36 results about "Coronary artery calcification" patented technology

The present invention is directed to a method and apparatus of scoring an arterial obstruction. The present invention includes identifying a particular group of pixels corresponding to a calcified region of coronary artery of a patient and based on the satisfaction of a number of thresholds connectivity criteria, determining a calcification score of that calcified region as indicative of the patient's risk of cardiovascular disease including atherosclerosis, heart attack, heart failure, and stroke. The present invention further includes determining the scaling factor unique to a particular imaging slice of data to provide a value of calcium weight and volume that is indicative of the true obstruction and true volume of the calcified region.

The invention provides a method, device and system for obtaining coronary-artery-calcification area classification, and relates to the technical field of calcification area selection. The method includes the steps that coronary-artery areas are obtained based on obtained three-dimensional information of CT plain scan information; suspected calcification areas are obtained from the obtained coronary-artery areas according to the certain condition; according to the preset condition, whether the suspected calcification areas need to be cut or not is judged, suspected calcification areas meeting the condition are cut; according to the characteristics of the cut suspected calcification areas, the suspected calcification areas are classified, and the classified suspected calcification areas areobtained. According to the method, device and system for obtaining coronary-artery-calcification area classification, the technical problems that calcification areas are misclassified when the calcification areas are manually marked, and coronary-artery-calcification-area selection and classification are large in error, complex in process and low in efficiency are solved, and coronary-artery-calcification-area selection and classification are simple in structure, high in efficiency and small in error.

The invention provides a method, apparatus and system for calculating coronary artery calcification integral, and relates to the technical field of calcification score. The method comprises: the coronary artery regions are obtained based on plain CT information, Suspected calcification regions are obtained from the acquired coronary artery regions according to certain judgment conditions, and thesuspected calcification regions are classified according to the image characteristics of the suspected calcification regions to obtain the classified coronary artery calcification regions, and the coronary artery calcification scores are obtained based on the calculated calcification scores of the classified coronary artery calcification regions. The invention provides a method, apparatus and system for calculating coronary artery calcification integral, , which can alleviates the technical problems of inaccurate marking or inaccurate marking range and low efficiency of artificially marking the coronary artery calcification area in the prior art, thereby inaccurate integrating of the coronary artery calcification area, low efficiency of calculating integrating and large error, thereby improving the efficiency of calculating the coronary artery calcification integral and reducing the error of calculating.

The present invention provides an automatic heart disease detection system based on a convolutional neural network. The automatic heart disease detection system comprises a database, a lesion information processing module, a key frame extraction module, a data cleaning module, an information fusion and storage module, a convolutional neural network learning module, a model storage and screening module and a lesion detection module. The invention relates to the fields of deep learning, medical treatment and computer vision. By design of scientific optimization, a set of automatic intelligent detection system for coronary artery calcification, complete occlusion and thrombosis based on the convolutional neural network is created and achieved, aiming at the requirements of diagnosis on resultaccuracy and timeliness, the design structure is ingenious, a high-quality auxiliary diagnosis detection result can be output without any manual assistance, and convenience is brought to coronary artery patients and doctors.

The invention relates to a myocardial perfusion development and coronary artery calcification score combined diagnostic system and method. The diagnostic system comprises a myocardial perfusion development system, a coronary artery calcification score system and a data processing system. Both the myocardial perfusion development system and the coronary artery calcification score system are based on a siemens Symbia T16 SPECT/CT image system, the myocardial perfusion development system is used for collecting nuclein myocardial perfusion development of patients and obtaining diagnostic data by analyzing images. The coronary artery calcification score system works after the myocardial perfusion development system finishes work and is used for collecting coronary artery calcification score data and sending the collected data to an Agatston automatic analysis software system to be computed and analyzed, diagnostic data obtained through the myocardial perfusion development system is combined with the analysis data of the coronary artery calcification score system to be sent to the data processing system to be processed, and the diagnostic result is obtained through statistical analysis. The myocardial perfusion development and coronary artery calcification score combined diagnostic system and method achieve one-stop examination and have the advantages that the system and method are easy and convenient to operate, safe and reliable, the diagnosis time is saved, the cost is saved, the diagnosis efficiency is improved, and the diagnosis accuracy is improved.

The invention discloses a coronary artery calcification lesion full-automatic segmentation method based on chest flat scanning CT, and the method comprises the following steps: carrying out the preprocessing of an original chest flat scanning CT image, and obtaining preprocessing data; inputting the preprocessed data into a heart segmentation model, and segmenting a heart region in the original chest flat-scanning CT image to obtain a heart segmentation image which corresponds to the original chest flat-scanning CT image and contains the heart region; screening suspected coronary artery calcification lesions in the heart area; extracting a plurality of candidate sample blocks from the screened suspected coronary artery calcification lesions; and based on the plurality of candidate sample blocks and the corresponding coordinates of the candidate sample blocks in the coordinate system corresponding to the original chest horizontal scanning CT image, obtaining coronary artery calcification focus segmentation results of the candidate sample blocks by using a calcification focus segmentation model. According to the method, a coarse-to-fine strategy is adopted, a two-stage deep neural network is used for achieving full-automatic coronary artery calcification focus segmentation based on chest flat scanning CT, and a good segmentation result can also be obtained for data with large noise.

The invention relates to a coronary artery calcification plaque segmentation method, a coronary artery calcification integral calculation method and a computer readable storage medium. The method comprises the following steps: acquiring a first medical image of a heart area; classifying each voxel of the first medical image by using a preset segmentation model to obtain a first classification result of classifying each voxel according to a parent category and a second classification result of classifying each voxel according to a subcategory; according to the parent category of each voxel in the first medical image, correcting the subcategory of each voxel in the first medical image to obtain a third classification result; and obtaining a coronary artery calcification plaque segmentation result of the first medical image according to the third classification result. According to the method and the device, the problem of low robustness of a coronary artery calcification plaque segmentation method in related technologies is solved, and the robustness of coronary artery calcification plaque segmentation is improved.

The invention relates to a coronary artery calcification plaque detection method and device, and the method comprises the steps: carrying out the segmentation processing of a to-be-processed image, and determining a plurality of target regions of a first organ in the to-be-processed image; for any target area, determining a first gray threshold and a second gray threshold of the target area according to the gray information of the target area; respectively determining a suspected focus area and a pseudo focus area in the target area according to the first gray threshold and the second gray threshold of the target area; and determining the focus area of the first organ according to the suspected focus area and the pseudo focus area of each target area. According to the embodiment of the invention, omission of the calcified plaque at the small branch blood vessel of the coronary blood vessel in the to-be-processed image can be effectively reduced, the recall rate of the calcified plaque is improved, and the accuracy of identification of the calcified plaque can be remarkably improved.

The invention discloses a multi-view shape constraint system, which comprises a slicing unit for slicing a received image to obtain a coronal slice, a sagittal slice and an axial slice; a first expansion Unet unit, a second expansion Unet unit and a residual expansion network RDN unit for receiving the coronal slice, the sagittal slice and the axial slice respectively; a multi-view shape constraint unit used for integrating different view information through multi-view attention learning to generate multi-view attention features; and a multi-task learning unit that estimates a related calcification index using the output of the multi-view shape constraint unit. The first expansion Unet unit and the second expansion Unet unit model the coronal slice and the sagittal slice and obtain prediction graphs of the coronal view and the sagittal view as supplementary information of calcification lesions, and the residual error expansion network RDN unit models the axial slice and outputs the prediction graph of the axial slice.

A method of predicting a potential degree of Coronary Artery Calcification (CAC) risk includes receiving a patient's medical test data relating to CAC; determining a cluster to which the patient's medical test data belong based on an age of the patient; extracting a risk factor score including at least two Coronary Artery Calcification Scores (CACSs) from the medical test data; storing a plurality of prediction models used for predicting a potential degree of CAC risk; and predicting a potential degree of CAC risk at a specific point in time by applying a CACS growth rate of the patient's medical test data calculated using the at least two CACSs of the patient's medical test data and the extracted risk factor score to a prediction model corresponding to the determined cluster to which the patient's medical test data belong among the plurality of prediction models.

The invention relates to a method for calculating a coronary artery calcification ratio based on end-to-end reasoning of a convolutional neural network. The method comprises the following steps: determining a CT image group to be input; then, segmenting the CT image group into grid 3D images; inputting the information of the image into a neural network to extract abstract features; performing neural network training on whether the image is a heart or not through a first full connection layer; performing neural network training for judging whether the image has coronary artery or not through asecond full connection layer; performing neural network training for judging whether calcification exists in coronary artery of the image or not through a third full connection layer; performing neural network training on the position of the coronary artery of the image through a fourth full connection layer; and performing neural network training of the calcification proportion of the coronary artery of the image through a fifth full connection layer. The method solves the problem of how to simulate a plurality of judgments performed by an imaging doctor in the process of judging the calcification of a case, and provides the end-to-end neural network capable of performing layer-by-layer reasoning.

The invention relates to the technical field of coronary rotational atherectomy, and particularly relates to a rotational atherectomy interventional treatment instrument for treating a coronary arterycalcification lesion. The rotational atherectomy interventional treatment instrument comprises a rotational atherectomy instrument host, a pedal control board, a nitrogen tank, a propeller, a rotational atherectomy catheter and a rotational atherectomy guide wire; a speed reduction switch is arranged on the pedal control board, and the outside of the pedal control board is communicated with a speed reduction control pipe and two ventilation hoses; a gas outlet connector is arranged at the top end of the nitrogen tank, and the gas outlet connector is communicated with a nitrogen interface by agas supply pipe; the propeller comprises a machine body, a moving handle, an optical fiber rotation speed connection cable, a saline solution input pipe and a gas connection pipe; a strip slot is formed on the upper end face of the machine body; the moving handle is mounted in a sliding mode inside the strip slot; scale lines are also formed on both front and rear sides of the strip slot; and a handle travel control structure is also mounted in a sliding mode on the machine body. According to the invention, a forward distance of a rotational atherectomy head can be limited, so as to effectively avoid a deviation generated due to artificial control on the front and rear process of the rotational atherectomy head and protect a coronary artery canal behind the calcification lesion position from being damaged.

The invention relates to a blood miR-3135b detection kit used for diagnosis of coronary artery vascular calcification, which includes a primer for amplifying miR-3135b. In addition, the invention also relates to an application of such primer for amplifying the miR-3135b in preparation of the blood detection kit for diagnosis of the coronary artery vascular calcification.

Provided is an agent that binds to a wild type or variant pre-miRNA-1229 comprising a G-quadruplex (GQ) structure, wherein binding of the agent to the wild type or variant pre-miRNA-1229 stabilizes the GQ structure of the wild type or variant pre-miRNA-1229. In some embodiments, the variant is rs2291418. Provided is a method of treating a disease in a subject, comprising: administering a therapeutically effective amount of the agent to the subject. In some embodiments, the disease is Alzheimer's disease, cancer or coronary artery calcification.