of digestive endoscopy technology, and discloses an adaptive path planning control system and method for digestive endoscopy probes. The system includes an endoscopy probe and a handle. The handle contains an environment sensing unit, a pose estimation unit, a path planning unit, a motion control unit, and an actuator. The environment sensing unit extracts the cavity center, the probe-mucosa distance, and anatomical landmarks. The pose estimation unit calculates the probe's three-dimensional pose in real time. The path planning unit generates an adaptive motion trajectory based on the state vector. The motion control unit converts these into pulse commands to drive the actuator, which in turn drives the probe to bend or push via a wire traction mechanism. This invention achieves adaptive obstacle avoidance path planning and safety control of the endoscopy probe, reduces operational difficulty, shortens the learning cycle, effectively avoids mucosal damage, and improves the safety and efficiency of digestive endoscopy.

A needle thoracostomy device with a venting device, a deployment device, and a positioning device. The venting device is configured to vent a pleural cavity of a patient. The deployment device is removably attached to the venting device and is configured to perform a needle thoracostomy to attach the venting device to the patient. The deployment device has a release mechanism which, when moved to a released position, allows the deployment device to be separated and lifted away from the venting device. The positioning device is configured to removably engage with at least one of the deployment device and the venting device to correctly position the deployment device and the venting device on the patient for the needle thoracostomy. The positioning device may position the deployment device and the venting device based on anatomical landmarks.