30 results about "Vascular segmentation" patented technology

A method of segmenting tubular structures in digital images includes selecting a point in an image of a tubular object to be segmented, defining an initial state of the selected point, initializing measurement weights, a conditional probability distribution and a prior probability distribution of a feature space of the initial state, sampling the feature space from the prior probability distribution, estimating a posterior probability distribution by summing sample measurements weighted by the measurement weights, and segmenting a cross section of the tubular object from the posterior probability distribution.

A technique is provided for automatically generating a bone mask in CTA angiography. In accordance with the technique, an image data set may be pre-processed to accomplish a variety of function, such as removal of image data associated with the table, partitioning the volume into regionally consistent sub-volumes, computing structures edges based on gradients, and / or calculating seed points for subsequent region growing. The pre-processed data may then be automatically segmented for bone and vascular structure. The automatic vascular segmentation may be accomplished using constrained region growing in which the constraints are dynamically updated based upon local statistics of the image data. The vascular structure may be subtracted from the bone structure to generate a bone mask. The bone mask may in turn be subtracted from the image data set to generate a bone-free CTA volume for reconstruction of volume renderings.

Methods, systems, computer programs, circuits and workstations are configured to generate at least one two-dimensional weighted CBF territory map of color-coded source artery locations using an automated vascular segmentation process to identify source locations using mutual connectivity in both image and label space.

An X-ray image processing device for providing segmentation information with reduced X-ray dose that includes an interface unit, and a data processing unit. The interface unit is configured to provide a sequence of time series angiographic 2D images of a vascular structure obtained after a contrast agent injection. The data processing unit is configured to determine an arrival time index of a predetermined characteristic related to the contrast agent injection for each of a plurality of determined pixels along the time series, and to compute a connectivity index for each of the plurality of the determined pixels based on the arrival time index. The data processing unit is configured to generate and provide segmentation data of the vascular structure from the plurality of the determined pixels, wherein the segmentation data is based on the connectivity index of the pixels.

The invention belongs to the technical field of medical image processing, and discloses a blood vessel image segmentation method based on centerline extraction, a nuclear magnetic resonance imaging system, and preprocessing of brain blood vessel data by vesselness filtering based on Hessian matrix; topology refinement method for blood vessel centerline Extraction; take the centerline point as a positive sample and the non-vascular point as a negative sample to extract the features of the training sample and the test sample; use the features of the training sample and the corresponding label to train the SVM model, and use the feature of the test sample as the input of the trained SVM model , the output label is the segmentation result of blood vessels. The invention reduces the workload and improves the calculation efficiency; it does not need to manually calibrate the target and the background, completes automatic blood vessel segmentation, and greatly improves the segmentation efficiency. The invention realizes the segmentation of cerebral blood vessels, which is accurate, fast and does not require human intervention; the true positive rate and true negative rate can reach 0.85.

The embodiment of the present invention discloses a blood vessel segmentation method, device, medical imaging equipment, and storage medium. The method includes: acquiring a blood vessel image to be segmented and a blood vessel distribution map corresponding to the blood vessel image to be segmented; combining the blood vessel image to be segmented and the The blood vessel distribution map is input to the preset blood vessel segmentation model to obtain the blood vessel segmentation result. The above technical solution solves the problem that the blood vessel segmentation method in the prior art is not suitable for the segmentation of relatively complex blood vessel images such as coronary artery images, and improves the precision of blood vessel segmentation.

The invention discloses a blood vessel segmentation method, equipment and computer storage medium. The method includes: acquiring and segmenting blood vessel image data, obtaining the prediction result of the aorta model and the prediction result of the small blood vessel model; judging the prediction result of the aorta model and Whether the prediction results of the small blood vessel model can be connected as a whole; when the prediction results of the aorta model and the prediction results of the small blood vessel model cannot be connected as a whole, perform coronal segmentation prediction on the blood vessel image data to obtain A prediction result of the coronary ostium model; integrating the prediction result of the aorta model and the prediction result of the small vessel model according to the prediction result of the coronary ostium model to obtain the prediction result of the normal coronary artery model. The invention makes the segmentation of the coronary ostium area more accurate, thereby overcoming the inaccurate or even abnormal segmentation of the joint between the coronary artery and the aorta in the traditional method, thereby obtaining a more complete and accurate coronary artery segmentation result.

The embodiment of the present invention relates to a method for segmenting blood vessels in a fundus image. Each blood vessel feature processing layer sends an up-sampled image obtained by this layer to a connected blood vessel feature optimization layer; the lowest blood vessel feature optimization layer also acquires the lowest blood vessel feature. The feature image of the extraction layer; the blood vessel feature processing layer of the highest layer sends the up-sampled image obtained by this layer to the blood vessel feature optimization layer of the lowest layer through the backward short connection; each blood vessel feature optimization layer sequentially acquires each image. Performing blood vessel feature extraction and non-linear processing to obtain a non-linear image corresponding to each image; each blood vessel feature optimization layer sends each acquired image to a higher blood vessel feature optimization layer through forward short connections. In the embodiment of the present invention, the high-level information is sent to the low-level through the backward short connection, and the low-level information is sent to the high-level through the forward short connection, so that the features of all levels are fully integrated, and the blood vessel segmentation is more accurate.

The invention discloses a retinal artery vein diameter ratio automatic measuring method and a device. The retinal artery vein diameter ratio automatic measuring device can automatically calculate thecorresponding AVR indexes, increase the accuracy and automation degree of AVR calculation, realize accurate measurement of the AVR indexes, and accelerate clinical disease diagnosis and treatment speed. The method comprises the following steps: (1) acquiring a retina image by using fundus imaging equipment; (2) carrying out optic disc positioning and blood vessel segmentation; (3) based on the center line, carrying out blood vessel topology analysis; and (4) based on a deep convolutional network, classifying the blood vessel tree into arteries or veins, calculating the diameter and category ofsampling point positions, and completing the measurement of the retinal artery-vein diameter ratio AVR.

The present application relates to a retinal vessel segmentation method, device and storage medium based on vessel features. Including obtaining the dataset of retinal vessel segmentation, and performing data enhancement operations on the dataset; establishing a deep convolutional neural network intelligent model based on multi-scale semantic information fusion, inputting the enhanced data into the intelligent model, and obtaining the output of the model; Use the image morphology method to detect blood vessel breakpoints and areas with inconsistent thickness in the output results; increase the weight of the loss function on the breakpoints and areas with inconsistent thickness, and iterate and optimize the intelligent model through the cross-entropy loss function of dynamic weights to achieve Accurate segmentation of retinal vessels. This method combines multi-scale semantic information and breakpoint information to solve vascular connectivity, and improves the loss function by extracting vascular thickness information to solve the problem of inconsistency in vascular thickness, effectively improving the accuracy of retinal vascular segmentation, which is of great significance for computer medical intelligent diagnosis .

The invention discloses a fundus image blood vessel segmentation method based on phase congruency and mainly overcomes the defect that a traditional method can not be used to accurately segment blood vessels in fundus images. The fundus image vascular segmentation method base on the phase congruency can be simultaneously used to segment small blood vessels of most tips. The method comprises the steps: (1) extracting green channels of the fundus images, (2) enhancing the contrast ratio of the images through contrast limited adaptive histogram equalization (CLAHE), (3) filtering the fundus images through the anisotropic coupled diffusion equation, (4) segmenting the blood vessels of the fundus images filtered or not filtered through the anisotropic coupled diffusion equation in a phase congruency algorithm, (5) multiplying pixel-levels of results of vessels, of two fundus images, segmented based on the phase congruency algorithm, (6) processing the images in a binaryzation mode through the iterative threshold segmentation method, (7) optimizing the images in the mathematical morphology method. The fundus image vascular segmentation method has significant application values in fields of three-dimensional splicing of the fundus images and judging existence of diabetes mellitus and severity of diabetes mellitus.

The invention discloses a method and system for segmenting retinal blood vessels based on retinal fundus images, belonging to the technical field of image processing, including: acquiring a retinal fundus image to be detected; constructing a basic module according to the characteristics of the retinal fundus image; cascading N basic modules as In the final network model, the retinal fundus image to be detected is used as the input of the overall network model to obtain the segmentation result of retinal blood vessels. By passing the foreground features of the previous basic module and the original picture into the next basic module, the latter basic module can inherit the learning experience of the previous basic module, thus speeding up the training process and effectively solving the problem of data imbalance. Problem: Using the retinal fundus image to be detected as the input of the overall model S-UNet, the segmentation result of retinal blood vessels is more accurate.

The present application discloses a method for cerebrovascular segmentation based on a multi-view cascaded deep learning network. The method includes acquiring MRA images and MRV images corresponding to the target part; determining several slice images based on the MRA images and MRV images; The trained first segmentation network model determines the reference segmentation map corresponding to each slice image; based on the MRA image, the MRV image, the obtained reference segmentation map and the trained second segmentation network model, determine the MRA The image corresponds to the segmented image. In this application, the first network segmentation model acquires the reference segmentation images corresponding to each section image as context information together with the MRA image and the MRV image as the input information of the second segmentation network model, so that the blood vessels in the MRA image can be learned from multiple perspectives Information, so that the accuracy of the second segmentation network model to determine the segmented image can be improved, thereby improving the accuracy of cerebrovascular segmentation.

The invention discloses a fundus image blood vessel segmentation method based on a shared decoder and a residual tower structure. The method comprises the following steps: obtaining an image block of a training data set and an image block of a test data set through a data input module; The tower module obtains the residual tower sequence; the multi-level semantic features are obtained through the encoding module; the multi-level probability map is obtained through the shared decoding module; the multi-scale label, the residual tower sequence, and the probability map obtained by the shared decoder are constructed The total loss of the model is formed, and PyTorch is used for gradient optimization to train the parameters in the encoding module and the shared decoding module; the image blocks of the test data set are sequentially input into the trained encoding module and the shared decoding module to obtain a probability map, and the obtained probability map And stitching, binarization processing to get the final segmentation results. The invention solves the problems of uneven distribution of blood vessel calibers and weak contrast of fundus images.

The invention discloses a wrapping-degree determination method and device for a the tumor-wrapping vessel and relates to the technical field of biomedicine with a main aim to be capable of ensuring accuracy in wrapping-degree calculation of the tumor-wrapping vessel. The method includes: acquiring vascular segmentation images which are acquired by subjecting the tumor-wrapping vessel to vascular segmentation; identifying vascular contours of the vessel according to vascular points segmented according to the vascular segmentation images; determining points that the vessel is adhered to the tumor by traversing the points in the vascular contours and two points in the vascular contours in a manner of neighboring back and forth; determining contour length of the vascular contours by traversing the points in the vascular contours and determining wrapping length of the tumor-wrapping tumor by traversing the points where the vessel is adhered to the tumor; according to the contour length and wrapping length, calculating the wrapping degree of the tumor-wrapping vessel. By the arrangement, wrapping degree of the tumor-wrapping tumor is determined.