30 results about "Vascular segmentation" patented technology

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.

The invention relates to a medical image processing technology, and provides a fundus image-oriented microaneurysm detection method for overcoming the defects of detection of a microaneurysm which isa micro target in the prior art, so as to effectively detect the microaneurysm in a fundus image, and better assist a doctor in diagnosis while realizing automatic detection. The method comprises: preprocessing the fundus image, so that the features of a small target are enhanced, and making and training a data set on a built basic feature extraction network; during detection, extracting image basic features from the input image, and performing blood vessel segmentation on the input image by utilizing a segmentation model to obtain a feature map and a segmentation map for subsequent processing; then integrating an attention mechanism into a feature fusion process to obtain a fused convolution feature layer; inputting the fused convolution feature layer into a candidate region generation network, and obtaining a candidate region by considering the position relationship between the target and the blood vessel; and finally, further classifying and regressing the candidate regions to obtain a detection result.

The invention discloses a method for fine segmentation of the eye ground optic disc based on SLIC super-pixel segmentation. The steps are as follows: super-pixel segmentation, vascular segmentation based on morphological processing and R-G double-channel color threshold segmentation are carried out on an input eye ground image, and after expansion of a connected region obtained from color threshold segmentation, a corresponding Toeplitz matrix template is selected according to the pixel coordinates of the connected region to filter an eye ground vascular image to obtain the center position ofthe optic disc; and then, a candidate area of the optic disc is extracted and the internal blood vessels are removed, threshold segmentation is carried out on the candidate area of the optic disc through a binarization method, the ROI (Region of Interest) of the optic disc ellipse is determined by using an ellipse fitting method based on least squares, super pixels with a certain overlapping areaare retained according to the SLIC super-pixel segmentation result, and thus, fine segmentation of the optic disc is completed. Through the method, automatic positioning and fine segmentation of the optic disc are realized, better contour information of the optic disc can be retained, less time is consumed, other follow-up processing of the eye ground image is facilitated, and assistant diagnosiscan be provided for ophthalmologists.

The present application relates to a cerebral vascular segmentation method, a system and an electronic device. The method comprises the following steps: the original blood vessel image data is processed by multi-scale filtering and enhancement to obtain the enhanced blood vessel image data and the corresponding direction vector field; the finite mixing model is established, and the parameters of the finite mixing model are estimated, and the class conditional probability P (y | x) is obtained; an initial marker field of the blood vessel image data is calculated and the initial marker field iscombined with a corresponding direction vector field to form a new Markov random field; according to the equivalence of Markov random field and Gibbs distribution, a priori-like probability P (x) is obtained; based on the conditional probability P (y | x) and the priori-like probability P (x), the blood vessel segmentation results of the blood vessel image data are obtained through the maximum posterior probability and the conditional iterative mode. The present application can extract more effective blood vessel candidate space, extract blood vessel structure under low contrast, and make theblood vessel network more complete.

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.

The invention discloses a method and a system for establishing a loss function for liver blood vessel segmentation, and belongs to the technical field of medical image processing and artificial intelligence. The method comprises the following steps: performing morphological corrosion on an original liver blood vessel mask, and removing free miscellaneous points to obtain a second mask; performing morphological expansion on the second mask to obtain a third mask; calculating an intersection of pixel positions, which are equal to 1, in the original mask and the third mask to obtain a fourth mask; calculating a difference set of pixel positions which are equal to 1 in the original mask and the fourth mask to obtain a fifth mask; dividing an original liver blood vessel mask pixel position set into three parts, and establishing a loss function of liver blood vessel segmentation; the system comprises a corrosion module, a removal module, an expansion module, a first calculation module, a second calculation module and an establishment module. The loss function established by the invention can perform different weighting on branch blood vessels, main blood vessels and background pixels, and the optimization direction of the network is more flexibly and accurately controlled.

The invention relates to a tumor area capillary segmentation method and device. The method comprises the following steps: acquiring an image block containing a tumor area and microvessels; and inputting the image block into a tumor area microvessel segmentation model to obtain a prediction probability graph of the tumor area and the microvessels, wherein the prediction probability graph comprises a capillary region, a tumor region and a background region, and the tumor area capillary segmentation model comprises two convolution layers with preset scales, a plurality of dense connection blocks and a sigmoid activation module. By adopting the method, the accuracy of tumor area capillary segmentation can be improved.