537 results about "Anatomical feature" patented technology

The invention discloses a brain disease classification method and system. Precise diagnosis is realized through multi-modal data fusion and dynamic modeling. The method comprises the following steps: collecting multi-modal brain image information and cognitive behavior information of a user; performing dynamic function connection analysis on the resting state functional magnetic resonance time sequence signal to obtain a time-varying brain network feature matrix, and performing white matter fiber bundle topology reconstruction on a structure connection matrix; constructing a four-dimensional correlation tensor by using the time-varying network features, the structural connection weights and the anatomical features through a neurodynamic model; performing multi-task learning on the four-dimensional correlation tensor based on a time-varying graph neural network model, and outputting a quantitative diagnosis result; and finally generating a clinical classification report integrating the individualized brain network remodeling target, the disease progress risk layering and the treatment response prediction. By dynamically fusing the structure and functional features, comprehensive characterization of the pathological mechanism of the brain disease is realized, and decision support with both accuracy and interpretation is provided for clinical diagnosis.

The fluid collection devices include a fluid impermeable barrier, a fluid permeable body, at least one attachment pad, and one or more support members extend from the at least one attachment pad into the fluid impermeable barrier. The fluid impermeable barrier at least partially defines a chamber, and an opening configured to be positioned adjacent to a urethra. The at least one attachment pad is spaced from the fluid impermeable barrier and the one or more support members extend from the at least one attachment pad into the chamber. The one or more support members maintain the space between the at least one attachment pad and fluid impermeable barrier while maintaining a shape to the fluid collection device that matches the shape of the anatomical features of the user in the pelvic region. Systems and methods using the same are described.

The invention discloses an AI-based neurosurgery auxiliary robot visual positioning system, and relates to the field of visual positioning, which comprises the steps of deploying and initializing structured light scanning equipment and near-infrared imaging equipment, carrying out multi-modal image acquisition on a surgical area, and carrying out standardization processing, space-time alignment and fusion on the acquired image. A multi-modal visual acquisition and preprocessing module, a three-dimensional tissue model construction and registration module, a visual-anatomical feature recognition and extraction module, an AI auxiliary positioning and path optimization module, an intraoperative dynamic perception and feedback control module and a target position confirmation and instruction output module are constructed. According to the method, high-precision identification and dynamic modeling of a brain tissue structure are realized, real-time identification, path planning and position correction can be carried out on a key anatomical structure in an operation process, the precision, the intelligent level and the intra-operation response capability of neurosurgery operation are remarkably improved, the operation risk is effectively reduced, and the positioning reliability and the automatic control efficiency are improved.

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

The invention relates to a reconstruction method of a liver, gall and pancreas three-dimensional image. According to the technical scheme, the method comprises the following steps: introducing a segmentation network for U-Net variants of the liver, the bile duct and the pancreas, and segmenting a multi-modal image by using a convolutional neural network, including separation of the liver, the bile duct and the pancreas; a boundary enhancement module is introduced in the segmentation process; performing spatial alignment on the image data of different modalities by adopting a deep learning driven non-rigid registration algorithm; carrying out registration by utilizing anatomical feature points of the organs; according to a registration result, performing multi-modal fusion on the liver image, the bile image and the pancreas image, and constructing fusion image data by comprehensively utilizing the bone and tissue contrast ratio of CT, the soft tissue resolution ratio of MRI and the real-time dynamic characteristics of ultrasound; extracting contours of liver, gall and pancreas from the segmented image, and constructing a preliminary three-dimensional mesh model; and according to the geometrical shapes of different organs, adaptive adjustment is carried out, and the distribution of grids during reconstruction is controlled to be matched with the organ shapes.

The invention discloses an in-vitro bionic mechanical testing system and method for spinal skeletal muscles, and particularly relates to the field of mechanical testing, and the system comprises a bionic construction module, a finite element modeling module, a multi-axis mechanical loading module, an iterative calculation module and a correction module; through modular design, the system comprises a vertebral body unit, an intervertebral disc simulation assembly and a skeletal muscle simulation unit, each assembly is customized according to anatomical features of a human body, a finite element modeling module is utilized to generate a three-dimensional model, a multi-directional mechanical load is applied, and response data is collected; experimental data are input into the finite element model for iterative calculation, so that stress distribution, a strain field and kinematic deviation are compared; if the error exceeds a preset threshold value, the model parameters and the physical parameters are corrected until the experiment and simulation results converge; and finally, based on the optimized finite element model, predicting a failure mechanism of the spinal skeletal muscle under a complex load, and evaluating the risks of fracture, hairline fracture, nucleus pulposus protrusion and muscle strain.

The invention relates to the field of medical image segmentation, and discloses a precision surgery-oriented robot medical image segmentation and feature extraction method, which comprises the steps of constructing a dynamic segmentation network model of a bidirectional attention architecture, and inputting a preprocessing module for feature extraction and generating a multi-scale feature pyramid; the Transform coding branch is used for time sequence feature modeling and outputting a time sequence enhancement feature; the convolutional coding branch is used for enhancing anatomical features and surgical instrument features and outputting spatial enhancement features; the multi-stage feature fusion unit is used for performing multi-stage iterative fusion and outputting final fusion features; the decoding output module is used for decoding and generating pixel-level segmentation masks of the anatomical structure and the surgical instrument; training the dynamic segmentation network model; and performing medical image segmentation and feature extraction based on a medical image video sequence input in real time by using the trained dynamic segmentation network model. Accurate segmentation of anatomical tissues and dynamic instruments in an operation scene is realized.

An orthopedic tool includes a main body and a reference member. The reference member may be configured to engage an anatomical feature of a patient, such as the patient's bone. The angle of the reference member may be adjusted using a knob.

Interactive viewing of three-dimensional content includes producing anatomical tracking data representative of the movement of an anatomical feature of a user, rendering of three-dimensional target content and manipulation of the target content in response to the anatomical tracking data such that the user perceives his self or her self to be changing location with respect to a scene or object or perceives being tracked by a virtual character. A system of interactive viewing includes a point cloud capture device by which the at least one anatomical feature of the user can be tracked, a display by which target content is displayed to the user, and a computer operatively connected to the point cloud capture device and display. The computer includes non-transitory computer readable media (CRM) storing instructions by which the virtual scene is manipulated based on input of the anatomical tracking data.

The invention relates to the technical field of tumor interventional operations, and discloses a visual navigation method based on a tumor interventional operation robot. The method comprises the following steps: acquiring real-time medical image data of a tumor area containing multi-modal imaging information so as to comprehensively present anatomical details; and performing three-dimensional reconstruction on the image data to generate a tumor area three-dimensional anatomical structure model capable of visually displaying a space structure. Key anatomical feature points are extracted based on the model, space coordinates are calculated, a surgical robot intervention path is planned according to the coordinates, and an initial navigation track is generated; and continuously collecting real-time pose data of the robot in an operation, dynamically matching the real-time pose data with the initial navigation trajectory, adjusting motion parameters according to a matching result, and generating a corrected navigation instruction. The method can reflect the intraoperative anatomy condition in real time, dynamically optimize the path, solve the problems that traditional navigation depends on preoperative static images and lacks real-time adjustment, reduce operative complications and improve the treatment effect of patients.

Systems and methods are provided for monitoring and processing physiological signals (e.g., electrocardiogram signals) to detect and predict for possible dysfunction of an anatomical feature (e.g., cardiac dysfunction) or otherwise predict a likelihood of future cardiac dysfunction of the individual. For example, a system comprises a plurality of sensors, a physiological signal processing system, and a feature analysis system. The sensors are configured to monitor physiological signals from an individual that has undergone a medical procedure on an anatomical feature. The physiological signal processing system is configured to analyze the physiological signals and extract features from the physiological signals which are indicative of a function of the anatomical feature. The feature analysis system is configured to analyze the extracted features and predict a risk of the individual developing a post-procedural dysfunction of the anatomical feature as a result of the medical procedure on the anatomical feature.

The invention relates to a chest pain classification method and system based on multi-modal data fusion and a deep learning model, and belongs to the technical field of intelligent medical auxiliary diagnosis. The method comprises the following steps: firstly, carrying out heart region segmentation and pathological feature enhancement preprocessing on a chest radiograph image, extracting anatomical features by adopting a multi-scale visual model which is optimized and improved aiming at the chest radiograph, meanwhile, carrying out multi-lead time sequence calibration and ST-segment waveform marking on an electrocardiogram signal, and capturing electrophysiological time sequence features through a multi-scale one-dimensional convolutional network; then, a dynamic gating hybrid expert system is constructed, an anatomy expert, an electrophysiology expert, a multi-modal association expert and a critical value expert process specific modal features respectively, and a gating network dynamically calculates expert weights based on real-time vital signs and pathological features; according to the method, by integrating multi-modal data and combining a deep learning technology, rapid and accurate chest pain classification support and report generation can be provided for clinicians, and the method has a wide application prospect.

The invention discloses an edentulous jaw dental implant positioning method combined with depth variation reasoning. The method comprises the following steps: S1, generating optimized medical image data; s2, generating a jaw bone three-dimensional reconstruction model of the edentulous jaw patient; s3, generating anatomical feature data; s4, generating a preliminary implant positioning scheme; s5, generating a local bone reconstruction simulation result; s6, performing dynamic interaction on a local bone reconstruction simulation result and the preliminary implant positioning scheme, optimizing the preliminary implant positioning scheme through a depth variation inference model, and generating an implant optimized positioning scheme including mechanical stability and biomechanical performance evaluation; and S7, based on the implant optimization positioning scheme, generating implant guide plate design data and surgical navigation data in combination with the jaw bone three-dimensional reconstruction model of the patient. According to the method, the final implant positioning point obtains a globally optimal solution in the aspects of long-term stability and biological adaptability.

An orthopedic tool includes a main body and a reference member. The reference member may be configured to engage an anatomical feature of a patient, such as the patient's bone. The angle of the reference member may be adjusted using a knob.

The invention discloses a digital imaging method of an ear-nose-throat examination endoscope, and relates to the technical field of medical treatment, and the method comprises the following steps: synchronously collecting a white light image, a photoacoustic signal, a stimulated Raman spectrum, pressure sensor data and a physiological signal of a target orifice through a multi-physical field probe; constructing a four-dimensional tensor bound with the anatomical features; based on a vocal cord vibration fundamental frequency harmonic characteristic optimization graph convolutional network, dynamically constructing an adjacent matrix and coupling cross-modal characteristics to generate a submucosal lesion enhanced image; a deformable convolutional network constrained by vocal cord biomechanics is adopted, and the shape of a convolution kernel is dynamically adjusted according to the relation between real-time strain and elastic modulus, so that motion artifacts caused by swallowing actions are inhibited; generating a curvature-driven asymmetric convolution kernel based on ear canal spiral geometry, and executing super-resolution reconstruction in combination with confrontation training of fractal constraint; and fusing the white light image gradient and the pressure gradient field, and outputting a three-dimensional lesion contour consistent with the anatomical structure.

The invention provides an echocardiogram video abstraction method based on a hierarchical space-time attention mechanism, and the method comprises the steps: obtaining an echocardiogram, inputting the echocardiogram into an anatomy and physiological basic model of a key frame recognition task, and obtaining key frame information; inputting the key frame information into a multi-dimensional spatial-temporal feature extraction model to obtain feature information and frequency domain information; fusing the feature information and the frequency domain information; performing key frame enhancement processing on the fusion information by adopting an SE channel attention mechanism to obtain key frame anatomical feature expression; processing the key frame anatomical feature expression by adopting a key frame backtracking strategy and a classification network to obtain a key frame identification classification result; performing left ventricle segmentation on the classified image by adopting an improved U-Net left ventricle region segmentation network; estimating the area and volume of the left ventricle based on a segmentation result; fusing the estimated left ventricular area and volume by adopting a hierarchical attention mechanism and a cross-layer information fusion strategy to obtain a ventricular volume result; according to the method, through abstract extraction of the key information of the ultrasonic cardiac video, the clinical practicability of intelligent screening and auxiliary diagnosis of cardiovascular diseases based on ultrasonic is improved.

The invention discloses a medical image segmentation algorithm and system based on Mama, and a storage medium. The method comprises the following steps: processing medical image features through a multi-path convolution attention mixer MCAM; the VSS Block captures global information through a state space model to enhance the coding capability; the COC module extracts features through channel projection, vertical offset and horizontal offset; the up-sampling features and the features output by the MCAM are fused, the resolution of the image is gradually recovered through inverse operation, and the number of channels is adjusted to the proportion required by the segmentation task; inputting the feature maps after multiple complete coding and down-sampling into an up-sampling and visual state space module, and decoding the feature maps together with the corresponding feature maps of multiple coding cross offset connection operations; medical image segmentation is carried out, and key anatomical features and subtle pathological changes are accurately distinguished. By improving downsampling and U-Net jump connection based on Mama, the feature extraction capability and the capability of capturing complex semantic information are enhanced.

A prosthesis can include a shell and a needle guard coupled to a posterior portion of the shell. The shell can include a laminate having a base layer, a top layer, and a soft silicone gel intermediate layer disposed between the base layer and the top layer and of sufficient thickness for self-sealing of a needle hole therethrough. The needle guard can include a plurality of offset layers of offset, puncture-resistance structures that, in an expanded state, (i) collectively form a concave shape and (ii) provide a concave exterior surface along the posterior portion of the prosthesis for approximating a human anatomical feature. The needle guard can have a shape memory characteristic for urging the prosthesis toward the expanded state.

The invention discloses a diabetic foot early-stage detection method combining infrared and visible light imaging, and relates to the technical field of diabetic foot medical imaging diagnos.The diabetic foot early-stage detection method comprises the steps that multi-mode image collection and standardization processing are conducted, infrared images and visible light images are synchronously collected, and standardization processing such as size normalization is conducted; image registration and space alignment are carried out, marking points are set based on foot anatomical features, and feature extraction, mismatching point elimination and transformation matrix calculation are carried out; extracting and screening multi-dimensional features, extracting temperature and structural features, and screening by using a Relief-F algorithm; feature lesion recognition and classification are fused, and lesion probability is output through a double-branch convolutional neural network; carrying out detection result verification and feedback optimization, and comparing a clinical diagnosis optimization model; and generating a detection report and storing data, and generating a report containing the fused image. The early lesion detection precision is improved through multi-modal fusion, individual and environment differences are adjusted and adapted in a personalized mode, and reliable technical support is provided for clinic.

The invention discloses a fractal-driven laparoscope Toldt line positioning method and device and a medium. The method comprises the following steps: acquiring a plurality of laparoscope video frames; performing feature modeling on the plurality of laparoscope video frames according to a Toldt line segmentation model to obtain a multi-scale feature map; and performing multi-scale spatio-temporal feature fusion and aggregation processing on the multi-scale feature map, and inputting a processing result into a multi-scale decoder for prediction to obtain the position of the Toldt line in the abdominal cavity. According to the fractal-driven laparoscope Toldt line positioning method and device and the medium, the anatomical features of the Toldt line are captured through accurate feature modeling, the adaptability to the dynamic abdominal cavity environment is enhanced in combination with multi-scale spatial-temporal feature fusion, high-precision prediction is achieved through a multi-scale decoder, and therefore the position of the Toldt line in the abdominal cavity is recognized, and the positioning accuracy of the Toldt line in the abdominal cavity is improved. The problem that it is difficult to accurately recognize the position of the Toldt line in the abdominal cavity can be solved.

The invention discloses a diffuse large B-cell lymphoma segmentation system based on deep learning, and the system comprises a data collection module which is used for collecting PET and CT images of a patient; the double-branch encoder module is used for processing PET and CT images respectively; the cross-modal feature cross fusion module is used for extracting positioning clues from the CT anatomical features by taking the PET metabolic features as queries; taking CT structure characteristics as query, and screening PET high-metabolism regions; the global-attention suppression module is used for extracting global context features, performing down-sampling, remodeling input features into a low-dimensional matrix, then performing self-attention calculation, generating a sparse weight matrix for the input features based on a full connection layer, and then performing sparse attention calculation; and finally, fusing the two attention calculation results and outputting enhanced features. And the multi-scale jump connection module is used for fusing the encoder features and the decoder features and recovering spatial details.

The invention discloses a high-precision and automatic dental crown generation method. The method comprises seven steps of oral cavity three-dimensional data acquisition, full-mouth tooth semantic segmentation, upper and lower jaw point cloud precise registration, abutment and associated gingiva precise segmentation and trimming, abutment surrounding environment multi-tissue segmentation, neck-edge line secondary precise detection, and dental crown generation and multi-dimensional refinement. The method comprises the steps of oral cavity three-dimensional data acquisition, full-mouth tooth semantic segmentation, upper and lower jaw point cloud precise registration, abutment and associated gingiva precise segmentation and trimming, abutment surrounding environment multi-tissue segmentation, neck-edge line secondary precise detection and dental crown generation. Through combination of deep learning and a point cloud algorithm, high-precision segmentation of teeth and gingiva and precise reduction of an occlusion relationship are realized, an oral anatomical feature library and a clinical repair standard are fused, and through multi-dimensional fine adjustment optimization, a dental crown 3D model which is high in fitting degree, harmonious in occlusion and capable of meeting clinical requirements is generated. The method is full-process automatic, greatly improves efficiency, reduces operation threshold, reduces material waste and diagnosis and treatment cost, remarkably improves repair success rate, and is suitable for various oral repair scenes.

The invention discloses a liver cancer image lightweight segmentation method based on UNet, and relates to the technical field of liver cancer segmentation of medical images. According to the method, a lightweight dilated convolution attention module combining local separable dilated convolution and a context anchor attention module is provided, and the feature expression ability is enhanced while the calculated amount is reduced; a self-adaptive wavelet pooling down-sampling module combining wavelet transform and a self-adaptive attention mechanism can perform further feature concentration and extraction on the feature map; a lightweight feature up-sampling and enhancement fusion module composed of a local separable cavity convolution module and a multi-frequency channel attention module can effectively improve the cross-scale feature fusion capability of the model, and maintain a low parameter amount and a low calculation amount. According to the method, while the parameter quantity and the calculation cost are remarkably reduced, the anatomical features of the liver can still be accurately captured, a finer segmentation result is provided, doctors are assisted in optimizing treatment schemes such as tumor excision and living body transplantation, and the clinical decision-making efficiency is improved.

A surgical planning system is disclosed which provides computer assisted navigation for spinal surgery. The surgical planning system includes a computer platform operative to obtain images of at least two different postures of a patient's spine, and to measure displacement of anatomical features of the patient's spine between the images of the at least two different postures of the patient's spine. The computer platform is further operative to estimate stiffness of the patient's spine based on the measurements of displacement, and to generate a patient-specific kinematic model of the patient's spine based on the estimated stiffness, and provides a surgical plan based on the patient-specific kinematic model of the patient's spine.

Examples relate to a system (110), method, and computer program for an ophthalmic microscope system, and to a corresponding ophthalmic microscope system. The system is configured to obtain imaging sensor data from an optical imaging sensor (122) of a microscope of the ophthalmic microscope system. The system is configured to determine information on an anatomical feature of an eye shown in the imaging sensor data. The system is configured to control an illumination system (130) of the ophthalmic microscope system to adjust a property of a light beam used for red reflex illumination of the eye based on the information on the anatomical feature.

The invention relates to the technical field of medical data mining, in particular to a postoperative pulmonary infection prediction method and system.The postoperative pulmonary infection prediction method comprises the following steps that a continuous data set is formed by collecting breathing parameters and conducting alignment completion, tidal volume and blood oxygen response delay characteristics are extracted through sliding analysis, and a functional abnormal pulmonary segment is located through local anomaly recognition; and fusing physiological and anatomical features to establish a spatial probability map, extracting a respiratory variation coefficient and a risk feature regression analysis confidence index, and outputting an infection risk early warning list with anatomical positioning. According to the method, data faults are eliminated through timestamp alignment to improve the reliability of time sequence association, the breathing compensation capability is quantified through multi-scale window analysis dynamic coupling, lesion avoidance feature masking is accurately positioned through local anomaly detection, and the result interpretability is enhanced through combination of multi-modal feature fusion and space mapping. The three-stage progressive mechanism improves the prediction precision and timeliness, and provides a decision basis with both time sensitivity and anatomical orientation for clinic.

The invention discloses an EEG-to-three-dimensional fMRI volume generation method based on multidirectional time-frequency convolution attention and VM-UNet, and the method comprises the steps: S1, carrying out the preprocessing of an inputted EEG signal, constructing an EEG input sample in a three-dimensional tensor form, and carrying out the normalization processing of EEG and fMRI data; s2, mapping an EEG input sample to a high-dimensional feature space by using an EEG spectrogram projection module to obtain an initial embedding representation; s3, processing the initial embedded representation through a multi-directional time-frequency convolution attention encoder to obtain a multi-level fusion global feature representation, and adjusting the multi-level fusion global feature representation into a feature map with a target size; s4, the adjusted feature map is input into a Vision-MambaU-Net decoder, multi-scale decoding and spatial reconstruction are carried out, and a three-dimensional fMRI volume is generated; and S5, performing end-to-end training on the model by adopting a joint loss function to realize the generation of the volume from the EEG to the three-dimensional fMRI. According to the method, the fine anatomical features of cortex wrinkles and deep grey structures are accurately reserved, and the video memory consumption and the reasoning time are greatly reduced.

The invention relates to the technical field of medical instruments, and discloses a tracheotomy anatomical structure image recognition method and system, and the method comprises the steps: obtaining real-time visual image data and spatial positioning data of an endoscope, carrying out the time synchronization, carrying out the mode recognition of the spatial positioning data, and locking the remarkable anatomical features in the real-time visual image data, and according to the characteristics and the spatial positioning data, calculating a geometric transformation relationship between an endoscope visual coordinate system and a spatial positioning coordinate system, and finally fusing the data and displaying anatomical structure information and a surgical tool position in real time. The method can effectively solve the problem that in the prior art, an endoscope is irregularly deformed in the narrow trachea with physiological bending of a patient.

The invention discloses a registration method based on a three-dimensional retina OCT (Optical Coherence Tomography) image, which relates to the technical field of OCT image registration, and comprises the following steps of: in the registration process of the OCT image, firstly, carrying out preliminary registration by taking anatomical feature points of the retina as key references of basic alignment, and eliminating global mismatching errors; after the preliminary registration is completed, the OCT image is divided into a plurality of sub-regions in an equal area, and each sub-region is used as an independent analysis unit, thereby facilitating the subsequent precise analysis of the local optical characteristics of each analysis unit. According to the method, global errors are eliminated through initial registration of retina anatomical feature points, image division and abnormal signal extraction are combined, healthy and scar tissue areas are intelligently recognized through a machine learning model, weights are dynamically adjusted to preferentially depend on the healthy areas, interference of scars on registration is reduced, registration reliability is improved, clinical misjudgment is reduced, and the accuracy of registration is improved. And technical support is provided for accurate diagnosis and treatment of retinopathy.

System and method for lidar-based anatomical mapping is provided. In one implementation, a LIDAR imaging technology is utilized to create a surface-based anatomical map of an exposed anatomical surface of at least a portion of the spine, or other anatomical features, using anatomical and / or artificial landmarks are disclosed. The surface mapping can then be registered with previously acquired images of at least the portion of the spine, or other anatomical features. The surface mapping can then be used to identify important structures, including an entry point in tissue (including soft tissue and bone) for the placement of a medical instrument or implant, or for Information the orientation of a medical instrument or device. The information can be displayed in an augmented reality overlay on a device display (e.g., wearable eyeglasses). The LIDAR imaging technology may be incorporated into wearable eyeglasses.