54 results about "Brain ct" patented technology

The invention relates to the technical field of internet big data, in particular to a cerebral hemorrhage space positioning and prognosis prediction network system based on three-dimensional point cloud. The cerebral hemorrhage space positioning system comprises an image acquisition module used for acquiring a brain CT image to be processed; the offset correction module is used for performing offset correction on the brain CT image to obtain a corrected brain CT image; the hematoma region segmentation model is used for segmenting the corrected brain CT image to obtain a hematoma region; the point cloud calculation module is used for calculating three-dimensional space coordinates of hematoma voxels in a hematoma area in a brain coordinate system based on the corrected brain CT image and taking the three-dimensional space coordinates as point cloud data of the hematoma area; and the point cloud data output module is used for performing normalization processing on the point cloud data of the hematoma area to obtain hematoma three-dimensional point cloud data representing the hematoma three-dimensional space position. According to the method, an accurate and comprehensive data basis is provided for cerebral hemorrhage prognosis prediction, and the accuracy and comprehensiveness of subsequent cerebral hemorrhage prognosis prediction are improved.

The invention relates to an automatic detection method for a stroke focus based on computer vision, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring brain CT images, and constructing a training data set; self-adaptive adjustment and standardization of the window width and the window level of the brain CT image are carried out through a self-adaptive window width and window level adjustment strategy; processing the standardized brain CT image through a multi-scale non-uniform region-of-interest sampling strategy; constructing a stroke focus automatic detection model; carrying out double-flow convolution feature extraction and fusion by adopting a double-flow encoder network; processing the fusion feature map through a density perception space attention module; calculating a focus boundary perception loss function, adopting a gradient weighting strategy to carry out gradient adaptive optimization, and updating model parameters through an optimizer to obtain a trained model; and inputting a region-of-interest image block obtained by processing a newly collected brain CT image into the trained model to obtain a final detection result. The accuracy of stroke focus detection can be improved.

The invention discloses a DSA image coronary artery interlayer automatic identification system based on U-KAN and anatomy prior. The DSA image coronary artery interlayer automatic identification system comprises a data preprocessing module, an anatomy prior information extraction module, a U-KAN model construction module, a model training module, an interlayer area automatic identification module and a result processing and evaluation module. According to the method, the KAN layer is introduced, so that the expression capability and the recognition precision of the U-Net model on complex medical images (such as DSA images) are remarkably enhanced. According to the method, anatomical prior information (such as geometric morphology of coronary artery, vascular branches and the like) is creatively fused into the deep learning model, so that the sensitivity of the model to a lesion area is improved. According to the invention, through the optimized U-KAN network, the identification precision is ensured, and the calculation efficiency is high. The U-KAN model provided by the invention has strong expansibility, can be applied to automatic identification of coronary artery interlayer, and can also be applied to other types of medical image analysis tasks, brain CT and automatic detection and segmentation of tumor MRI images.

The invention discloses an image processing and simulation analysis method for a carotid artery image, which comprises the following steps: firstly, acquiring a CTA image, observing and selecting, then importing the CTA image, adjusting the image contrast, generating a three-dimensional blood vessel model, extracting a carotid artery focus, and carrying out overall and local surface optimization processing on the model. The method comprises the steps that firstly, a model is built, then local surface cutting naming and grid division are conducted on the model, finally parameters are set, CFD hemodynamic simulation is conducted, a simulation result is subjected to visualization processing, and preoperative and postoperative data are compared and analyzed. According to the method, multi-modal imaging data of brain CT perfusion imaging, brain CT angiography and the like of a patient suffering from carotid artery stenosis can be collected to be used for reconstructing a three-dimensional model of the carotid artery stenosis, the carotid artery hemodynamic characteristics of the patient and the individual characteristics of the patient are analyzed in an auxiliary mode based on computational fluid mechanics, operation mode selection is optimized, and the three-dimensional model of the carotid artery stenosis is reconstructed. Cerebral ischemia reperfusion injury is avoided, clinical prognosis of a patient is improved, and life quality of the patient is improved.

The invention relates to the technical field of image processing, and relates to an AI-based image segmentation method and device, and the method comprises the steps: obtaining a brain CT image sequence, a brain MRI image sequence, and a brain MRA image sequence; the brain CT image sequence, the brain MRI image sequence and the brain MRA image sequence are subjected to feature extraction, first feature information, second feature information and third feature information are obtained, the first feature information comprises three-dimensional structure feature information of brain blood vessels, and the second feature information comprises brain tissue distribution feature information; fusing the first feature information, the second feature information and the third feature information to obtain fused feature map information; enhancing the fused feature map information to obtain enhanced feature map information; and according to the enhanced feature map information, the microvessels in the brain are segmented, segmented image information is obtained, the features of multiple modes are integrated, and the segmentation precision of the brain microvessels is effectively improved.

The application provides a child skull fracture automatic detection method and device based on generated data augmentation. The method comprises the following steps: acquiring a first child brain CT image set, and performing pixel labeling and preprocessing to obtain fracture data, suture data and normal data; obtaining a trained fracture lesion generation model and a suture generation model according to the fracture data, the suture data and the normal data; randomly sampling Gaussian noise for multiple times, inputting the Gaussian noise into the fracture lesion generation model and the suture generation model to obtain generated fracture CT patch data and generated suture CT patch data; training a candidate fracture detection model according to the fracture data, the suture data, the normal data, the generated fracture CT patch data and the generated suture CT patch data, and outputting a fracture detection model; and acquiring a second child brain CT image, and detecting the second child brain CT image according to the fracture detection model. The fracture lesion data and the suture data are augmented, so that the resolution of fractures and sutures can be improved.

The invention discloses a generative artificial intelligence cerebral artery lesion detection method based on plain-scan brain CT. The method comprises the steps that 1, a plain-scan brain CT image to be processed is acquired; 2, converting the plain scanning brain CT image into a CTA image through an adaptive noise elimination network; and 3, carrying out multi-modal lesion detection on the basis of the CTA image generated in the step 2. According to the method, a CTA image is generated through an adaptive noise elimination network (ANE-NET), and a real-time lesion feature analysis engine (RTAL-FE) is embedded in the generation process, so that real-time classification prediction of lesion types is realized. And furthermore, a detection result is output through a dynamic weight decision model (DWD-M), so that the generation quality and the detection efficiency are remarkably improved. The problems that a traditional method is low in generation quality and lags behind detection are solved, and the method is particularly suitable for low-dose and non-invasive cerebrovascular disease screening scenes and has important clinical application value.

The present invention relates to the field of image processing technology, and in particular to a multi-plane cranial CT reconstruction method for patients with cerebral venous thrombosis, comprising: obtaining a closed area of ​​each plane of a CT image of a patient's CT scan; obtaining an area to be analyzed on each plane according to the shape distribution and grayscale distribution of each closed area of ​​each CT image; dividing the area to be analyzed on each plane according to the angle distribution of the area extension lines between the area to be analyzed on each plane and the area to be analyzed on the adjacent plane, and obtaining suspected lesion areas on each plane; performing edge matching on the suspected lesion areas on each plane and the adjacent plane according to the distance distribution and relative position distribution between the edge pixels of the suspected lesion areas on each plane and the edge pixels of the suspected lesion areas on the adjacent plane; and performing three-dimensional reconstruction of the patient's cranial CT according to the matching results. The present invention improves the effect of three-dimensional reconstruction of cranial CT.

A method for deriving dementia-related information using volume predicted from brain CT includes: a step of an analysis apparatus receiving a brain CT (Computed Tomography) image of a subject; a step of the analysis apparatus inputting the brain CT image into a pre-trained segmentation model to extract regions of interest; a step of the analysis apparatus inputting pixel information of the regions of interest into a pre-trained first learning model to predict the volume of at least one region among the regions of interest; and a step of the analysis apparatus inputting the volume of the at least one region into a pre-trained second learning model to derive dementia-related information of the subject.

The invention relates to the technical field of image analysis, in particular to a CT image intelligent recognition and analysis system based on deep learning, and the system comprises an uploading module which is used for uploading a brain CT image of a diagnosed patient and marking the brain CT image of the patient; the importing module is used for importing the patient brain CT image which is uploaded and marked in the uploading module into a ResNet model for training; according to the method, the prior patient brain CT image is imported into the deep learning model for training, so that intelligent diagnosis is provided for the patient through the deep learning model and the patient brain CT image, the reception pressure of medical staff is effectively relieved, and the diagnosis efficiency is improved when the patient is diagnosed through the deep learning module. According to the method, the name of the disease of the patient can be accurately output, matching is carried out based on prior data, a recommended treatment scheme is provided for the patient, and the convenience of diagnosis and treatment of brain diseases at the present stage is effectively improved.

The application provides a neurosurgical brain puncture operation robot and electronic equipment, in the neurosurgical brain puncture operation robot, a control host is used for matching a brain CT image to be planned with an intracranial hematoma puncture path database constructed to determine a hematoma puncture planning path, and the mechanical arm is controlled according to the hematoma puncture planning path to drive the surgical instrument to reach the hematoma position of the brain.In the embodiment of the application, only the CT image scanned in the diagnosis process is used as the brain CT image to be planned, and no other additional image is needed, and the intracranial hematoma puncture path database constructed is matched, so that the planning of the intracranial hematoma puncture path is completed only using the brain tissue in a short time, the planning efficiency is improved, and moreover, the planning process does not need additional manual operation, and the implementation difficulty is smaller for young doctors and primary doctors with less clinical experience, and on this basis, the treatment efficiency of the patient with cerebral hemorrhage is effectively improved.

The embodiment of the invention provides a simulation method and device for craniocerebral growth and a storage medium, and belongs to the technical field of images. The method comprises the following steps: constructing three-dimensional brain models in one-to-one correspondence with standard brain CT sample images corresponding to different growth time periods; taking the three-dimensional brain model corresponding to an initial time period in different growth time periods as an initial simulation model for segmentation to obtain a plurality of brain model components; performing finite element solution of growth parameters on a gridding three-dimensional craniocerebral model obtained by gridding each craniocerebral model component to obtain a predicted craniocerebral model of the next growth time period; obtaining a first target growth parameter corresponding to the current growth time period according to the predicted craniocerebral model and the three-dimensional craniocerebral model in the same growth time period; and taking the predicted craniocerebral model as an initial simulation model to obtain a first target growth parameter of the next growth time period. According to the embodiment of the invention, the simulation efficiency and simulation precision of skull simulation can be considered.

The disclosure provides a kind of cerebral hemorrhage analysis method and system based on image registration, comprising: automatically segmenting cerebral hemorrhage area in brain CT image;The cerebral CT image and the segmented cerebral hemorrhage image are pretreated;Cerebral CT image is registered according to MRI template, and the deformation field is obtained;The cerebral hemorrhage image is transformed using the deformation field;Analysis patient bleeding level and the brain area involved.Use normal brain MRI image as registration template, register patient brain CT with MRI template, extract patient hematoma feature, can quickly and effectively analyze bleeding level and the brain area involved by hematoma, provide guidance for doctors, and can be used for subsequent visualization modeling, help doctors to formulate more comprehensive treatment plan.

The application discloses an abnormality recognition method based on brain CT images, and relates to the technical field of image processing.The method comprises the following steps: acquiring a brain CT image sequence and extracting an imageomics feature, and constructing an initial imageomics feature set; analyzing the dynamic evolution mode of the feature in the image sequence, and obtaining an imageomics trajectory dynamic coefficient by calculating the entropy value and standard deviation of the feature change rate sequence; constructing a brain structure-function heterogeneous graph, inputting the heterogeneous graph into an abnormality recognition model constructed based on a graph neural network, and outputting a graph-level semantic embedding vector; performing similar case matching, abnormality type determination and key brain area positioning based on the vector, and generating a structured abnormality recognition report.The application solves the problems of insufficient utilization of dynamic information of brain CT images, lack of fusion of anatomical structure prior knowledge and poor result interpretability in the prior art, and improves the accuracy, interpretability and clinical applicability of brain abnormality recognition.

The invention relates to the technical field of medical image processing, in particular to a patient brain CT image intelligent calibration system and method based on big data. A density gradient analysis unit is subjected to self-adaptive partitioning according to brain anatomical characteristics, and through three-dimensional direction gradient detection and Laplacian differential operator dual-scale fusion, a brain CT image is obtained; calculating the density gradient value of each region; the displacement compensation weight unit depends on an association rule model trained by big data, a continuous weight field is generated through two-stage decision, and a specific compensation coefficient is loaded to a key area; the image calibration execution unit adopts a differentiation algorithm to carry out sub-pixel-level non-rigid correction on a high-density mutation region and carry out rigid correction on a homogeneous region; and a partition correction result is integrated, so that the influence of head micromotion is effectively counteracted, tissue boundary blur is eliminated, the brain CT image definition and structural accuracy are improved, and reliable support is provided for clinical diagnosis.

The application discloses a middle cerebral artery high-density sign identification method, device, equipment and storage medium. In the identification method, first, the middle cerebral artery region of a brain CT plain scan image is extracted to obtain a region extraction image; then, according to a blood vessel enhanced image corresponding to the region extraction image, position information of a middle cerebral artery high-density sign candidate box is obtained; then, texture features are obtained according to the region extraction image and the position information, and shape features are obtained according to the blood vessel enhanced image and the position information; finally, a middle cerebral artery high-density sign identification result is obtained according to the texture features and the shape features. The application can realize accurate and reliable identification of HMCAS in a complex brain lesion environment, and has strong adaptability to scanning equipment and individual imaging differences.

The invention discloses a system, a method, equipment and a medium for generating diffusion from craniocerebral CT to craniocerebral CTA under self-adaptive characterization guidance, and belongs to the technical field of medical image processing and artificial intelligence. The system comprises an adaptive representation extraction module used for extracting a depth feature vector representing a blood vessel and an anatomical structure from an input non-enhanced CT image; the conditional potential diffusion model is used for executing a de-noising diffusion process in a potential space by fusing U-Net of a cross attention mechanism under the condition of the feature vector to generate target feature representation; and the image reconstruction module is used for decoding and reconstructing the potential features into a synthetic CTA image. According to the method, the diffusion generation process is guided through the self-adaptively extracted anatomical priori depth, high-quality and high-anatomical-consistency three-dimensional craniocerebral CTA image synthesis without an iodine contrast agent is realized, and the defects of dependence on the contrast agent, inaccurate blood vessel reduction and poor three-dimensional continuity in the prior art are effectively overcome.

The invention discloses a brain tumor CT image classification system based on a neural network. The brain tumor CT image classification system comprises a data preprocessing module, a brain CT image classification neural network construction module, a brain tumor CT image classification neural network training module and a brain tumor CT image classification output module. An advanced data enhancement technology is adopted, and the advanced data enhancement technology comprises operations such as random rotation, overturning, zooming and cutting. The operations improve the adaptability of the model to image transformation by simulating different image transformation. The model optimizes the automatic classification of brain tumor CT images. By introducing residual connection, the model can effectively alleviate the gradient disappearance problem in the deep network, and the hybrid attention mechanism enhances the capturing capability of the global dependency relationship of the image. Compared with a traditional CNN model, the system provided by the invention can extract local features and global features more accurately, and significantly improves the classification accuracy of brain tumor CT images.

The application relates to a method for obtaining a perfusion parameter map and a lesion area and a computer readable storage medium, and the method comprises the following steps: obtaining three-dimensional dynamic CT perfusion images based on brain CT perfusion images at different time points; performing filtering processing on each voxel in the three-dimensional dynamic CT perfusion images to obtain filtered three-dimensional dynamic CT perfusion images, wherein the filtering processing comprises the following steps: at different time points, for each current voxel, filtering is performed by using spatial similarity and time intensity similarity of adjacent voxels of the current voxel; and obtaining a perfusion parameter map and a lesion area according to the filtered three-dimensional dynamic CT perfusion images. The application filters by using spatial similarity and time intensity similarity, considers not only spatial similarity information but also time information of continuous scanning CT perfusion images, improves the signal-to-noise ratio of the images and the calculation accuracy, and the obtained perfusion parameter map and lesion area in the subsequent process are more reliable.

According to the novel imaging method for achieving brain tumor functional area occupation based on the mixed reality technology, anatomical relation imaging of a brain tumor and brain functional areas, nerves and blood vessels around the brain tumor can be achieved, precise positioning navigation and precise excision in an operation are achieved, and damage to the brain functional areas, the nerves and the blood vessels around the brain tumor is reduced to the maximum extent. Comprising the following steps: (1) acquiring a brain CT examination data source, and outputting the brain CT examination data source in a DICOM format; (2) acquiring a brain MRI examination data source, and outputting the brain MRI examination data source in a DICOM format; (3) acquiring a cerebrovascular CTA examination data source, and outputting the cerebrovascular CTA examination data source in a DICOM format; (4) registering the CT, MRI and CTA of the same patient to unify a coordinate system; (5) completing a brain reconstruction three-dimensional virtual model; and (6) generating the modeling result into a two-dimensional code or importing the modeling result into mixed reality glasses for result publishing.

The present invention relates to a collaborative cross segmentation system for cerebral hemorrhage and surrounding edema based on automatically generated labels. The system comprises: a preprocessing module, which preprocesses the collected 3D cranial CT images; a label image generation module, which automatically generates label images for the preprocessed 3D brain parenchyma CT images; a supervised label image generation module, which performs label fusion processing on the label images to obtain supervised label images; a training module, which uses the supervised label images to train a collaborative cross segmentation model for cerebral hemorrhage and surrounding edema based on deep learning; and a measurement module, which uses the deep learning segmentation results to perform morphological measurements and present the results. The collaborative cross segmentation model comprises two hemorrhage segmentation networks and a surrounding edema segmentation network with the same structure, the encoder of the hemorrhage segmentation network is cross-connected with the decoder of the surrounding edema segmentation network, and the encoder of the surrounding edema segmentation network is cross-connected with the decoder of the hemorrhage segmentation network.

The present invention relates to a collaborative cross segmentation system for cerebral hemorrhage and surrounding edema based on automatically generated labels. The system comprises: a preprocessing module, which preprocesses the collected 3D cranial CT images; a label image generation module, which automatically generates label images for the preprocessed 3D brain parenchyma CT images; a supervised label image generation module, which performs label fusion processing on the label images to obtain supervised label images; a training module, which uses the supervised label images to train a collaborative cross segmentation model for cerebral hemorrhage and surrounding edema based on deep learning; and a measurement module, which uses the deep learning segmentation results to perform morphological measurements and present the results. The collaborative cross segmentation model comprises two hemorrhage segmentation networks and a surrounding edema segmentation network with the same structure, the decoder of the hemorrhage segmentation network is cross-connected with the decoder of the surrounding edema segmentation network, and the decoder of the surrounding edema segmentation network is cross-connected with the decoder of the hemorrhage segmentation network.

The application relates to a kind of brain CT perfusion image full-automatic post-processing methods, comprising: reading CT perfusion image;The CT perfusion image is preprocessed, and the skull perfusion image with skull is obtained, and the brain tissue image containing lateral ventricle after removing skull;Obtain lateral ventricle segmentation result binary graph, comprising: using the skull perfusion image with skull, obtain the optimal image layer of the largest connected domain area in skull;In the brain tissue image, from the optimal image layer, find the first distance towards the direction of skull top, find the second distance towards the direction of skull bottom, obtain the image layer containing lateral ventricle;The image layer containing lateral ventricle is converted into feature image, and lateral ventricle segmentation is carried out on each layer feature image, and lateral ventricle segmentation result binary graph is obtained;Using the brain tissue image, arterial input function and perfusion parameter map are sequentially obtained;The perfusion parameter map is binarized, and the lesion area is obtained in combination with the lateral ventricle segmentation result binary graph.

The utility model provides a shelter CT ambulance. The shelter CT ambulance comprises a frame, the frame is provided with a cab and a treatment and examination cabin, one side of the treatment and examination cabin is provided with a right side single door, the tail of the treatment and examination cabin is provided with a double-open tail door, the side, away from the right side single door, of the treatment and examination cabin is provided with a left side single door, and the side, away from the left side single door, of the treatment and examination cabin is provided with a right side single door. An oxygen bottle, an oxygen cabinet, a busbar, an equipment fixing guide rail, a wall cupboard, an electric appliance control panel, an integrated communication system, an oxygen terminal and a plurality of alternating-current and direct-current sockets are arranged in the treatment and examination cabin, a brain CT transfer device is arranged in the treatment and examination cabin, and a brain CT machine is detachably mounted on the brain CT transfer device. The shelter CT ambulance has the advantages that the shelter CT ambulance can provide golden rescue time for apoplexy patients, and screening and rescue of the patients can be moved to a disease site.

The application relates to the technical field of medical image processing, and discloses an intracranial aneurysm recognition method and system based on a brain CT. The method obtains a brain CT image sequence, adopts an anisotropic diffusion filtering algorithm for pretreatment to suppress noise and retain blood vessel edge details. Subsequently, a region growing algorithm is used to segment an intracranial blood vessel region, and a skeletonization algorithm is used to extract a blood vessel center line to calculate curvature and diameter changes. Finally, based on curvature anomaly and diameter ratio analysis, combined with a multi-scale sliding window and a blood vessel topological structure verification, automatic recognition of intracranial aneurysms is realized, and the accuracy and reliability of diagnosis are improved.

The utility model relates to the technical field of examination beds, in particular to a CT examination bed which comprises a bed bottom plate, a head supporting mechanism is arranged at one end of the bed bottom plate, and a lifting and moving mechanism is arranged at the bottom end of the bed bottom plate. The head supporting mechanism comprises a connecting column, one end of the connecting column is fixedly connected with one end of the bed bottom plate, and the other end of the connecting column is fixedly connected to the head supporting frame. Through a head supporting frame, a bottom supporting plate and a side position plate which are arranged on the head supporting mechanism, the head of a patient is placed on the bottom supporting plate, a first adjusting screw rod is driven to rotate by rotating a first handle, and therefore the bottom supporting plate is driven to move up and down, and flexible adjustment can be conveniently conducted according to the heights of different head positions. A second handle is rotated to drive a second adjusting screw rod to rotate, so that a side plate is driven to move front and back, the side fixing effect of the head of the patient is achieved, the situation that the head shakes to affect the image quality and cause diagnosis errors during brain CT scanning of the patient is avoided, and the practical effect of the examining table is improved.

The invention discloses a three-dimensional brain CT medical visual question-answering method and system based on an anatomical memory matrix, and aims to solve the problem that an existing medical visual question-answering model is difficult to fully process association between three-dimensional brain CT image space continuity and an anatomical structure. According to the method, through explicit modeling of three-dimensional anatomical priori and cross-slice semantic association, the spatial understanding ability of the model for brain structures and pathological features is enhanced, and the accuracy and reliability of medical visual questions and answers in a three-dimensional brain CT scene are improved.

The application provides an acute cerebral stroke early intelligent screening early warning method and system based on deep learning, relates to the technical field of medical image processing, and comprises the following steps: acquiring patient brain CT images, historical medical records and cerebral vascular data, extracting density abnormality features and symmetry change features to judge lesions, constructing a multi-scale feature pyramid and recursively refining lesion boundaries, combining three-dimensional lesion feature maps and illness change time sequence features, adopting a self-attention mechanism and a variational autoencoder to analyze cerebral vascular tree structures, and finally generating a cerebral vascular function impairment probability early warning map, so that early accurate screening and early warning of cerebral stroke can be realized, and the timeliness of treatment is improved.

The invention discloses a baseline brain CT image processing method and platform and a storage medium. The platform comprises an image segmentation module used for obtaining a baseline brain CT image of a user and inputting the baseline brain CT image into a pre-trained segmentation model to obtain a brain parenchyma region bleeding image and a bleeding volume; the image vector construction module is used for inputting the brain parenchyma area bleeding image into a pre-trained radiomics model to obtain a radiomics score; constructing an image vector based on the radiomics score and the bleeding volume; the text vector construction module is used for extracting blood biochemical marker information and medical record information of the user and constructing a text vector; and the output module is used for inputting the image vector and the text vector into a pre-trained risk judgment model and outputting a risk score of the baseline brain CT image. According to the method, the baseline brain CT image can be automatically subjected to quantitative evaluation based on the image processing platform, and the cerebral hemorrhage expansion risk score is output for reference of the user.