# US 12,112,483 B2 · Systems and methods for anatomic structure segmentation in image analysis > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,112,483 B2 - Document ID: US12112483B2 - Status: Granted - Kind: B2 - Application number: 18/171,915 - Filed: 2023-02-21 - Published: 2023-06-22 - Granted: 2024-10-08 - Earliest claimed: 2017-05-09 - Assignees: HeartFlow, Inc. - Applicants: HeartFlow, Inc. - Inventors: Leo Grady; Peter Kersten Petersen; Michiel Schaap; David Lesage - Web page: https://patentgrep.com/patent/US12112483B2 ## CPC - G06T 7/174: involving the use of two or more images - G06T 7/0012: {Biomedical image inspection} - G06T 7/12: Edge-based segmentation - G06T 7/149: involving deformable models, e.g. active contour models - G06T 2200/04: involving 3D image data - G06T 2207/10072: Tomographic images - G06T 2207/10081: Computed x-ray tomography [CT] - G06T 2207/10088: Magnetic resonance imaging [MRI] - G06T 2207/10104: Positron emission tomography [PET] - G06T 2207/10108: Single photon emission computed tomography [SPECT] - G06T 2207/10132: Ultrasound image - G06T 2207/20081: Training; Learning - G06T 2207/20112: Image segmentation details - G06T 2207/30004: Biomedical image processing - G06T 2207/30101: Blood vessel; Artery; Vein; Vascular - G06V 2201/03: Recognition of patterns in medical or anatomical images - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Systems and methods are disclosed for anatomic structure segmentation in image analysis, using a computer system. One method includes: receiving an annotation and a plurality of keypoints for an anatomic structure in one or more images; computing distances from the plurality of keypoints to a boundary of the anatomic structure; training a model, using data in the one or more images and the computed distances, for predicting a boundary in the anatomic structure in an image of a patient's anatomy; receiving the image of the patient's anatomy including the anatomic structure; estimating a segmentation boundary in the anatomic structure in the image of the patient's anatomy; and predicting, using the trained model, a boundary location in the anatomic structure in the image of the patient's anatomy by generating a regression of distances from keypoints in the anatomic structure in the image of the patient's anatomy to the estimated boundary. ## Claims ### Claim 1 A computer-implemented method of machine-learning based anatomic structure segmentation in image analysis, the method comprising: receiving image data of an anatomic structure of a patient; obtaining an annotation of the anatomic structure; determining, based on the annotation, one or more keypoints; determining a respective distance between each keypoint and a boundary of the anatomic structure; determining respective intensities along a respective ray associated with each keypoint; training a patient-specific Convolutional Neural Network (CNN), based on the respective distance and the respective intensities of each keypoint, to predict sub-pixel or sub-voxel locations of the boundary of the anatomic structure; and generating, using the trained patient-specific CNN a sub-pixel or sub-voxel boundary of the anatomic structure. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the generated sub-pixel or sub-voxel boundary of the anatomic structure is a continuous boundary. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the image data includes a plurality of successive frames that are orthogonal to a centerline of the anatomic structure. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, wherein generating the sub-pixel or sub-voxel boundary of the anatomic structure includes generating a respective boundary portion for each frame of the plurality of successive frames. ### Claim 5 (depends on 4) The computer-implemented method of claim 4, wherein generating the sub-pixel or sub-voxel boundary of the anatomic structure further includes generating a continuous three-dimensional surface of the anatomic structure based on the respective boundary portions. ### Claim 6 (depends on 3) The computer-implemented method of claim 3, wherein the annotation includes an identification, in each frame of the plurality of successive frames, of the centerline of the anatomic structure. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the anatomic structure includes a blood vessel. ### Claim 8 A system for performing machine-learning based anatomic structure segmentation in image analysis, comprising: at least one memory storing instructions and a Convolutional Neural Network (CNN); and at least one processor operatively connected to the at least one memory and configured to execute the instructions to perform operations, including: receiving image data of an anatomic structure of a patient; obtaining an annotation of the anatomic structure; determining, based on the annotation, one or more keypoints; determining a respective distance between each keypoint and a boundary of the anatomic structure; determining respective intensities along a respective ray associated with each keypoint; training the CNN, based on the respective distance and the respective intensities of each keypoint, to be patient-specific and to predict sub-pixel or sub-voxel locations of the boundary of the anatomic structure; and generating, using the trained patient-specific CNN a sub-pixel or sub-voxel boundary of the anatomic structure. ### Claim 9 (depends on 8) The system of claim 8, wherein the generated sub-pixel or sub-voxel boundary of the anatomic structure is a continuous boundary. ### Claim 10 (depends on 8) The system of claim 8, wherein the image data includes a plurality of successive frames that are orthogonal to a centerline of the anatomic structure. ### Claim 11 (depends on 10) The system of claim 10, wherein generating the sub-pixel or sub-voxel boundary of the anatomic structure includes generating a respective boundary portion for each frame of the plurality of successive frames. ### Claim 12 (depends on 11) The system of claim 11, wherein generating the sub-pixel or sub-voxel boundary of the anatomic structure further includes generating a continuous three-dimensional surface of the anatomic structure based on the respective boundary portions. ### Claim 13 (depends on 10) The system of claim 10, wherein the annotation includes an identification, in each frame of the plurality of successive frames, of the centerline of the anatomic structure. ### Claim 14 (depends on 8) The system of claim 8, wherein the anatomic structure includes a blood vessel. ### Claim 15 A non-transitory computer-readable medium comprising instructions for machine-learning based anatomic structure segmentation in image analysis, the instructions executable by one or more processors to perform operations, including: receiving image data of an anatomic structure of a patient; obtaining an annotation of the anatomic structure; determining, based on the annotation, one or more keypoints; determining a respective distance between each keypoint and a boundary of the anatomic structure; determining respective intensities along a respective ray associated with each keypoint; training a patient-specific Convolutional Neural Network (CNN), based on the respective distance and the respective intensities of each keypoint, to predict sub-pixel or sub-voxel locations of the boundary of the anatomic structure; and generating, using the trained patient-specific CNN a sub-pixel or sub-voxel boundary of the anatomic structure. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the generated sub-pixel or sub-voxel boundary of the anatomic structure is a continuous boundary. ### Claim 17 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the image data includes a plurality of successive frames that are orthogonal to a centerline of the anatomic structure. ### Claim 18 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein generating the sub-pixel or sub-voxel boundary of the anatomic structure includes: generating a respective boundary portion for each frame of the plurality of successive frames; and generating a continuous three-dimensional surface of the anatomic structure based on the respective boundary portions. ### Claim 19 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the annotation includes an identification, in each frame of the plurality of successive frames, of the centerline of the anatomic structure. ### Claim 20 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the anatomic structure includes a blood vessel.