# US 2021/0241484 A1 · Prediction method for healthy radius of blood vessel path, prediction method for candidate stenosis of blood vessel path, and blood vessel stenosis degree prediction device > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241484 A1 - Document ID: US20210241484A1 - Status: Published application - Kind: A1 - Application number: 17/237,480 - Filed: 2021-04-22 - Published: 2021-08-05 - Earliest claimed: 2018-09-24 - Assignees: Keya Medical Technology Co., Ltd. - Applicants: Keya Medical Technology Co., Ltd. - Inventors: Xin Wang; Youbing Yin; Junjie Bai; Yuwei Li; Yi Lu; Kunlin Cao; Qi Song - Web page: https://patentgrep.com/patent/US20210241484A1 ## CPC - G06T 7/0012: {Biomedical image inspection} - G06T 7/62: of area, perimeter, diameter or volume - A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet} - A61B 5/02007: {Evaluating blood vessel condition, e.g. elasticity, compliance} - A61B 5/1075: {for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer (A61B8/0858 takes precedence)} - A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems} - A61B 5/7267: {involving training the classification device} - A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor} - A61B 6/032: {Transmission computed tomography [CT]} - A61B 6/504: {for diagnosis of blood vessels, e.g. by angiography} - A61B 6/507: {for determination of haemodynamic parameters, e.g. perfusion CT} - A61B 6/5217: {extracting a diagnostic or physiological parameter from medical diagnostic data} - G06T 7/0014: {using an image reference approach} - G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS - G16H 30/40: for processing medical images, e.g. editing - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/30: for calculating health indices; for individual health risk assessment - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/20021: Dividing image into blocks, subimages or windows - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30101: Blood vessel; Artery; Vein; Vascular - G06T 2207/30172: Centreline of tubular or elongated structure - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract A computer-implemented method for predicting a blood vessel stenosis is disclosed. The method may include extracting a blood vessel path and its centerline based on the image of the blood vessel. The method may further include determining a candidate stenosis for the blood vessel path and identifying image blocks along the centerline of the blood vessel path within a range of candidate stenosis for the blood vessel path determined based on the candidate stenosis. The method may also include determining a degree of stenosis for the blood vessel path by applying a trained learning network comprising a convolutional neural network and a recurrent neural network on the image blocks within the range of candidate stenosis. ## Claims ### Claim 1 A computer-implemented method for predicting a blood vessel stenosis, the method comprising: extracting a blood vessel path and its centerline based on an image of a blood vessel; determining a candidate stenosis for the blood vessel path; identifying image blocks along the centerline of the blood vessel path within a range of candidate stenosis for the blood vessel path determined based on the candidate stenosis; and determining a degree of stenosis for the blood vessel path by applying a trained learning network comprising a convolutional neural network and a recurrent neural network on the image blocks within the range of candidate stenosis. ### Claim 2 (depends on 1) The computer-implemented method according to claim 1, further comprising: predicting a healthy radius of the blood vessel path by performing a quadratic regression on blood vessel radius peaks of the blood vessel path; and determining the candidate stenosis for the blood vessel path based on the healthy radius of the blood vessel path. ### Claim 3 (depends on 2) The computer-implemented method according to claim 2, wherein predicting the healthy radius of the blood vessel path further comprises: predicting a reference healthy radius of the blood vessel path by performing a linear regression on the blood vessel radius peaks; and replacing the blood vessel radius peaks in the blood vessel path that are lower than the reference healthy radius with the reference healthy radius. ### Claim 4 (depends on 1) The computer-implemented method according to claim 1, wherein the range of candidate stenosis for the blood vessel path has the candidate stenosis at its center. ### Claim 5 (depends on 1) The computer-implemented method according to claim 1, wherein the convolutional neural network and the recurrent neural network are sequentially applied, wherein the convolutional neural network is applied on the image blocks along the centerline of the blood vessel path to generate vectors for the respective image blocks, wherein the recurrent neural network is applied on the vectors. ### Claim 6 (depends on 1) The computer-implemented method according to claim 1, wherein the convolutional neural network and the recurrent neural network are jointly trained. ### Claim 7 (depends on 1) The computer-implemented method according to claim 1, wherein the convolutional neural network comprises a plurality of convolutional neural network units each configured to process one of the image blocks along the centerline of the blood vessel path. ### Claim 8 (depends on 1) The computer-implemented method according to claim 1, wherein the recurrent neural network comprises a forward recurrent neural network and a reverse recurrent neural network configured to collectively learn a spatial correlation among the image blocks along the centerline of the blood vessel path. ### Claim 9 (depends on 8) The computer-implemented method according to claim 8, wherein the forward recurrent neural network and the reverse recurrent neural network are separately trained. ### Claim 10 (depends on 1) The computer-implemented method according to claim 1, further comprising outputting at least one of the candidate stenosis, the range of candidate stenosis, and the degree of stenosis of the blood vessel path. ### Claim 11 A blood vessel stenosis degree prediction system, comprising: an interface configured to receive an image of a blood vessel; a processor configured to: extract a blood vessel path and its centerline based on the image of the blood vessel; determine a candidate stenosis for the blood vessel path; identify image blocks along the centerline of the blood vessel path within a range of candidate stenosis for the blood vessel path determined based on the candidate stenosis; and determine a degree of stenosis for the blood vessel path by applying a trained learning network comprising a convolutional neural network and a recurrent neural network on the image blocks within the range of candidate stenosis. ### Claim 12 (depends on 11) The blood vessel stenosis degree prediction system according to claim 11, wherein the processor is further configured to: predict a healthy radius of the blood vessel path by performing a quadratic regression on blood vessel radius peaks of the blood vessel path; and determine the candidate stenosis for the blood vessel path based on the healthy radius of the blood vessel path. ### Claim 13 (depends on 11) The blood vessel stenosis degree prediction system according to claim 11, wherein to predict the healthy radius of the blood vessel path, the processor is further configured to: predict a reference healthy radius of the blood vessel path by performing a linear regression on the blood vessel radius peaks; and replace the blood vessel radius peaks in the blood vessel path that are lower than the reference healthy radius with the reference healthy radius. ### Claim 14 (depends on 11) The blood vessel stenosis degree prediction system according to claim 11, wherein the range of candidate stenosis for the blood vessel path has the candidate stenosis at its center. ### Claim 15 (depends on 11) The blood vessel stenosis degree prediction system according to claim 11, wherein to determine the degree of stenosis for the blood vessel path, the processor is configured to apply the convolutional neural network and the recurrent neural network sequentially, wherein the convolutional neural network is applied on the image blocks along the centerline of the blood vessel path to generate vectors for the respective image blocks, wherein the recurrent neural network is applied on the vectors. ### Claim 16 (depends on 11) The blood vessel stenosis degree prediction system according to claim 11, wherein the convolutional neural network and the recurrent neural network are jointly trained. ### Claim 17 (depends on 11) The blood vessel stenosis degree prediction system according to claim 11, wherein the recurrent neural network comprises a forward recurrent neural network and a reverse recurrent neural network configured to collectively learn a spatial correlation among the image blocks along the centerline of the blood vessel path. ### Claim 18 (depends on 11) The blood vessel stenosis degree prediction system according to claim 11, further comprising a display configured to output at least one of the candidate stenosis, the range of candidate stenosis, and the degree of stenosis of the blood vessel path. ### Claim 19 A computer-implemented method for training a learning network for predicting a blood vessel stenosis, the learning network comprising a convolutional neural network and a recurrent neural network, comprising: receiving training data including image blocks at a plurality of stenosis locations along a blood vessel path and a labeled degree of stenosis for the stenosis locations; determining a predicted degree of stenosis by applying the learning network to the image blocks in the training data; jointly optimizing a first set of parameters of the convolutional neural network and a second set of parameters of the recurrent neural network against an objective function determined based on the labeled degree of stenosis in the training data and the predicted degree of stenosis; and providing the learning network updated with the jointly optimized first set of parameters and second set of parameters. ### Claim 20 (depends on 19) The computer-implemented method according to claim 19, wherein the recurrent neural network comprises a forward recurrent neural network and a reverse recurrent neural network configured to collectively learn a spatial correlation among the image blocks at the plurality of stenosis locations along the blood vessel path.