# US 2026/0073547 A1 · Systems and methods for generating three-dimensional measurements using endoscopic video data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0073547 A1 - Document ID: US20260073547A1 - Status: Published application - Kind: A1 - Application number: 19/392,081 - Filed: 2025-11-17 - Published: 2026-03-12 - Earliest claimed: 2021-05-25 - Assignees: Stryker Corporation - Applicants: Stryker Corporation - Inventors: Cole Kincaid HUNTER; Brian FOUTS; Sanskruti MASKE - Web page: https://patentgrep.com/patent/US20260073547A1 ## CPC - G06T 7/60: Analysis of geometric attributes - A61B 1/000094: {extracting biological structures} - A61B 34/10: Computer-aided planning, simulation or modelling of surgical operations - G01S 17/894: Three-dimensional [3D] imaging with simultaneous measurement of time-of-flight at a two-dimensional [2D] array of receiver pixels, e.g. time-of-flight cameras or flash lidar - G06T 7/11: Region-based segmentation - G06T 7/246: using feature-based methods, e.g. the tracking of corners or segments - G06T 7/579: from motion - G06T 7/593: from stereo images - G06T 17/00: Three-dimensional [3D] modelling for computer graphics - A61B 2034/105: {Modelling of the patient, e.g. for ligaments or bones} - G06T 2200/24: involving graphical user interfaces [GUIs] - G06T 2207/10012: Stereo images - G06T 2207/10016: Video; Image sequence - G06T 2207/10068: Endoscopic image - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30204: Marker - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES ## Abstract Described herein are methods for tracking a location of a fixed point in endoscopic images. Endoscopic video data is received. A selection from a user of a location within a first two-dimensional image on which to place a graphical marker is received. The graphical marker is placed on the first two-dimensional image at the location within the first two-dimensional image. A first location of an interest point within the first two-dimensional image is detected by applying a machine learning model to the first two-dimensional image to identify the interest point. A second location of the interest point within a second two-dimensional image of the video data is detected by applying the machine learning model to the second two-dimensional image to identify the interest point. The graphical marker is placed on the second two-dimensional image in accordance with the detection of the second location of the interest point. ## Claims ### Claim 1 A method for tracking a location of a fixed point in endoscopic images, the method comprising: receiving video data captured by an endoscopic imaging device configured to image an internal area of a patient; receiving a selection from a user of a location within a first two-dimensional image of the video data on which to place a graphical marker; placing the graphical marker on the first two-dimensional image at the location within the first two-dimensional image; detecting a first location of an interest point within the first two-dimensional image, wherein detecting the first location of the interest point comprises applying a machine learning model to the first two-dimensional image to identify the interest point; detecting a second location of the interest point within a second two-dimensional image of the video data, wherein detecting the second location of the interest point comprises applying the machine learning model to the second two-dimensional image to identify the interest point; and placing the graphical marker on the second two-dimensional image in accordance with the detection of the second location of the interest point. ### Claim 2 (depends on 1) The method of claim 1, comprising: prior to placing the graphical marker on the second two-dimensional image, determining a distance and direction between the first location and the second location. ### Claim 3 (depends on 2) The method of claim 2, wherein placing the graphical marker on the second two-dimensional image is based on the determined distance and direction between the first location and the second location. ### Claim 4 (depends on 2) The method of claim 2, wherein determining the distance and direction between the first location and the second location comprises matching the interest point in the first two-dimensional image with the interest point in the second two-dimensional image using a k-nearest neighbors algorithm. ### Claim 5 (depends on 4) The method of claim 4, wherein determining the distance and direction between the first location and the second location further comprises generating a motion matrix that indicates movement of the endoscopic imaging device using one or more homographic transformations. ### Claim 6 (depends on 1) The method of claim 1, wherein detecting a second location of the interest point within the second two-dimensional image comprises determining that the second location of the interest point is beyond a boundary of the second two-dimensional image. ### Claim 7 (depends on 6) The method of claim 6, wherein placing the graphical marker on the second two-dimensional image comprises placing the graphical marker at an edge of the second two-dimensional image. ### Claim 8 (depends on 7) The method of claim 7, wherein the graphical marker indicates a direction of the interest point relative to the second two-dimensional image. ### Claim 9 (depends on 1) The method of claim 1, wherein the first two-dimensional image and/or the second two-dimensional image is captured from the video data based on a user indication to capture the respective two-dimensional image. ### Claim 10 (depends on 1) The method of claim 1, wherein the first two-dimensional image and/or the second two-dimensional image is captured from the video data automatically without prompting by the user. ### Claim 11 (depends on 1) The method of claim 1, wherein the graphical marker comprises a flag, a graphical pin, or an arrow. ### Claim 12 (depends on 1) The method of claim 1, wherein the machine learning model comprises a Greedily Learned Accurate Match Points (GLAM) model, a Self-Supervised Interest Point Detector (Superpoint) model, a Magicpoint model, or an Unsuperpoint model. ### Claim 13 A system for tracking a location of a fixed point in endoscopic images, the system comprising: a memory; and one or more processors, wherein the memory stores one or more programs that, when executed by the one or more processors, cause the one or more processors to: receive video data captured by an endoscopic imaging device configured to image an internal area of a patient; receive a selection from a user of a location within a first two-dimensional image of the video data on which to place a graphical marker; place the graphical marker on the first two-dimensional image at the location within the first two-dimensional image; detect a first location of an interest point within the first two-dimensional image, wherein detecting the first location of an interest point comprises applying a machine learning model to the first two-dimensional image to identify the interest point; detect a second location of the interest point within a second two-dimensional image of the video data, wherein detecting the second location of the interest point comprises applying the machine learning model to the second two-dimensional image to identify the interest point; and place the graphical marker on the second two-dimensional image in accordance with the detection of the second location of the interest point. ### Claim 14 (depends on 13) The system of claim 13, wherein the one more programs further cause the one or more processors to: prior to placing the graphical marker on the second two-dimensional image, determine a distance and direction between the first location and the second location. ### Claim 15 (depends on 14) The system of claim 14, wherein placing the graphical marker on the second two-dimensional image is based on the determined distance and direction between the first location and the second location. ### Claim 16 (depends on 14) The system of claim 14, wherein determining the distance and direction between the first location and the second location comprises matching the interest point in the first two-dimensional image with the interest point in the second two-dimensional image using a k-nearest neighbors algorithm. ### Claim 17 (depends on 16) The system of claim 16, wherein determining the distance and direction between the first location and the second location further comprises generating a motion matrix that indicates movement of the endoscopic imaging device using one or more homographic transformations. ### Claim 18 (depends on 13) The system of claim 13, wherein detecting a second location of the interest point within the second two-dimensional image comprises determining that the second location of the interest point is beyond a boundary of the second two-dimensional image. ### Claim 19 (depends on 18) The system of claim 18, wherein placing the graphical marker on the second two-dimensional image comprises placing the graphical marker at an edge of the second two-dimensional image such that the graphical marker indicates a direction of the interest point relative to the second two-dimensional image. ### Claim 20 A non-transitory computer readable storage medium storing one or more programs for execution by one or more processors of a computing system, the one or more programs including instructions that, when executed by the one or more processors, cause the computing system to: receive video data captured by an endoscopic imaging device configured to image an internal area of a patient; receive a selection from a user of a location within a first two-dimensional image of the video data on which to place a graphical marker; place the graphical marker on the first two-dimensional image at the location within the first two-dimensional image; detect a first location of an interest point within the first two-dimensional image, wherein detecting the first location of the interest point comprises applying a machine learning model to the first two-dimensional image to identify the interest point; detect a second location of the interest point within a second two-dimensional image of the video data, wherein detecting the second location of the interest point comprises applying the machine learning model to the second two-dimensional image to identify the interest point; and place the graphical marker on the second two-dimensional image in accordance with the detection of the second location of the interest point.