# US 11,443,428 B2 · Systems and methods for probablistic segmentation in anatomical image processing > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,443,428 B2 - Document ID: US11443428B2 - Status: Granted - Kind: B2 - Application number: 16/790,037 - Filed: 2020-02-13 - Published: 2020-06-11 - Granted: 2022-09-13 - Earliest claimed: 2016-12-23 - Assignees: HeartFlow, Inc. - Applicants: HeartFlow, Inc. - Inventors: Peter Kersten Petersen; Michiel Schaap; Leo Grady - Web page: https://patentgrep.com/patent/US11443428B2 ## CPC - G06V 10/764: using classification, e.g. of video objects - G06F 18/2415: based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate - G06T 7/0014: {using an image reference approach} - G06T 7/11: Region-based segmentation - G06T 7/13: Edge detection - G06T 7/143: involving probabilistic approaches, e.g. Markov random field [MRF] modelling - G06T 7/60: Analysis of geometric attributes - A61B 6/504: {for diagnosis of blood vessels, e.g. by angiography} - G06T 2200/04: involving 3D image data - G06T 2207/20076: Probabilistic image processing - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30101: Blood vessel; Artery; Vein; Vascular - G06T 2207/30104: Vascular flow; Blood flow; Perfusion - G06T 2207/30172: Centreline of tubular or elongated structure - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract Systems and methods are disclosed for performing probabilistic segmentation in anatomical image analysis, using a computer system. One method includes receiving a plurality of images of an anatomical structure; receiving one or more geometric labels of the anatomical structure; generating a parameterized representation of the anatomical structure based on the one or more geometric labels and the received plurality of images; mapping a region of the parameterized representation to a geometric parameter of the anatomical structure; receiving an image of a patient's anatomy; and generating a probability distribution for a patient-specific segmentation boundary of the patient's anatomy, based on the mapping of the region of the parameterized representation of the anatomical structure to the geometric parameter of the anatomical structure. ## Claims ### Claim 1 A computer-implemented method of performing probabilistic segmentation in anatomical image analysis, the method comprising: receiving a plurality of images of an anatomical structure; receiving one or more geometric labels of the anatomical structure; determining a parameterized representation of the anatomical structure based on the one or more geometric labels and the received plurality of images; receiving an image of a patient's anatomy; computing a probability distribution for a patient-specific segmentation boundary associated with the patient's anatomy, based on the parameterized representation; computing at least one statistical confidence value based on the probability distribution; generating a segmentation boundary associated with the patient's anatomy based on the probability distribution and the at least one statistical confidence value; determining a region of the segmentation boundary associated with the statistical confidence value below a predetermined threshold; and generating a display and/or notification of the region with the statistical confidence value below the predetermined threshold. ### Claim 2 (depends on 1) The method of claim 1, further comprising: determining a blood flow characteristic based on the segmentation boundary and the statistical confidence value. ### Claim 3 (depends on 1) The method of claim 1, further comprising: generating an interactive user interface or prompt based on the statistical confidence value. ### Claim 4 (depends on 1) The method of claim 1, further comprising: predicting a physiological or biomechanical property of the patient's anatomy, based on the statistical confidence value. ### Claim 5 (depends on 1) The method of claim 1, further comprising: segmenting the image of the patient's anatomy, based on the segmentation boundary. ### Claim 6 (depends on 1) The method of claim 1, wherein the one or more geometric labels include annotations of a vessel lumen boundary, centerline, surface, or a combination thereof. ### Claim 7 (depends on 1) The method of claim 1, further comprising: determining a distance from a point on a centerline of the anatomical structure to a point on a lumen boundary of the anatomical structure; mapping a region of the parameterized representation to the determined distance; and generating the probability distribution, based on the mapping of the region of the parameterized representation of the anatomical structure to the determined distance. ### Claim 8 (depends on 1) The method of claim 1, further comprising: generating the segmentation boundary further using surface reconstruction. ### Claim 9 A system for performing probabilistic segmentation in anatomical image analysis, the system comprising: at least one server storing instructions for performing probabilistic segmentation in anatomical image analysis; and at least one processor configured to execute the instructions to perform a method including: receiving a plurality of images of an anatomical structure; receiving one or more geometric labels of the anatomical structure; determining a parameterized representation of the anatomical structure based on the one or more geometric labels and the received plurality of images; receiving an image of a patient's anatomy; computing a probability distribution for a patient-specific segmentation boundary associated with the patient's anatomy, based on the parameterized representation; computing at least one statistical confidence value based on the probability distribution; generating a segmentation boundary associated with the patient's anatomy based on the probability distribution and the at least one statistical confidence value; determining a region of the segmentation boundary associated with the statistical confidence value below a predetermined threshold; and generating a display and/or notification of the region with the statistical confidence value below the predetermined threshold. ### Claim 10 (depends on 9) The system of claim 9, further comprising: determining a blood flow characteristic based on the segmentation boundary and the statistical confidence value. ### Claim 11 (depends on 9) The system of claim 9, wherein the system is further configured for: generating an interactive user interface or prompt based on the statistical confidence value. ### Claim 12 (depends on 9) The system of claim 9, wherein the system is further configured for: predicting a physiological or biomechanical property of the patient's anatomy, based on the statistical confidence value. ### Claim 13 (depends on 9) The system of claim 9, wherein the system is further configured for: segmenting the image of the patient's anatomy, based on the segmentation boundary. ### Claim 14 (depends on 9) The system of claim 9, wherein the one or more geometric labels include annotations of a vessel lumen boundary, centerline, surface, or a combination thereof. ### Claim 15 (depends on 9) The system of claim 9, wherein the system is further configured for: determining a distance from a point on a centerline of the anatomical structure to a point on a lumen boundary of the anatomical structure; mapping a region of the parameterized representation to the determined distance; and generating the probability distribution, based on the mapping of the region of the parameterized representation of the anatomical structure to the determined distance. ### Claim 16 (depends on 15) The system of claim 15, wherein the system is further configured for: generating a statistical confidence score of the determined distance. ### Claim 17 A non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions, that when executed by a computer cause the computer to perform a method of performing probabilistic segmentation in anatomical image analysis, the method comprising: receiving a plurality of images of an anatomical structure; receiving one or more geometric labels of the anatomical structure; determining a parameterized representation of the anatomical structure based on the one or more geometric labels and the received plurality of images; receiving an image of a patient's anatomy; computing a probability distribution for a patient-specific segmentation boundary associated with the patient's anatomy, based on the parameterized representation; computing at least one statistical confidence value based on the probability distribution; generating a segmentation boundary associated with the patient's anatomy based on the probability distribution and the at least one statistical confidence value; determining a region of the segmentation boundary associated with the statistical confidence value below a predetermined threshold; and generating a display and/or notification of the region with the statistical confidence value below the predetermined threshold. ### Claim 18 (depends on 17) The non-transitory computer readable medium of claim 17, the method further comprising: determining a blood flow characteristic based on the segmentation boundary and the statistical confidence value. ### Claim 19 (depends on 17) The non-transitory computer readable medium of claim 17, the method further comprising: generating an interactive user interface or prompt based on the statistical confidence value. ### Claim 20 (depends on 17) The non-transitory computer readable medium of claim 17, the method further comprising: segmenting the image of the patient's anatomy, based on the segmentation boundary.