# US 2022/0284571 A1 · Method and system for automatic calcium scoring from medical images > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0284571 A1 - Document ID: US20220284571A1 - Status: Published application - Kind: A1 - Application number: 17/505,917 - Filed: 2021-10-20 - Published: 2022-09-08 - Earliest claimed: 2021-03-05 - Assignees: Shenzhen Keya Medical Technology Corporation - Applicants: Shenzhen Keya Medical Technology Corporation - Inventors: Hao-Yu Yang; Junjie Bai; Youbing Yin; Qi Song - Web page: https://patentgrep.com/patent/US20220284571A1 ## CPC - G06T 7/0012: {Biomedical image inspection} - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06N 20/00: Machine learning - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30008: Bone - G06T 2207/30101: Blood vessel; Artery; Vein; Vascular - G06T 2210/41: Medical - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Embodiments of the disclosure provide systems and methods for medical image analysis. A method may include receiving a medical image acquired of a subject by an image acquisition device. The method may also include applying a calcium detection model to detect at least one calcium region relevant in determining a calcium score from the medical image. The method may further include applying a score regression learning model to the at least one calcium region to determine a calcium score for the medical image. The method may additionally include providing the determined calcium score of the medical image for a diagnosis of the subject. ## Claims ### Claim 1 A system for determining a calcium score from a medical image, comprising: a communication interface configured to receive the medical image acquired of a subject by an image acquisition device; and at least one processor, configured to: apply a calcium detection model to detect at least one calcium region relevant in determining a calcium score from the medical image; apply a score regression learning model to the at least one calcium region to determine a calcium score for the medical image; and provide the determined calcium score of the medical image for a diagnosis of the subject. ### Claim 2 (depends on 1) The system of claim 1, wherein, to detect at least one calcium region, the at least one processor is further configured to: detect a region of interest (ROI) containing a target vessel in the medical image; and detect each calcium region for the medical image by detecting a calcium-containing region in the ROI containing the target vessel. ### Claim 3 (depends on 1) The system of claim 1, wherein the calcium detection model includes one or more of an active shape model, adaptive thresholding, support vector machine (SVM) classifier, or deep neural network-based model, to identify the at least one calcium region from the medical image. ### Claim 4 (depends on 1) The system of claim 1, wherein the at least one processor is further configured to: classify the medical image into one of preset modalities; and adjust parameters for the score regression learning model used to determine the calcium score, based on the classified modality of the medical image. ### Claim 5 (depends on 1) The system of claim 1, wherein, to detect the at least one calcium region, the at least one processor is further configured to: determine a bounding box for each calcium region, wherein, to determine the calcium score for the medical image, the at least one processor is further configured to: determine a regional calcium score for each bounding box; and determine the calcium score for the medical image based on the regional calcium score for the at least one calcium region. ### Claim 6 (depends on 1) The system of claim 1, wherein, to detect the at least one calcium region, the at least one processor is further configured to: detect a voxel-wise calcium mask from the medical image, wherein the voxel-wise calcium mask including a plurality of voxels, wherein, to determine the calcium score for the medical image, the at least one processor is further configured to: determine a regional calcium score for each voxel; and determine the calcium score for the medical image by summing up the regional calcium scores of the respective voxels included in the voxel-wise calcium mask, ### Claim 7 (depends on 1) The system of claim 1, wherein, to detect the at least one calcium region, the at least one processor is further configured to: detect a plurality of calcium regions from the medical image, wherein the score regression learning model is a recursive neural network, wherein, to determine the calcium score for the medical image, the at least one processor is further configured to: apply the recursive neural network to the plurality of calcium regions collectively to regress the calcium score in the whole medical image. ### Claim 8 (depends on 1) The system of claim 1, wherein the calcium detection model and the score regression learning model are jointly trained. ### Claim 9 (depends on 1) The system of claim 1, wherein the score regression learning model is trained by determining sample regional calcium scores based on a ground truth calcium score in training data and distributing the sample regional calcium scores to corresponding ground truth calcium regions derived from the training data. ### Claim 10 (depends on 1) The system of claim 1, wherein the calcium detection model or the score regression learning model is trained using training data including non-contrast computed tomography (NCCT) images or virtual NCCT images and their corresponding ground truth calcium scores. ### Claim 11 A computer-implemented method for determining a calcium score from a medical image, comprising: receiving the medical image acquired of a subject by an image acquisition device; applying a calcium detection model to detect at least one calcium region relevant in determining a calcium score from the medical image; applying a score regression learning model to the at least one calcium region to determine a calcium score for the medical image; and providing the determined calcium score of the medical image for a diagnosis of the subject. ### Claim 12 (depends on 11) The computer-implemented method of claim 11, wherein detecting at least one calcium region relevant from the medical image further comprises: applying a region of interest (ROI) module to detect a region of interest (ROI) containing a target vessel in the medical image; and detecting each calcium region for the medical image by detecting a calcium-containing region in the ROI containing the target vessel. ### Claim 13 (depends on 11) The computer-implemented method of claim 11, wherein the calcium detection model includes one or more of an active shape model, adaptive thresholding, support vector machine (SVM) classifier, or deep neural network-based model, to identify the at least one calcium region from the medical image. ### Claim 14 (depends on 11) The computer-implemented method of claim 11, further comprising: applying a modality recognition module to classify the medical image into one of preset modalities; and adjusting parameters for the score regression learning model used to determine the calcium score, based on the classified modality of the medical image. ### Claim 15 (depends on 11) The computer-implemented method of claim 11, wherein determining the calcium score for the calcium region further comprises: determining a bounding box for the calcium region; and determining the calcium score for the medical image further comprises: determining a regional calcium score for each bounding box; and determining the calcium score for the medical image based on the regional calcium score for the at least one calcium region. ### Claim 16 (depends on 11) The computer-implemented method of claim 11, wherein detecting the at least one calcium region further comprises: detecting a voxel-wise calcium mask from the medical image, wherein the voxel-wise calcium mask including a plurality of voxels; and determining the calcium score for the medical image further comprises: determining a regional calcium score for each voxel; and determining the calcium score for the medical image by summing up the regional calcium scores of the respective voxels included in the voxel-wise calcium mask. ### Claim 17 (depends on 11) The computer-implemented method of claim 11, wherein. detecting the at least one calcium region further comprises: detecting a plurality of calcium regions from the medical image; and the score regression learning model is a recursive neural network, and determining the calcium score for the medical image further comprises: applying a recursive neural network to the plurality of calcium regions collectively to regress the calcium score in the whole medical image. ### Claim 18 (depends on 11) The computer-implemented method of claim 11, wherein the calcium detection model and the score regression learning model are jointly trained. ### Claim 19 (depends on 11) The computer-implemented method of claim 11, wherein the score regression learning model is trained by determining sample regional calcium scores based on a ground truth calcium score in training data and distributing the sample regional calcium scores to corresponding ground truth calcium regions derived from the training data. ### Claim 20 A non-transitory computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by at least one processor, performs a method for determining a calcium score from a medical image, the method comprising: receiving the medical image acquired of a subject by an image acquisition device; applying a calcium detection model to detect at least one calcium region relevant in determining a calcium score from the medical image; applying a score regression learning model to the at least one calcium region to determine a calcium score for the medical image; and providing the deter mined calcium score of the medical image for a diagnosis of the subject.