496 results about "Vertebra" patented technology

A clinical vertebra image segmentation method for assisting pedicle screw placement surgery, said method comprising: constructing a VerseDiff-UNet end-to-end framework, the framework being integrated with a denoising diffusion probabilistic model (DDPM); combining a noise-added image with a marked mask by using the VerseDiff-UNet framework, and guiding a diffusion direction toward a target region; and introducing a shape priors module on the basis of the DDPM, and extracting structural semantic information from an input spine image. In order to capture specific anatomical prior information in a medical image, the shape priors module is combined and the module effectively extracts the structural semantic information from the input spine image, thereby enabling more accurate anatomical structure segmentation, and facilitating accurate diagnosis and treatment of spinal disorders.

The embodiment of the invention provides a spine image recognition method and related equipment. The spine image recognition comprises the following steps: acquiring a spine image to be detected, and preprocessing the spine image to be detected to obtain a target spine image; the target spine image is input into a key point recognition model, position information output by the key point recognition model for each section of vertebral body in the target spine image is obtained, and the position information is marked on the target spine image and at least comprises positioning frame information, a vertebral body name and angular point coordinates; the key point identification model comprises a CBAM module; correcting and adjusting the position information to obtain corrected position information; and performing intervertebral disc positioning and vertebra identification according to the corrected position information. According to the technical scheme, the improved key point recognition model is adopted, the positioning frame, the cone name and the angular point coordinates are directly output without depending on a segmentation model, the process is simplified, and the detection speed and robustness are improved.

The embodiment of the invention is suitable for the technical field of computer-aided medical treatment and image processing, and provides an automatic planning device and equipment for a pedicle screw and a computer program product. Extracting vertebral body outer layer point cloud data of a to-be-planned single vertebral body from the spine image data; the prediction model is used for processing the centrum outer layer point cloud data and outputting prediction coordinates, and the prediction coordinates comprise entry point coordinates and exit point coordinates; and the planning unit is used for planning the placement position of the pedicle screw based on the entry point coordinate and the exit point coordinate. By adopting the device, the planned position of the pedicle screw can be quickly and accurately predicted by using the deep learning model based on the point cloud data of the outer layer of the vertebral body, the data volume needing to be processed in the planning process is reduced, and the planning efficiency is improved.

The invention specifically discloses an automatic generation method for a cervical vertebra disease rehabilitation prescription, and the method comprises the steps: constructing a cervical vertebra image feature automatic measurement system based on computer vision and deep learning, so as to monitor the training motion quality of a patient in real time, and obtaining the kinematics parameters of rehabilitation exercise training; acquiring a medical image and clinical data of a patient, and standardizing the medical image to construct a standard space of a multi-modal medical image; performing automatic segmentation and anatomical structure calibration on a vertebral body-intervertebral disc based on the standard space to obtain fusion image features; and constructing a multi-modal hierarchical decision model fusing the image features, the kinematics parameters and the clinical data to carry out quantitative analysis on pathological feature parameters, and outputting a standardized illness state assessment result conforming to international clinical guidelines. The dynamic optimization of the treatment scheme can be realized through a feedback mechanism, and the treatment effect is improved.

The invention provides a spine centrum compression fracture recognition method and system based on artificial intelligence, and the method comprises the steps: receiving original spine CT sequence data, and outputting a preprocessed standardized image; inputting the preprocessed image into a three-dimensional U-Net + + network to obtain a centrum segmentation probability graph; outputting the segmented centrum set and the position code thereof; for each segmented centrum, extracting a corresponding area based on a centrum mask, and splicing all features to form a comprehensive feature vector of each centrum; inputting the three-dimensional image block of each vertebral body into an image flow network to extract visual semantic features; and automatically generating a structured PDF report. According to the spine centrum compression fracture recognition method and system based on artificial intelligence, the problem of image quality difference caused by different CT devices and scanning parameters is effectively solved through the multi-scale residual enhancement technology and self-adaptive window level adjustment, and standardized processing of images is achieved.

A continuum manipulator that comprises an assembly of ‘vertebra’-like modules fabricated using two curved links and twelve strings and actuated using Motor-Tendon Actuators. The modules being modeled as tensegrity structures having a polyhedron shape. The vertices and edges of the tensegrity structure polyhedron correspond to the holes and strings or links of the structure. Furthermore, a mobile continuum manipulator that includes a control unit and wheels or mobile legs.

A system for correcting a lateral curvature of a spine that can include a plurality of screws configured to be implanted in a plurality of vertebrae, and a plurality of extensions configured to be removably coupled with the plurality of screws. Some embodiments of the plurality of extensions can be curved, bent, angled, and / or offset along at least a portion thereof and can be removably coupled with a screw head of each of the plurality of screws. The system can include a connecting element or rod that is configured to be coupled with the plurality of screw heads. Some embodiments of the system can be configured such that the rod can be guided along the plurality of extensions from the proximal toward the distal end portions of the extensions and into engagement with the plurality of screws to cause the plurality of vertebrae to move laterally.

The present invention discloses a two-stage AIS spine CT image multi-label segmentation method based on deep learning. The method first obtains an AIS spine image dataset, preprocesses the images in the dataset, and creates new labels for vertebrae positioning. Secondly, the preprocessed images and new labels are processed to train a convolutional neural network for vertebrae positioning. The preprocessed images are then tested using the trained convolutional neural network, and the test results are post-processed to obtain the predicted results of the vertebral center of mass in the spine image. The preprocessed original images and labels are then reprocessed to train the constructed segmentation network. Finally, a region of interest is created based on the vertebral center of mass position predicted during the vertebrae positioning process, and the trained segmentation network is used to segment the target vertebrae in the region of interest, and then the multi-label segmentation results are obtained by integration. The present invention can effectively segment vertebrae.

The invention discloses a pillow regulation and control method self-adaptive to cervical vertebra flexion and shoulder width and application of the pillow regulation and control method to a pillow, and relates to the technical field of sleeping posture regulation. The method comprises the steps of registering a three-dimensional contour point cloud through an ICP algorithm, calculating a neck curvature value, establishing a multiple linear regression model in combination with a pressure gradient change curve and a pressure peak offset, and realizing quantitative correlation between pressure data and a cervical vertebra inclination angle; based on the curvature change amplitude and shoulder breadth classification, a personalized model is constructed; and through attitude monitoring and pressure feedback, a real-time optimization instruction is generated. A target displacement error is set, and correction is stopped after the bending degree reaches the standard continuously for three times; pre-storing a medical-level support parameter library; multi-user switching is achieved through pressure feature and head contour double verification, and physical compression emergency stop is set. According to the method, through multi-source data fusion, scientific model construction and dynamic closed-loop control, the personalization and safety of pillow supporting are improved, and the method is suitable for people with sub-health cervical vertebrae and multi-user scenes.

The invention discloses an image processing method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining to-be-processed three-dimensional medical image data, carrying out the spinal segmentation of a spinal region in the three-dimensional medical image data, and obtaining vertebral mask data; and performing cone identification processing on the cone mask data to obtain a target end cone mask corresponding to the Cobb angle, constructing a space vector model for the target end cone based on the target end cone mask, and determining a Cobb angle value for the target end cone based on the space vector model. According to the method, the Cobb angle is calculated by introducing space vector modeling, so that the measurement error of measurement of the Cobb angle can be remarkably reduced, and the accuracy of measurement of the Cobb angle is improved.

The invention provides an orthodontic skull lateral film cervical vertebra image segmentation method and system. According to the method, an orthodontic skull side position film image to be processed is obtained, a target image area containing a second cervical vertebra, a third cervical vertebra and a fourth cervical vertebra is determined, then image preprocessing is carried out on the target image area to obtain a standardized input image, then the standardized input image is input into a CNN-Transformer fusion segmentation network for segmentation reasoning, and the image is obtained. According to the method, the centrum segmentation masks of the second cervical vertebra, the third cervical vertebra and the fourth cervical vertebra are output, and finally, the segmentation result is output based on the centrum segmentation masks, so that the segmentation result is output based on the centrum segmentation masks, and a repeatable and traceable objective basis can be provided for subsequent morphological parameter extraction and bone age / maturity quantitative evaluation; the subjective deviation and time cost of manual sketching are practically reduced, and the segmentation consistency and availability under the cross-individual and cross-equipment condition are improved.

The invention relates to the technical field of medical equipment, in particular to an angle-adaptive anterior cervical dilator. An auxiliary plier body is connected with a main plier body. The plurality of supporting rods are mounted at respective working ends; the self-adaptive opening assembly is used for automatically adjusting and fixing according to the intervertebral space of a patient, and comprises a sliding adjusting part which is fixed on the main forceps body and is used for opening the main forceps body and the auxiliary forceps body in a semicircular rotating direction; the hydraulic triggering part is installed in the main clamp body and acts on the sliding adjusting part so that the main clamp body and the auxiliary clamp body can be locked after the cervical vertebra of the patient is opened. The clamping blocks are driven through the hydraulic action to lock the piston block located in the inner cavity of the clamp body, and then the main clamp body and the auxiliary clamp body fix the intervertebral space at the current opening distance; the clamping distance adjusting assembly is designed, the auxiliary supporting rod and the ratchet bar are combined with the locking block with the rack structure to fix the auxiliary supporting rod, and the fixing device has the advantages of being good in fixing effect, high in flexibility and convenient to use.

The invention discloses a manufacturing method of a 3D printing guide plate for a cervical vertebra single-door operation, and relates to the technical field of crossing of medical auxiliary instruments and additive manufacturing. Firstly, a cervical vertebra CT data three-dimensional reconstruction skeleton model is obtained, an operation scheme is virtually planned, then a navigation template digital prototype is generated through parameterization, finite element simulation optimization is carried out, and a three-dimensional 3D printing guide plate for the cervical vertebra single-door operation is obtained. Printing with the biocompatible resin by using light curing equipment, and finally treating to obtain a final product. According to the method, a high-precision cervical vertebra skeleton model is generated through CT thin-layer scanning and three-dimensional reconstruction, parameters of a hinge area, a door opening groove line and a nail path are determined through virtual planning, and the problem that damage or positioning deviation is easily caused due to the fact that a traditional operation depends on experience is solved; constructing a navigation template attaching curved surface by a specific algorithm, reserving a soft tissue compensation gap, matching with a hollow window bulge to limit the depth of an abrasion drill, controlling the deformation of a guide plate through finite element simulation and topological optimization, selecting specific resin to ensure the stability, ensuring the suitability by virtue of virtual registration and standardized manufacturing, and improving the accuracy, safety and efficiency of an operation.

The invention relates to the technical field of cervical vertebra rehabilitation systems, and discloses an intelligent self-adaptive cervical vertebra rehabilitation system. The system comprises a multi-mode sensing unit which synchronously collects form data and soft tissue data of the cervical vertebra of a user through an optical mark point array and an ultrasonic probe array to generate a multi-source sensing data set; the intelligent analysis unit constructs a cervical vertebra dynamic mechanical model by using a graph convolutional neural network based on the multi-source sensing data set, analyzes a cervical vertebra joint coupling relation and stress distribution, and generates an analysis parameter set; the strategy generation unit generates a personalized rehabilitation strategy according to the analysis parameter set, the personalized rehabilitation strategy comprises an action sequence and an impedance adjustment parameter, and a strategy instruction set is formed; the dynamic control unit responds to the strategy instruction set and combines muscle activity data fed back by the electromyographic signal sensor in real time to adjust an execution strategy and generate a control command set; and the flexible driving unit drives the soft robot outer skeleton to perform multi-axis linkage rehabilitation training based on the control command set.

A neck traction fixing device comprises an elastic supporting part, an adjusting assembly is arranged in the elastic supporting part and comprises an outer shell, an upper supporting frame, a lower supporting frame, a transmission assembly and a rotary adjusting part, the upper supporting frame and the lower supporting frame are arranged in the outer shell in an up-down staggered mode, and the transmission assembly is connected between the upper supporting frame and the lower supporting frame in an engaged mode; the rotary adjusting part comprises an adjusting plate and a rotary cover body, a mounting groove is formed in the front face of the outer shell, a rigid tooth-shaped surface is arranged on the inner edge of the mounting groove, and a concave notch is formed in the outer wall of the adjusting plate. The elastic supporting part is arranged for protecting the cervical vertebra; by arranging the adjusting assemblies capable of moving independently, the elastic supporting part can conduct independent traction and fixation on the left side, the right side and the rear side of the neck, especially for a patient with the torticollis, neck muscle groups at different positions of the neck can be stretched, neck muscle tension can be relieved, and the torticollis orthopedic effect is more remarkable.

Systems and methods for automatically determining pedicle screw trajectories for surgery may be provided. A scan of a spine may be received, and positions of one or more vertebra and one or more components of the one or more vertebra in the scan may be identified. Next, a screw trajectory planning algorithm may determine an initial screw trajectory plan using the positions of the one or more vertebra and the one or more components. The screw trajectory planning algorithm may then determine a revised screw trajectory plan by revising the initial screw trajectory plan according to weighted factors.

The invention provides an osteoporosis fracture risk prediction method and system based on multi-modal imageomics. The method comprises the following steps: S1, acquiring multi-modal image data of the lumbar vertebra of a user; s2, performing feature correction according to the acquired multi-modal image data to obtain a time-space aligned multi-modal feature tensor; s3, according to the obtained multi-modal feature tensor, the integrity of the bone microstructure at each position is evolved based on the trained bone microstructure evolution model, and a bone microstructure integrity evolution result is obtained; and S4, according to the obtained bone microstructure integrity evolution result, extracting a fracture risk characteristic part, and further performing fracture risk prediction on the fracture risk characteristic part to obtain a fracture risk prediction result. According to the invention, the accuracy and reliability of osteoporotic fracture risk prediction can be improved.

The invention provides a collision dummy, relates to the technical field of collision dummies, and can improve the reliability of collision test data. A thoracic section spine structure of the collision dummy comprises at least two first rigid thoracic vertebra pieces and at least two flexible thoracic vertebra pieces, the first rigid thoracic vertebra pieces and the flexible thoracic vertebra pieces are alternately arranged in the extending direction of the thoracic section spine structure and are sequentially connected end to end, and rib pieces are fixedly connected with the first rigid thoracic vertebra pieces. The rib parts and the first rigid thoracic vertebra parts are arranged in a one-to-one correspondence mode, and the at least two rib parts are slidably connected with the sternum part in the extending direction of the thoracic segment spine structure. According to the collision dummy provided by the invention, compression and extension of the flexible thoracic vertebra piece enable the thoracic segment spine structure to generate motions such as bending, extension and lateral bending, so that the collision dummy can generate responses such as forward bending, backward bending and lateral turning, and the injury condition of the collision dummy in collision is relatively consistent with the injury condition of a real human body; the reliability of collision test data can be improved.

A magnetic compression anastomosis device comprises a first multipiece internal vertebrae support structure including a first set of magnets attached to an outward-facing side of the first multipiece internal vertebrae support structure; and a second multipiece internal vertebrae support structure including a second set of magnets attached to an outward-facing side of the second multipiece internal vertebrae support structure, wherein the first and second multipiece internal vertebrae support structures are attached together in a sandwich configuration with an inward-facing side of the first multipiece internal vertebrae support structure facing an inward-facing side of the second multipiece internal vertebrae support structure and the magnets on the outward-facing sides of the magnetic compression anastomosis device.

The invention provides an X-ray-based scoliosis 3D intelligent automatic diagnosis system, which is characterized in that a spine area bounding frame coordinate, a hip bone bounding frame coordinate and a vertebral pedicle bounding frame coordinate are extracted from spine coronal image data, and the first 17 results are extracted to be matched with the number and spatial distribution of typical vertebral bodies of a human body; the method comprises the following steps: obtaining a spine region sub-graph, a hip bone region sub-graph and a vertebral pedicle region sub-graph, converting the sub-graphs into tensors to obtain a bounding box, four angular point coordinates and corresponding confidence of each vertebral body, storing the bounding box, the four angular point coordinates and the corresponding confidence as a structured record, performing Cobb angle calculation according to the structured record, and converting the structured record into an anatomical segment identifier; cutting is conducted according to the spine area, the hip bone area and the vertebral pedicle area subgraph, segmented vertebral pedicles and hip bones are obtained, the major curvature type, the pelvis height and the rotation direction are judged according to anatomical segment identifiers, and the patient age is determined according to the midpoints of the upper edges and the lower edges of the vertebral bodies, the arrangement angle of the adjacent vertebral bodies, spine major curvature core parameters and the age of the patient. And outputting a scoliosis 3D intelligent automatic diagnosis result.

The invention relates to the technical field of medical instruments, in particular to an active variable bush-hook of a cervical and lumbar surgery instrument and an application method of the active variable bush-hook. The variable bush-hook comprises a handle assembly and a variable bush-hook assembly which are connected with each other. The variable hook knife assembly comprises an angle-adjustable hook knife; by operating the handle assembly, the hooked knife can be driven to change the angle, and the cutting action on target tissue is achieved through manual force application. Through the specific structure connection and transmission design, the exposure angle of the cutting part of the hook knife can be changed, automatic resetting can be achieved, and precise, safe and active cutting of the hooked vertebra joint ligament or the posterior longitudinal ligament in the deep narrow space is achieved.

1. The name of the design product: Cervical vertebra massager (RD-A136). 2. The use of the design product: for massaging cervical vertebra. 3. The design points of the design product: in shape. 4. The picture or photo best indicating the design points: perspective view 1.

The invention discloses an intelligent evaluation method for screening multiple diseases of lumbar vertebra based on deep learning, and relates to the technical field of intelligent screening of lumbar vertebra diseases, and the method comprises the steps: collecting multi-source data, and obtaining lumbar vertebra CT, MRI, bone mineral density images, clinical diagnosis and treatment data and operation related parameters; preprocessing data, denoising images, segmenting key structures, and standardizing non-image data; a deep learning model is constructed, image features are extracted through CNN branches, non-image features are processed through Transform branches, and disease classification, severity assessment and risk prediction are achieved in combination with multi-task learning; integrating the multi-modal features through a feature fusion optimization algorithm, and outputting an evaluation result; doctor interaction correction and model iteration are supported, and the generalization ability of the system is improved. According to the method, multi-source data are integrated, the accuracy and efficiency of lumbar vertebra multi-disease assessment are improved, postoperative risk prediction is optimized, clinical diversified requirements are met, and powerful support is provided for precise diagnosis and treatment and rehabilitation.

The utility model relates to the technical field of medical rehabilitation, in particular to a cervical vertebra, thoracic vertebra and lumbar vertebra of a spine of a human body have various diseases, and when medical staff carry out treatment, patients are often suggested to support injured parts by using some appliances or other methods, so that the injured parts return to the physiological curvature which is naturally grown by the human body. However, existing appliances in the medical field are not good in effect, in order to solve the technical problem, the technical scheme is that the combined three-vertebra rehabilitation pillow comprises a bottom cushion, a pillow cushion, a lumbar vertebra cushion, a cervical vertebra cushion and a binding belt, when a user is supported to be in a supine state, the cervical vertebra, the thoracic vertebra and the lumbar vertebra return to the physiological bending degree at the same time, and the cervical vertebra, the thoracic vertebra and the lumbar vertebra return to the physiological bending degree. And a stretching (or relaxing) state is formed under the action of gravity, so that conditions are created for maintaining the health of the spine of a human body and removing injuries and diseases of the spine.

The invention discloses a cervical curvature judgment method based on in-vitro photographing. The method comprises the following steps: preprocessing an original neck in-vitro image of a user to obtain an equalized image; determining a single local feature of each pixel at different positions under different convolution sums; extracting a previous-layer feature map in the single local features through U-Net, and performing segmentation to obtain a current-layer feature map; performing up-sampling operation on the current-layer feature map to obtain a prediction size of the front-layer feature map, and determining cross entropy loss in combination with the prediction size; a key point set in the original neck in-vitro image is extracted through a total loss function, and the total length of a fitting curve is calculated; determining the curve of the cervical vertebra shape according to the total length of the fitting curve; determining the curvature of the position of the key point in the key point set and the correction length of the curve of the cervical vertebra shape; therefore, the degree of cervical curvature abnormity is determined. According to the invention, a deep learning technology is combined, and doctors are assisted in diagnosis in a radiation-free manner.

The invention relates to the technical field of osteoporosis detection, in particular to an osteoporosis risk assessment system and method for lumbar magnetic resonance images. The system comprises an image acquisition module, a structure processing module, a feature extraction module, a bone mineral density prediction module and a reasoning control module, through MRI image preprocessing and image feature vector extraction, in combination with non-image information such as age, gender, height, weight and the like, a backstepping mapping model is constructed in a multi-modal mode, and continuous estimation of bone mineral density and bone related characterization values is achieved. According to the method, quantitative extraction of the iconography parameters of the lumbar vertebra bone tissue can be achieved under the non-radiation condition, and a safe and repeatable opportunity analysis means is provided for iconography monitoring and follow-up visit of high-risk people; meanwhile, the adaptability of the model in multi-center data is remarkably enhanced, overfitting is avoided, and the consistency and reliability of calculation results of bone characterization parameter values between different devices and samples are improved.

This disclosure provides a method, system, electronic device, and storage medium for determining the actual shape of a vertebra. The method includes: acquiring a vertebral image; acquiring edge association information for each edge of the vertebra in the vertebral image; determining whether the edge association information satisfies a corresponding preset condition; if so, processing the edge association information using a matching preset edge processing rule to generate a real edge line corresponding to each edge; wherein different edges correspond to different preset conditions and preset edge processing rules; and generating a real edge graphic corresponding to the vertebra based on each real edge line. In this disclosure, different edges of the vertebra are processed using corresponding preset edge processing rules to automatically, quickly, and accurately extract each actual edge line of the vertebra, ensuring the accuracy and efficiency of segmenting the actual shape of the vertebra from the vertebral image.

1. The name of the design product: cervical vertebra protection pillow. 2. The use of the design product: cervical vertebra protection pillow. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.