273 results about "Vessel segmentation" patented technology

The invention provides a vascular calcification analysis method and device based on a non-enhanced CT image, and relates to the technical field of artificial intelligence. The method comprises the following steps: analyzing scanning parameter information of a non-contrast enhanced scanning image set; standardizing the non-contrast enhanced scanning image set based on the scanning parameter information to obtain a preprocessed image sequence; inputting the preprocessed image sequence into a multi-modal registration network, and generating a fused image based on the obtained key anatomical mark points; inputting the fused image into an attention-enhanced segmentation network, and determining blood vessel segmentation masks of the thoracic aorta, the common carotid artery and the intracranial artery; a candidate voxel set is extracted from the fused image based on the blood vessel segmentation mask, three-dimensional connected domain analysis is carried out on the candidate voxel set, and a blood vessel calcification three-dimensional area is determined; and inputting the vascular calcification three-dimensional region into the multi-task prediction model, determining a quantitative score of the vascular calcification degree, and improving the accuracy and reliability of a vascular calcification evaluation result.

The invention relates to the technical field of ophthalmologic image processing, and discloses a fundus blood vessel segmentation method based on multi-modal data. The method comprises the following steps: firstly, synchronously acquiring an optical coherence tomography image, a color fundus photographic image and a fluorescent angiography image to form multi-modal fundus data; preprocessing the eye fundus image data set to generate a standardized multi-mode eye fundus image data set; fusing heterogeneous features based on the data set, and constructing a multi-dimensional fundus feature space; using the space to train a deep learning segmentation network model, and generating an initial fundus blood vessel segmentation mask; carrying out confidence evaluation analysis on the initial mask to obtain a blood vessel segmentation result confidence distribution map; dividing blood vessel segmentation quality grades according to the distribution diagram, and defining a judgment rule; and performing interactive correction on the low-confidence region based on a rule to generate a final optimized fundus blood vessel segmentation result. And the final result is transmitted to a visual terminal for three-dimensional topology reconstruction. According to the method, the advantages of multi-modal data are integrated, and a more comprehensive fundus blood vessel segmentation result is formed.

The invention discloses a blood vessel segmentation method and device based on large model fine tuning, equipment and a medium, and the method comprises the steps: obtaining a blood vessel image, carrying out the preprocessing of the obtained blood vessel image, and obtaining a training set; performing fine tuning on a pre-trained image segmentation model by adopting the training set, so that the image segmentation model learns related features of blood vessel segmentation; wherein the low-rank adaptive fine tuning technology is adopted to perform fine tuning on the image segmentation model: two parallel low-rank matrixes are inserted into a Transform layer in the image encoder, the input is processed by the two low-rank matrixes in sequence, and the processing result and the original output are added to obtain the final output. According to the method, the defects of a large model in a blood vessel segmentation task are overcome through efficient parameter fine tuning and a multi-scale feature fusion module. Accurate blood vessel segmentation can be realized under limited annotation data, the difficulty and cost of medical image segmentation tasks are greatly reduced, and the method can be widely applied to the technical field of medical image processing.

According to various examples of the present disclosure, there is provided a machine learning image segmentation model for automatically identifying and segmenting structural features of a vessel tree from image frames. The model comprises a 3D encoder and 2D decoder, the 3D encoder and 2D decoder connected by at least one interlinked convolution node and a plurality of temporal extraction nodes therebetween. The model is configured to identify and segment structural features of a vessel tree from the plurality of image frames, by the model being configured to: receive a plurality of image frames as an input to the 3D encoder; provide an output of the 3D encoder as an input to the plurality of temporal extraction nodes to extract temporal information; generate, by the plurality of temporal extraction nodes, a 2D temporal output based on the extracted temporal information; provide the generated 2D temporal output to the at least one interlinked 2D convolution node and 2D decoder; generate, at the 2D decoder, a combined temporal output based on an output of the at least one interlinked 2D convolution node and at least one temporal extraction node, wherein the combined temporal output represents a predicted segmentation of the vessel; and generate an output representative of the segmented structural features of the vessel tree based on the predicted segmentation.

The invention provides a low-dose CTA image noise reduction model training method, a noise reduction method and equipment. The method comprises the following steps: acquiring cardiovascular and edge noise enhancement data of a low-dose CTA image based on a cardiovascular segmentation model and an edge extraction model; according to the data and the conventional dose CTA image, a cyclic generative adversarial network is trained based on target loss so as to train the cyclic generative adversarial network into a low dose CTA image noise reduction model, and the target loss comprises cardiovascular maintenance loss obtained based on a cardiovascular segmentation model and edge maintenance loss obtained based on an edge extraction model. According to the invention, the blood vessel and edge enhancement and the global noise reduction of the low-dose CTA image can be combined in the training process of the low-dose CTA image noise reduction model, so that the model can give consideration to the reservation of the fine structure of the image and the improvement of the overall image quality. Therefore, the application effectiveness and reliability of a low-dose CTA image noise reduction result obtained by adopting the model can be effectively improved, and the reliability of CTA clinical diagnosis is improved.

The invention relates to the technical field of medical image segmentation, in particular to a medical image segmentation method combining a convolutional neural network and a graph network, and the method comprises the following steps: forming a data set CoronarySet by using coronary artery CT angiography three-dimensional image data collected by a computed tomography (CT) device, and randomly dividing the data set CoronarySet into a training set CoronarySet 1 and a test set CoronarySet 2; the method comprises the following steps of: segmenting data of a training set CoronarySet1 into a plurality of patches; inputting the patchings into a double-branch parallel encoder to obtain a multi-layer coronary artery texture feature map and a multi-layer coronary artery topological feature map; constructing a feature fusion module, and fusing the texture feature maps and the topological feature maps of different layers based on an attention mechanism module to obtain fused blood vessel features; shallow layer features of a three-dimensional convolution module and fused blood vessel features are sequentially input into each layer of a segmentation decoder, a three-dimensional blood vessel structure can be extracted, and a blood vessel segmentation result is more continuous and more accurate.

The invention relates to the technical field of operation planning, in particular to an adaptive drainage basin segmentation and operation planning system based on deep learning, which comprises a chest imaging data modal layer used for acquiring chest imaging data modal information including lung lobes, lung segment boundaries and lung blood vessels; the end-to-end multi-task deep learning model layer is in communication connection with the chest imaging data modal layer and comprises a blood vessel segmentation module used for performing blood vessel segmentation on the chest imaging data modal; the hierarchical drainage basin segmentation module is used for carrying out hierarchical drainage basin segmentation based on the blood vessel segmentation result; the topological basin network module is used for analyzing the topological relation of the vascular structure; the drainage basin visualization module is used for generating visualization results of lung lobes and lung segments; the path planning module is used for generating an operation scheme; the evaluation and decision-making module is used for carrying out evaluation and decision-making analysis on the operation scheme; according to the invention, full-process automatic processing from medical image input to surgical scheme output is realized.

The invention discloses a three-dimensional blood vessel image segmentation method and system. Belongs to the technical field of medical image processing and particularly relates to the technical field of three-dimensional blood vessel image segmentation. The method solves the following problems existing in a blood vessel segmentation task in an existing method: small blood vessel features are difficult to accurately extract in a CTA image which is low in contrast and contains noise and artifacts; global context modeling is difficult to consider and local correlation is difficult to guarantee, so that long-distance dependent modeling is insufficient or a local structure is fractured; limited by a fixed geometrical shape of a traditional convolution kernel, the traditional convolution kernel is difficult to adapt to deformation characteristics of a complex topological structure of a blood vessel, resulting in discontinuous segmentation or fuzzy boundary of a branch region. Channel dynamic grouping and energy-driven attention generation are achieved through a grouping self-adaptive attention module, and self-adaptive modeling of a blood vessel complex branch structure and a geometrical shape is achieved through a multi-scale space structure aggregation module in combination with a strip-shaped deformable convolution and cross-scale guiding mechanism.

The invention belongs to the technical field of medical image processing, and discloses a blood vessel segmentation reconstruction and bleeding analysis method and device. In the bleeding area detection model, detecting whether a bleeding area exists or not; and performing blood vessel segmentation and blood vessel three-dimensional reconstruction operation. According to the method, the time sequence image is adopted for comprehensive analysis, and the problems that blood vessel details are lost due to insufficient diffusion of a contrast agent and the image is blurred due to excessive diffusion are effectively solved. Based on an 8-neighborhood pixel tracking method, accurately identifying blood vessel cross points, and performing segmentation processing on the skeleton line by using the cross points. By constructing the topological structure of the blood vessel, the feature point matching efficiency is greatly improved, and the automatic three-dimensional reconstruction process is also realized. By introducing the architecture design of the hybrid convolutional neural network and the state space model, the local fine-grained feature extraction capability and the global long-range dependence modeling capability are effectively combined, so that the segmentation precision and robustness of the intracranial hemorrhage region are remarkably improved.

The invention discloses a retinal vessel segmentation method based on a double-space attention U-net model, and belongs to the field of computer vision and medical image processing. According to the method, on the basis of the U-Net network, a traditional convolution operation is replaced by traditional convolution + dynamic Snake Convolution (DE-Conv), so that the capturing capability of complex structures such as small curved blood vessels is remarkably enhanced. Meanwhile, a double space residual attention (DSRA) mechanism is provided, features are weighted in two dimensions of a channel and a space, and the overall performance of the segmentation model is improved. According to the technical scheme of the invention, by improving the feature extraction and attention distribution strategy of the network, the method fully adapts to the characteristics of micro structures such as retinal blood vessels, and significantly improves the segmentation precision and topological continuity. Experiments show that compared with an existing method, the method shows better accuracy, continuity and stability in a blood vessel segmentation task, and is suitable for various medical image analysis scenes such as clinical diagnosis and hemodynamic simulation.

The invention discloses a retina image unsupervised anomaly detection method for early screening of diabetes mellitus. The method comprises the following steps: carrying out registration and multi-scale attention-guided blood vessel segmentation on a longitudinal time sequence retina image of a patient; extracting a vascular skeleton and constructing a time sequence vascular topological graph, calculating geometric morphology and hemodynamic attributes of each vascular segment, identifying vascular morphology evolution characteristics by comparing topological graphs of adjacent time points, and calculating hemodynamic characteristics such as wall shear stress through simulation; the evolution and hemodynamic characteristics are jointly input into a time sequence encoder for unsupervised learning, and an early diabetes risk score is comprehensively generated by analyzing a reconstruction error, an abnormal score based on density estimation and a time sequence trajectory deviation degree of a potential space; the scheme of the invention does not depend on lesion labels, and can sensitively detect the tiny anomalies at the early stage of pathology from multi-dimensional dynamic changes, thereby providing an objective and quantitative new way for early screening and intervention of diabetes.

The invention provides a blood vessel image segmentation method, device and equipment based on a differential learning model, and relates to the field of neural network and image processing. According to the method, 3D blood vessel original image sets of different modalities are obtained and input into a blood vessel image segmentation model, and in the segmentation model, a reconstruction image embedded with modal features is obtained by using a modal difference marking method; inputting the reconstructed image into a 3D UNet encoder to extract low-level features; according to the low-level features, high-level features are generated through a position encoder and a self-attention mechanism; expanding the advanced features into one-dimensional vectors through a linear layer, and optimizing the one-dimensional vectors by adopting a Transform encoder to generate optimized features; secondly, calculating difference features between modals by means of element-by-element subtraction of the features after Transform optimization, so as to obtain difference fusion features; the difference fusion features are remodeled through a Transform encoder, and a final blood vessel segmentation result is obtained. According to the invention, morphological characteristics of small blood vessels can be accurately captured, and the blood vessel image segmentation precision is effectively improved.

Methods and systems for manually assisted definition of vascular features are described. In some embodiments, a method provides for editing of vascular paths by enabling a user to drag an erroneously segmented region of a selected vascular path into alignment with a more correctly segmented position that is depicted as a blood vessel in a vascular image. The method may use an energy function, defined as a function of position along the segmentation of the selected blood vessel, to determine how a vascular path is to be moved based on dragging motions provided by the user. In some instances, non-zero regions of the energy function are set based on the position of the selected region.

The invention relates to a venipuncture target recommendation method based on image processing and an artificial intelligence algorithm. The venipuncture target recommendation method comprises the following steps: S1, performing image enhancement processing on an input infrared image; s2, executing a hand image blood vessel segmentation task; s3, judging whether a blood vessel region exists in the hand graph or not; s4, segmenting the center line of the blood vessel, and dividing the blood vessel into a plurality of independent sections according to the bending characteristics of the blood vessel; and S5, summarizing a target spot rule suitable for puncture by utilizing the database model, and determining key characteristics of the puncture target spot according to the target spot rule. According to a blood vessel puncture rule, a linear regression model is established, the weight of each feature of a puncture target is calculated, the score of each potential puncture target is obtained in a weighted summation mode, and the puncture target with the highest score is selected for recommendation. The target spot and the puncture direction which accord with the puncture habit of a nurse can be more accurately selected. And the accuracy of puncture operation is improved, so that the success rate and the safety of puncture are remarkably improved in practical application.

The invention discloses a multi-mode cardiovascular disease detection method based on comprehensive view analysis, and relates to the problems that in the automatic diagnosis technology of cardiovascular diseases (CVD), CVD progress analysis is difficult, and diagnosis and prevention are difficult to achieve as early as possible. Although the current CVD automatic diagnosis technology based on deep learning achieves a certain progress, most of the technologies are limited to analysis of single-mode data, and the progress of CVD cannot be comprehensively predicted. In order to solve the problem, the invention provides a multi-mode cardiovascular disease detection method based on comprehensive view analysis. Experiments show that the method uses the blood vessel segmentation image to assist the retina fundus image to perform cross-view learning, and enhances the feature perception of the model on the retina blood vessel; two multi-modal data of a retina fundus image and a non-invasive clinical index are used as input, so that the deep understanding of the model on a CVD event is enhanced; a multi-order belief interactive fusion method is used for carrying out fusion and complementation on multi-modal features, and the accuracy and reliability of detection are improved. The method is applied to CVD detection.

The invention belongs to the field of blood vessel naming, and provides a head and neck blood vessel automatic naming method and system based on a CTA image, and the method comprises the steps: obtaining a head and neck CTA image, carrying out the segmentation through a pre-trained blood vessel segmentation model, generating a blood vessel mask image, and generating multi-dimensional point cloud data according to the blood vessel mask image; classifying each point by using a pre-trained head and neck branch point cloud segmentation model to obtain a point cloud classification result; constructing a feature vector based on the multi-dimensional point cloud data and the point cloud classification result, performing classification decision by using a pre-trained decision tree model, and determining a point cloud naming tag; and constructing a comprehensive feature vector according to the multi-dimensional point cloud data and the point cloud naming tag, clustering the comprehensive feature vector by adopting a constraint clustering algorithm, and correcting the point cloud naming tag according to a clustering result to obtain a head and neck vessel name. According to the method, local geometric features and spatial information of the point cloud are comprehensively considered as multi-dimensional point cloud data to correct the naming tag, and the classification precision of vessel naming is improved.

A method and system for processing an intracranial large vessel image, an electronic device, and a medium are provided. The method includes: acquiring original intracranial large vessel images of a target to be identified and a sample subject; applying a cerebrovascular segmentation model to obtain cerebrovascular mask images; computing regions of interest of the cerebrovascular mask images and bounding boxes; selecting corresponding regions of interest from the original intracranial large vessel images; separately preprocessing the mask image regions of interest and the original image regions of interest to obtain images to be processed; annotating a target region in the image to be processed of the sample subject; training a convolutional neural network model with a training set to obtain a cerebrovascular occlusion classification model; and inputting the image to be processed of the target to be identified to the cerebrovascular occlusion classification model to obtain a target region identification result.

The invention discloses a time-of-flight magnetic resonance blood vessel image cerebral vessel segmentation method, system and device and a medium, and relates to the field of medical image processing, and the method comprises the steps: obtaining a to-be-processed time-of-flight magnetic resonance blood vessel image; reinforcing blood vessel features in the to-be-processed time-of-flight magnetic resonance blood vessel image to obtain a preprocessed image; according to the preprocessed image, performing cerebrovascular segmentation by adopting a few-sample segmentation model to obtain a blood vessel probability graph; the few-sample segmentation model is obtained by migrating knowledge of a pre-training video word segmentation device to train 3D U-Net; and performing post-processing on the blood vessel probability graph and the to-be-processed flight time magnetic resonance blood vessel image based on a human-computer interaction interface and a conditional random field to obtain a final segmented image. According to the method, a high-precision and high-robustness segmentation effect can be realized only by a small number of samples, and result optimization can be carried out through an efficient man-machine interaction mode.

A system for hypertensive retinopathy (HTNR) detection includes a processor and a memory, including instructions stored thereon, which when executed by the processor, cause the system to: preprocess a retinal image using contrast enhancement, noise reduction and / or resolution normalization; segment a plurality of vessels from the preprocessed retinal image to generate a vessel segmentation map; detect a retinal marker, a vascular marker and / or an optic disc marker in the preprocessed retinal image using a first machine learning model; generate a severity score based on the detections; determine that the severity score exceeds a predefined threshold; and generate an output indicating a presence of HTNR based on the vessel segmentation map and the severity score using a second machine learning model.

The invention discloses a fundus blood vessel segmentation method based on deformable polar coordinate convolution and multi-scale gating. The fundus blood vessel segmentation method comprises the following steps: step 1, extracting multi-level features from an input image by using an encoder based on the deformable polar coordinate convolution; 2, enhancing the characteristics of the encoder by adopting a multi-scale gating attention module; and step 3, generating a blood vessel segmentation map by using the feature pyramid module based on the deformable polar coordinate convolution. According to the method, a deformable polar coordinate convolution is provided, and the offset of the deformable convolution is learned in a polar coordinate system, so that the deformable convolution can better adapt to the complex form of the blood vessel. Besides, in order to cope with the adjustment of large blood vessel scale difference and high similarity between blood vessels and background noise, the invention provides a multi-scale gating attention module, through a multi-scale strategy and a gating attention mechanism, the characterization capability of the model to multi-scale information and the anti-interference capability of the model to background noise are effectively enhanced, and the accuracy of the model is improved. Therefore, high-precision fundus blood vessel segmentation is realized.

The invention discloses a blood vessel OCT image training method, a blood vessel OCT image generating method and a blood vessel OCT image training device based on a DCN cross-modal fusion network and structure-blood flow coupling modeling. The method comprises the following steps: acquiring image data marked with a blood vessel segmentation result and a plaque position; acquiring the preprocessed OCT image data; registering the image data marked with the blood vessel segmentation result and the plaque position and the OCT image data so as to obtain registered image data; acquiring hemodynamic parameters; inputting the coronary artery CTA image, the image marked with the blood vessel segmentation result and the plaque position and hemodynamic parameters into a cross-modal fusion network based on deformable convolution so as to obtain CTA features of the cross-modal fusion network; and inputting the CTA features of the cross-modal fusion network into a trained OCT generation network so as to obtain a cross-section OCT image containing blood vessel wall and plaque information. The method can replace part of invasive OCT examination, assist doctors in assessing plaque vulnerability and guide interventional therapy.

The invention discloses a medical blood vessel segmentation dynamic optimization method and system based on anatomy prior perception, and belongs to the field of artificial intelligence and the technical field of medical image processing. The method comprises the following steps: acquiring medical image data and corresponding blood vessel labeling data, constructing a data set, and preprocessing and post-processing data in a training set; constructing a segmentation model, wherein the segmentation model comprises a multi-scale feature fusion module and a dynamic snakelike convolution module; constructing a loss function of the segmentation model, and training the segmentation model by using the training set and the blood vessel labeling data; and performing blood vessel segmentation on the medical image data in the test set by using the trained segmentation model. According to the invention, through the dynamically configured multi-scale feature fusion module and fusion mechanism and the dynamic optimization strategy of topology perception, the problem that it is difficult to consider fine segmentation of the blood vessel structure and overall topology integrity in the prior art is effectively solved, and the robustness and operation efficiency of the medical blood vessel segmentation model are significantly improved.

The invention relates to the technical field of medical image processing, and discloses a blood vessel segmentation model, a model training method and device, equipment and a storage medium, and the blood vessel segmentation model comprises an encoder and a decoder. The encoder comprises a plurality of stages of down-sampling modules, and the decoder correspondingly comprises a plurality of stages of up-sampling modules; each level of down-sampling module comprises a multi-scale local form attention module; the multi-scale local form attention module comprises a plurality of different convolution modules, and the different convolution modules have different convolution kernel sizes and are used for respectively extracting blood vessel form characteristics under different scales; the multi-scale local form attention module further comprises a splicing module which is used for splicing the blood vessel form characteristics under different scales. And an output layer in the decoder carries out blood vessel segmentation based on the blood vessel morphological characteristics under different scales. According to the invention, the segmentation accuracy of the blood vessel with a complex morphological structure can be improved.

The invention discloses a medical image optimization method and system based on vascular branch selective blurring. The method comprises the following steps: carrying out preprocessing and blood vessel enhancement on a three-dimensional angiography DICOM image; performing precise blood vessel segmentation by using an encoder-decoder network comprising a wide activation and residual cavity space pyramid module; identifying an interference branch which shields the target blood vessel structure at a specific working angle through blood vessel topology analysis; selective blurring repair is performed on the interference branches based on a Poisson equation and a bidirectional convolution LSTM to generate an optimized image which is visually unobstructed and keeps topological continuity. And a safety mechanism of virtual-real combined display and multi-angle plan planning is introduced, so that the reliability of surgical navigation is ensured. And finally, integrating the optimized 3D blood vessel model to a radiography system supporting real-time synchronization, and using the 3D blood vessel model as a road map. The clinical problem that the optimal working angle cannot be used due to blood vessel shielding can be effectively solved, and the precision and safety of an endovascular interventional operation are remarkably improved.

The embodiment of the invention provides a cross-domain generalization model training method, a blood vessel segmentation method, computing equipment, a computer readable storage medium and a computer program product. The cross-domain generalization model training method comprises the steps that source domain image data, label data corresponding to the source domain image data and target domain image data are acquired; respectively inputting the source domain image data and the target domain image data into a blood vessel segmentation model to obtain a source domain segmentation result and a target domain segmentation result; inputting the source domain segmentation result and the target domain segmentation result into a discriminator to generate a discrimination result; constructing a first loss function and a second loss function; and by taking minimization of the first loss function and simultaneous maximization and minimization of the second loss function as optimization objectives, iteratively adjusting network parameters of the blood vessel segmentation model and network parameters of the discriminator to obtain a trained generalized blood vessel segmentation model. According to the technical scheme provided by the embodiment of the invention, the blood vessel automatic segmentation precision and the cross-domain adaptive capacity are improved.

The present disclosure relates to a method, a device and a medium for performing vessel segmentation in a medical image. The method may comprise acquiring a medical image for vessel segmentation containing multiple parts, each of which contains vessels with different structural attributes. The method may comprise dividing the medical image into sub-medical images according to the parts by using a processor. The method may comprise determining individual vessel segmentation result for each part by means of using the vessel segmentation model corresponding to the part based on the sub-medical image of the part by using the processor. The method may comprise obtaining a vessel segmentation result of the medical image by means of fusing the individual vessel segmentation results of the sub-medical images of the parts by the processor. By applying the method and the device, vessel segmentation models are adapted differentially for the individual parts so as to segment the vessels of the individual parts respectively, so that the entire vessel segmentation process is fast, effective, and accurate.

The invention relates to an automatic blood vessel segmentation method and system for a CT image. The method comprises the following steps: acquiring an enhanced CT image and a plain-scan CT image at the same position; inputting the enhanced CT image to a pre-trained first blood vessel automatic segmentation model to obtain a first blood vessel segmentation result; and registering the enhanced CT image and the plain-scan CT image to mark and map the first blood vessel segmentation result to the plain-scan CT image so as to obtain a second blood vessel segmentation result. The method has the advantages that a deep learning model (such as a registration network based on U-Net or Transform) is utilized to calculate a deformation field, and in combination with multi-scale feature enhancement and regularization strategies, the precision and stability of enhanced CT and plain-scan CT image registration are improved, and artifacts and local distortion in the deformation field are reduced; in order to solve the problem that the contrast difference of a plain scanning CT image and an enhanced CT image is significant, a compensation strategy of multi-modal texture and intensity distribution is introduced, such as adversarial loss and structural similarity index (SSIM) optimization, so that registration is more robust among different modals.

The invention relates to the technical field of face injection, in particular to a face precise injection system based on real-time B-ultrasonic imaging and a using method, and the method comprises a high-frequency ultrasonic imaging module which is provided with a deformable curved surface probe array; the image processing unit integrates a blood vessel segmentation algorithm, a three-dimensional reconstruction module and a dangerous area dynamic calibration function; the navigation positioning module comprises an electromagnetic positioner and an optical tracker; the injection executing mechanism is composed of a six-degree-of-freedom mechanical arm, a pressure feedback type injector and a needle tip microspur sensor; the main control unit is used for coordinating data interaction of all the modules and generating injection safety early warning; by adopting high-frequency ultrasonic imaging, the skin layer, fat layer, muscle and blood vessel structures can be clearly displayed, deviation caused by traditional touch typing injection is avoided, tiny blood vessels can be recognized through an AI blood vessel automatic segmentation algorithm, and therefore the risk of wrong injection in the blood vessels is remarkably reduced.

The invention provides a DSA-oriented X-ray coronary angiography image blood vessel segmentation method and system, and relates to the technical field of image processing and deep learning. The method comprises the following steps: firstly, acquiring a target segmentation image and a reference image based on X-ray coronary artery angiography video data, and forming a training sample by the reference image and the target segmentation image to construct a training data set; then constructing a segmentation network model; the segmentation network model comprises a main encoder, an auxiliary encoder, a plurality of up-sampling attention modules, a similarity attention module and a decoder; calculating a total loss function by using the segmentation mask of the target segmentation image, and optimizing the segmentation network model; and finally, inputting an X-ray coronary artery angiography image data set needing to be segmented into the trained segmentation network model to obtain a segmentation result. According to the method, an X-ray perspective image before radiography is compared with a target segmentation image, so that a neural network is helped to distinguish a foreground from a background, and blood vessels are accurately recognized.

The present invention provides a method and system for automatically segmenting blood vessels within organs in enhanced CT images. The method includes performing organ segmentation processing on the enhanced CT images to extract the organ regions, and obtaining three-dimensional regions of interest in the organs from the organ regions; within the three-dimensional regions of interest in the organs, performing global blood vessel segmentation within the organs and local blood vessel segmentation within the organs respectively to generate a global automatic blood vessel segmentation result within the organs and a local automatic blood vessel segmentation result within the organs; and fusing the global automatic blood vessel segmentation result within the organs and the local automatic blood vessel segmentation result within the organs to obtain the final automatic blood vessel segmentation result within the organs. The present invention proposes a fully automatic progressive deep learning solution for segmenting blood vessels within organs, aiming to maintain the integrity of blood vessels in terms of their shape details and continuity. Experimental results show that this solution has high accuracy, efficiency, and robustness.