3591 results about "Anatomical structures" patented technology

A robotic surgical system includes a surgeon consol coupled to a patient consol, and the patient consol coupled to surgical instruments. A surgeon computer is coupled to or at the surgeon consol that is coupled to to one or more surgical instruments. A robotic surgery control system includes an artificial intelligence (AI) system with one or more deep learning algorithms. A feedback loop monitors and collects data from the one or more sensors. One or more cameras provide feedback to the robotic surgical system, and are configured to provide images of an anatomical object in at least a two dimensional (2D) arrangements of pixels / Deep learning algorithms of the AI system distinguish different anatomical objects from the images.

The invention provides an artificial intelligence-based ultrasonic image data classification method and system, and the method comprises the steps: firstly obtaining a real-time ultrasonic scanning signal sequence containing the time sequence change characteristics of a tissue elastic parameter and a hemodynamic parameter, carrying out the noise suppression and motion artifact compensation processing, generating a standardized ultrasonic image sequence, and marking the coordinates of an anatomical boundary; then performing multi-scale anatomical structure decomposition on the ultrasonic image to obtain a local feature map set of different organization levels, inputting the local feature map set into a cascade deep classification network, and realizing cross-frame feature fusion and dynamic weight adjustment through spatial-temporal feature alignment and a multi-granularity attention distribution module to obtain a spatial-temporal feature fusion model; and the abnormal region classification probability distribution and the spatial topological relation graph are output, finally, a multi-modal diagnosis report is generated according to the abnormal region classification probability distribution and the spatial topological relation graph, an interactive three-dimensional visual interface containing risk level labels and treatment suggestions is generated after the multi-modal diagnosis report is compared with historical cases, and ultrasonic image classification accuracy and diagnosis efficiency are improved.

The invention discloses a medical image segmentation method based on a full convolutional neural network, and the method comprises the steps: carrying out the preprocessing of a medical image through a deep learning algorithm for the complex anatomical structure and pathological features of the medical image, enhancing the key features in the image, and providing a richer information basis for the subsequent feature extraction; in the feature extraction stage of the full convolutional neural network, an attention mechanism module and a spatial pyramid pooling module are introduced to learn importance weights of different areas in an image, weight distribution of a convolution kernel is adaptively adjusted, and features with finer granularity and discrimination capability are extracted. The up-sampling process is optimized through a deconvolution operation and a jump connection structure, a conditional random field model is introduced to carry out post-processing optimization, a multi-scale feature fusion module is constructed, and the generalization ability of the network is improved by adopting an adversarial learning strategy. According to the invention, the accuracy and robustness of medical image segmentation are effectively improved.

The invention relates to the field of image analysis, in particular to a temporal bone disease classification method and system based on a multi-modal medical image fusion technology. The method comprises the following steps: acquiring a multi-modal image of a patient, performing adaptive distortion correction, and generating a standardized image set; performing layer-by-layer anatomical structure semantic segmentation and multi-modal image fusion on the standardized image set to construct an image fusion framework; according to the image fusion framework, performing intelligent recognition on the fine structure of the temporal bone, and constructing a personalized temporal bone anatomical structure chart; performing tissue function state analysis and digital pathology dynamic simulation based on the personalized temporal bone anatomical structure chart, and constructing a digital pathology model; and performing intelligent pathological feature classification based on the digital pathological model to obtain an intelligent classification report. According to the method, rapid, efficient and accurate temporal bone disease classification is realized.

The invention discloses a multi-modal medical image fusion diagnosis system based on artificial intelligence, and the system comprises the following steps: extracting shared features of CT and MRI images through a convolutional neural network, and mapping the shared features to the same feature space; a two-way step-by-step alignment strategy is adopted, a three-dimensional deformation field matrix is generated, and cross-modal image anatomical structure alignment is achieved; calculating modal feature weights and eliminating distribution differences through an attention mechanism and an adversarial domain adaptation layer; constructing a CT-MRI image block contrast learning task, and optimizing a shared feature encoder; a conditional generative adversarial network is used for generating a false image of a missing mode according to the semantic segmentation map, and data distribution is constrained through a Wasserstein distance; uniform feature extraction of multi-modal medical images is realized through a shared feature encoder, the cross-modal image alignment accuracy is improved in combination with a bidirectional deformation field prediction module, and the comprehensiveness and accuracy of fusion features are enhanced by using a multi-modal feature fusion module.

The invention relates to a method of visualising a dynamic anatomical structure (1), a computer program and a user interface. The method comprises (a) providing a sequence of three-dimensional medical images (M1, M2, M3, . . . MZ) of a dynamic anatomical structure (1) spanning a time period (T), (b) providing a dynamic model (14), in particular surface of the anatomical structure, (c) determining a volume of interest (40) containing an anatomical feature of interest (3) within each of the three-dimensional images, wherein the volume of interest (40) follows the position and / or the shape of the anatomical feature of interest (3) across the time period and wherein the volume of interest (40) is smaller than the complete field of view of the three-dimensional medical images (M1, M2, M3, . . . MZ), and (d) providing a three-dimensional visualisation environment (50, 70), wherein a visualisation (45) corresponding to a particular point in time comprises (i) a volume rendering of the volume of interest (40) of the three-dimensional image; and (ii) a visualisation of the dynamic model (14) in the same coordinate system. Preferably, the three-dimensional visualisation environment (50, 70) allows for displaying the dynamic model (14) and the volume rendered volume of interest (40) for each three-dimensional image across the time period in cine mode.

The invention discloses a three-dimensional stomatognathic model reconstruction system based on multi-modal data fusion. The system comprises a multi-modal data input unit, a template deformation reconstruction unit, a registration fusion unit, a multi-source data integration unit and an output unit. Through fusion processing of a CBCT image, an oral cavity vision measurement model and facial scanning data, a body deformation algorithm is adopted to couple biomechanical characteristics to realize craniojaw template deformation, and a non-rigid ICP algorithm is combined for dynamic regulation and control to realize facial template adaptation. A deep neural network is innovatively constructed to segment CBCT gingival data, the CBCT gingival data is fused with an oral cavity vision measurement model, and high-precision tooth reconstruction is realized by applying a differential geometry multi-scale curvature field segmentation and adversarial edge optimization technology. Through a composite registration strategy combining adaptive rigid registration and non-rigid registration, an occlusal plane constraint mechanism and an orbital curvature extreme point matching algorithm are innovatively introduced, finally, multi-source data high-precision registration fusion is realized, and a three-dimensional oral-jaw system model with anatomical structure integrity and clinical precision can be generated.

Systems and methods are disclosed for anatomic structure segmentation in image analysis, using a computer system. One method includes: receiving an annotation and a plurality of keypoints for an anatomic structure in one or more images; computing distances from the plurality of keypoints to a boundary of the anatomic structure; training a model, using data in the one or more images and the computed distances, for predicting a boundary in the anatomic structure in an image of a patient's anatomy; receiving the image of the patient's anatomy including the anatomic structure; estimating a segmentation boundary in the anatomic structure in the image of the patient's anatomy; and predicting, using the trained model, a boundary location in the anatomic structure in the image of the patient's anatomy by generating a regression of distances from keypoints in the anatomic structure in the image of the patient's anatomy to the estimated boundary.

The invention provides a multi-angle photographing and three-dimensional reconstruction method and system for soft tissue, and relates to the technical field of soft tissue three-dimensional reconstruction, and the method comprises the steps: analyzing soft tissue features based on deep learning, generating a bulb tube motion track and sampling interval data, and controlling a mechanical arm to drive a bulb tube to collect projection images at multiple angles; screening an optimal image sequence by using an image quality evaluation network, and performing preprocessing such as material decomposition, feature extraction, space registration and image optimization; and inputting the optimized image and the material features into a reconstruction model, performing iterative calculation in combination with an anatomical structure template to generate density distribution data, performing curved surface reconstruction and intelligent tissue segmentation based on graph convolution based on the density distribution data, and finally generating a three-dimensional model containing tissue layering information.

Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

The invention discloses a coronary angiography image blood vessel segmentation system based on multi-scale feature fusion. According to the invention, through the innovative design of the adaptive morphological sensing module, the system can dynamically analyze the anatomical structure characteristics of the blood vessel: the differentiable morphological operation layer converts the corrosion expansion operation into a learnable feature extraction process, so that the network can autonomously identify the gradient change and boundary trend of the blood vessel wall; the dynamic nuclear adaptation mechanism adjusts the scale and direction of morphological operation in real time according to the local blood vessel diameter and curvature characteristics, interference of surrounding tissues in a main blood vessel area can be inhibited, and continuous expression can be enhanced for capillary branches. Through deep fusion of dissection driving and data driving, the topological structure integrity of a segmentation result at a blood vessel bifurcation point and a narrow lesion area is remarkably improved, and the common problems of blood vessel fracture and misconnection in a traditional method are effectively avoided.

The invention discloses a medical image super-resolution reconstruction method based on multi-level attention guidance, and the method comprises the following steps: S10, constructing a deep learning network model based on a generative adversarial network architecture, which comprises a generator and a discriminator; the generator is based on an improved U-Net architecture, a hierarchical attention module and a dual-path feature processing module are configured in an encoder and a decoder of the generator, the hierarchical attention module adopts different attention strategies according to network levels to consider structure and texture, and the dual-path feature processing module separates and processes low-frequency and high-frequency information; the generator further comprises a multi-level feature fusion module for integrating the multi-scale features of the decoder, and an attention guide up-sampling module for final enhancement and dimension raising. The discriminator adopts a spectrum normalization U-Net architecture and uses multi-scale features for matching; s20, training the network model by adopting a composite loss function comprising pixels, adversarial, perception and total variation loss; and S30, inputting the low-resolution image into the trained model, and outputting a high-resolution image. According to the method, through deep fusion of multi-level attention and multi-scale feature processing, the image restoration quality can be remarkably improved, the texture detail definition can be enhanced, the anatomical structure accuracy can be ensured, and the noise robustness can be improved.

A method of visualizing structural information of pulmonary vessels in medical images includes obtaining structural information of pulmonary vessels based on anatomical structure features of the pulmonary vessels from a medical image based on segmentation results of a pulmonary vascular region of the medical image; obtaining pulmonary embolism finding information in the pulmonary vessels of the medical image; visualizing the structural information of the pulmonary vessels in accordance with hierarchical information; and visualizing the pulmonary embolism finding information in association with individual sub-regions of the structural information of the pulmonary vessels visualized in accordance with the hierarchical information.

A method of anonymizing medical images includes acquiring or receiving a first medical image; separating an identification area containing anatomical structure information and a de-identification area containing a skin area in the first medical image; generating a de-identified image including a plurality of pattern areas with a predetermined size for the de-identification area; and generating a second medical image by replacing the de-identification area with the de-identified image.

The invention discloses an intelligent detection method and device for fusing a medical image learning image, and relates to the technical field of medical image processing. The method comprises the following steps: acquiring and preprocessing a bimodal medical image, and extracting a feature map through multi-scale decomposition; constructing a cross-modal correlation model, and setting a modal attention mechanism (embedding anatomical structure prior guidance feature complementation) and a morphological attention mechanism (setting lesion morphological constraint weight); the method comprises the following steps: collecting multiple types of image samples, pairing according to a focus form and an imaging mode to construct a bimodal joint data set, and correlating and labeling to generate a training data set with modal attributes; after a multi-stage iteration training model, inputting the preprocessed image to carry out feature fusion so as to obtain a fused image; and generating a lesion probability graph according to the fused image, positioning a lesion area through multi-threshold segmentation, and outputting a detection result. The system comprises a data acquisition module, a preprocessing module and the like. The method improves the accuracy and reliability of medical image detection, and is suitable for clinical multi-modal image analysis.

Disclosed are a method of anonymizing medical images, the method comprising: acquiring or receiving a first medical image; separating an identification area containing anatomical structure information and a de-identification area containing a skin area in the first medical image; generating a de-identified image including a plurality of pattern areas with a predetermined size for the de-identification area; and generating a second medical image by replacing the de-identification area with the de-identified image.

The invention relates to the field of medicine, and discloses a thyroid nodule surgical operation planning and navigation system based on artificial intelligence, which comprises a multi-modal medical image acquisition module, an image preprocessing and enhancing module, an intelligent anatomical structure analysis module, an operation planning decision module and an intraoperative real-time navigation module, the multi-modal medical image acquisition module is used for integrating data of various image equipment such as ultrasonic equipment, CT equipment and MRI equipment, and space-time consistency of multi-modal images is ensured. According to the thyroid nodule surgical operation planning and navigation system based on artificial intelligence, the real-time calculation problem is solved, the system delay is reduced, which is crucial to real-time navigation in an operation, the real-time performance and the operation efficiency are improved, the operation time is shortened, and complications are reduced; by integrating the advantages of different images, the system can provide more comprehensive dissection views, help doctors to more accurately position nodules and important structures and avoid injury to nerves and blood vessels, so that the accuracy is improved, and the surgical risk is further reduced.

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.

The invention relates to the field of medical image processing, in particular to a lung cancer PET-CT fusion segmentation method and system based on multi-modal feature comparative learning, and the method comprises the steps: firstly extracting PET and CT image features, projecting the features to a shared semantic space through a semantic guide type symmetric comparative learning architecture, obtaining key region features through a focus adaptive attention sampling mechanism, and carrying out the segmentation of a target region; optimizing feature representation through a cross-modal feature difference self-calibration mechanism, constructing a multi-scale feature pyramid, fusing features of different scales by using a multi-scale hierarchical contrast learning mechanism, and performing self-supervised learning by combining an anatomical guidance self-supervised contrast learning enhancement module and using a CT anatomical structure, so as to further reinforce the features; a high-precision lung cancer lesion segmentation result is generated through a decoder network, the Dice coefficient is increased from 0.78 to 0.91, and the detection rate of lesions below 10 mm is increased from 65% to 87%. A novel efficient and accurate image processing method is provided for lung cancer diagnosis.

Co-manipulation robotic systems are described herein that may be used for assisting with laparoscopic surgical procedures. The co-manipulation robotic systems allow a surgeon to use commercially-available surgical instruments while providing benefits associated with surgical robotics. Further, the co-manipulation robotic system may operate in multiple modes to enhance usability and safety, while allowing the surgeon to position the instrument directly with the instrument handle and further maintain the desired position of the instrument or a desired force applied by the instrument using the robot arm. For example, the robot arm may be moved automatically to maintain a constant tension force on an anatomical structure via the instrument in a constant tension mode.

The invention provides a medical image segmentation method based on geometry and topology cooperative guidance. The medical image segmentation method comprises the following steps of image preprocessing and data enhancement; a shared encoder; a dual-path cooperative decoder; carrying out multi-mode deformation iterative refining; and a multi-objective composite loss function and an optimization strategy. The method has the beneficial effects that the performance can be remarkably improved: through a unique geometry and topology collaborative refining mechanism, the segmentation precision and the boundary definition are far superior to those in the prior art, the topology correctness of an anatomical structure can be actively maintained and repaired, clinically unacceptable errors are remarkably reduced, and the reliability of a result is improved; in addition, operation can be simplified, stability and generalization are enhanced, and advanced application is promoted.

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

The invention relates to the technical field of multi-modal medical image registration optimization methods, in particular to a medical image registration optimization method and system, and the method comprises the following steps: inputting a medical image data set comprising an MRI image data set, a CT image data set and a PET image data set into a trained deformable registration network; obtaining a deformation field by using the deformable registration network; obtaining a final registration result of the two groups of medical image data based on the deformation field, obtaining an optimized final registration result through an iterative optimization algorithm, and obtaining a quality evaluation coefficient at the same time; constructing an optimized objective function by using the quality evaluation coefficient, and realizing the adjustment of the hyper-parameter of the deformable registration network through the back propagation of the optimized objective function; prediction registration is carried out by using the deformable registration network after hyper-parameter adjustment, so that an optimized registration result is obtained, the position and the shape of a convolution kernel are adaptively adjusted based on a feature extraction network of deformable convolution, and nonlinear deformation of a local anatomical structure is better captured.

The invention discloses an image registration and segmentation joint optimization method, system, device and medium, and relates to the technical field of image registration and segmentation, and the method comprises the steps: obtaining an original medical image, and employing a dynamic pairing strategy to obtain a floating image and a fixed image; the method comprises the following steps: constructing an image registration and segmentation joint optimization model, wherein the model comprises an encoder sharing weight, a segmentation decoder, a registration decoder and an uncertainty estimator; and inputting the floating image and the fixed image into an image registration and segmentation joint optimization model to obtain a registered image and a tag thereof, inputting the fixed image and the registered image into an uncertainty estimator to predict the uncertainty of registration, and minimizing a loss function to obtain an updated image registration and segmentation joint optimization model. According to the method, the problem of class imbalance of different anatomical structure labels can be relieved, and the forward action of the layered segmentation feature map in the deformation subfield generation process can be promoted.

Systems and methods are provided for image guided surgery. Such systems and methods receive a model of an anatomical structure as well as a model of an object, and process a projection image generated by an imaging device from an imaging direction, wherein the projection image includes at least a part of the anatomical structure and at least a part of the object. Based on (i) the projection image, (ii) the imaging direction, (iii) the model of the object, and (iv) the model of the anatomical structure, the systems and methods determine a spatial position and orientation of the object relative to a space of movement. The space of movement may be defined in relation to the anatomical structure.

Systems and methods are disclosed for creating an interactive tool for determining and displaying a functional relationship between a vascular network and an associated perfused tissue. One method includes receiving a patient-specific vascular model of a patient's anatomy, including at least one vessel of the patient; receiving a patient-specific tissue model, including a tissue region associated with the at least one vessel of the patient; receiving a selected area of the vascular model or a selected area of the tissue model; and generating a display of a region of the tissue model corresponding to the selected area of the vascular model or a display of a portion of the vascular model corresponding to the selected area of the tissue model, respectively.

The invention provides a tumor type bone defect reconstruction method and system. The method comprises the following steps: acquiring bone defect information; constructing a bone defect restoration; constructing a bone defect three-dimensional model; and bone defect simulation surgery. According to the method, the bone defect information is obtained by evaluating the illness state of the patient, meanwhile, the anatomical structure of the bone tumor of the patient is subjected to structural evaluation through modern medical imaging, and the bone defect restoration is constructed in combination with the corresponding bone defect information; then, according to preset prosthesis design data, carrying out repair design on the bone defect prosthesis and obtaining three-dimensional skeleton data of a patient in real time, then, according to the obtained three-dimensional skeleton data, constructing a three-dimensional bone defect model and carrying out a bone defect simulation operation, and finally, predicting and optimizing the implantation position, direction and angle of the bone defect prosthesis. The effect of improving the construction accuracy of the bone defect prosthesis is achieved, and the problem that in the prior art, the construction accuracy of the bone defect prosthesis is low is solved.

The invention relates to the technical field of medical image processing, in particular to an artificial intelligence assisted intraoperative imaging method, which comprises the following steps: S1, preprocessing a multi-modal medical image, segmenting and recognizing an anatomical structure by a deep learning model according to the preprocessed image, measuring anatomical parameters based on a segmentation and recognition result, and generating an operation planning path by artificial intelligence according to the anatomical parameters; s2, collecting a C-shaped arm perspective image stream in real time, dynamically tracking space coordinates of a surgical instrument, comparing the position of the instrument with a surgical planned path, calculating offset, and when the offset is greater than an offset threshold, outputting correction guidance through an AR superposition layer; and S3, monitoring an image quality index in real time, dynamically adjusting exposure parameters through a reinforcement learning model, and when a metal implant is detected, switching a dual-energy-spectrum mode and executing an artifact suppression algorithm. According to the method, preoperative precise planning and intraoperative assistance are realized through artificial intelligence, the problems of poor image quality and high radiation risk are solved through technical optimization, and the method has important clinical application value.

The embodiment of the invention discloses an image annotation method and system applied to brain MRI image segmentation, and the method comprises the steps: obtaining a brain MRI image data set of a target object, and the brain MRI image data set comprises original image sequences of a plurality of scanning levels; performing multi-modal feature fusion processing on the original image sequence to generate an enhanced image feature set; calling a multi-layer cascade segmentation network to perform hierarchical feature extraction on the enhanced image feature set to obtain a multi-scale anatomical structure feature map; and performing region boundary optimization processing based on the multi-scale anatomical structure feature map, and generating a marked brain structure segmentation image. Therefore, the boundary of each structure of the brain can be accurately defined, the segmented image is more accurate and clearer, and the image segmentation and marking of the brain MRI image can be accurately and clearer realized.

The invention provides a liver focus three-dimensional modeling method, and belongs to the technical field of image processing based on computer vision. Firstly, a multi-view spatial registration method based on optical flow optimization is designed, pixel-level displacement information of different view images is estimated by calculating an optical flow field, accurate image alignment is achieved, and spatial consistency of three-dimensional reconstruction is improved. And secondly, a three-dimensional reconstruction strategy based on two-dimensional focus segmentation is proposed, the two-dimensional focus segmentation is completed by adopting a lightweight U-Net variant, and a segmentation result is mapped to a three-dimensional space through a voxel probability projection method, so that 3D focus reconstruction is realized, and the calculation cost is reduced. And finally, extracting high-frequency features of the three-dimensional model by adopting a local edge enhancement method based on a Laplacian operator, and strengthening a focus boundary and a key anatomical structure through interpolation optimization, so that the three-dimensional model is more accurate and clearer. Compared with a traditional method, the method has the advantages that the mode of purely depending on image superposition is avoided, and the accuracy of three-dimensional modeling is improved.