# US 2025/0095191 A1 · Image processing device, display device, endoscope device, image processing method, image processing program, trained model, trained model generation method, and trained model generation program > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0095191 A1 - Document ID: US20250095191A1 - Status: Published application - Kind: A1 - Application number: 18/970,897 - Filed: 2024-12-06 - Published: 2025-03-20 - Earliest claimed: 2022-07-19 - Assignees: FUJIFILM Corporation - Applicants: Fujifilm Corporation - Inventors: Masaaki OOSAKE - Web page: https://patentgrep.com/patent/US20250095191A1 ## CPC - G06T 7/00: Image analysis - G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80) - A61B 1/000094: {extracting biological structures} - A61B 1/000096: {using artificial intelligence} - A61B 1/0005: {combining images e.g. side-by-side, superimposed or tiled} - G06N 3/02: Neural networks - G06N 3/045: Combinations of networks - G06N 3/08: Learning methods - G06N 20/00: Machine learning - G06T 7/0012: {Biomedical image inspection} - G06T 7/11: Region-based segmentation - G06T 7/73: using feature-based methods - G16H 40/63: for local operation - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - A61B 34/25: {User interfaces for surgical systems} - A61B 90/30: Devices for illuminating a surgical field, the devices having an interrelation with other surgical devices or with a surgical procedure - A61B 2034/2065: {Tracking using image or pattern recognition} - A61B 2090/365: {augmented reality, i.e. correlating a live optical image with another image} - G06T 2207/10068: Endoscopic image - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30028: Colon; Small intestine - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract An image processing device includes a processor, in which the processor acquires a lumen direction that is a direction in which an endoscope is inserted, from an image obtained by imaging a tubular organ via a camera provided in the endoscope, in accordance with a trained model obtained through machine learning based on a positional relationship between a plurality of division regions obtained by dividing the image and a lumen corresponding region included in the image, and outputs lumen direction information that is information indicating the lumen direction. ## Claims ### Claim 1 An image processing device comprising: a processor, wherein the processor is configured to: acquire a lumen direction that is a direction in which an endoscope is inserted, from an image obtained by imaging a tubular organ via a camera provided in the endoscope, in accordance with a trained model obtained through machine learning based on a positional relationship between a plurality of division regions obtained by dividing the image and a lumen corresponding region included in the image, and output lumen direction information that is information indicating the lumen direction. ### Claim 2 (depends on 1) The image processing device according to claim 1, wherein the lumen corresponding region is a region in a predetermined range including a lumen region in the image. ### Claim 3 (depends on 1) The image processing device according to claim 1, wherein the lumen corresponding region is an end part of an observation range of the camera in a direction in which a position of the lumen region is estimated from a fold region in the image. ### Claim 4 (depends on 1) The image processing device according to claim 1, wherein a direction of a division region overlapping the lumen corresponding region among the plurality of division regions is the lumen direction. ### Claim 5 (depends on 1) The image processing device according to claim 1, wherein the trained model is a data structure configured to cause the processor to estimate a position of the lumen region based on a shape and/or an orientation of a fold region in the image. ### Claim 6 (depends on 1) The image processing device according to claim 1, wherein the lumen direction is a direction in which a division region having a largest area overlapping the lumen corresponding region in the image among the plurality of division regions is present. ### Claim 7 (depends on 1) The image processing device according to claim 1, wherein the lumen direction is a direction in which, among the plurality of division regions, a first division region that is a division region having a largest area overlapping the lumen corresponding region in the image is present and a direction in which a second division region that is a division region having a second largest area overlapping the lumen corresponding region following the first division region is present. ### Claim 8 (depends on 1) The image processing device according to claim 1, wherein the division regions include a central region of the image and a plurality of radial regions that are present radially from the central region toward an outer edge of the image. ### Claim 9 (depends on 8) The image processing device according to claim 8, wherein eight radial regions are present radially. ### Claim 10 (depends on 1) The image processing device according to claim 1, wherein the division regions include a central region of the image and a plurality of peripheral regions present on an outer edge side of the image with respect to the central region. ### Claim 11 (depends on 1) The image processing device according to claim 1, wherein the division regions are obtained by dividing the image into regions in three or more directions toward an outer edge of the image with a center of the image as a starting point. ### Claim 12 (depends on 1) The image processing device according to claim 1, wherein the division regions include a central region of the image and a plurality of peripheral regions present on an outer edge side of the image with respect to the central region, and the peripheral regions are obtained by dividing the outer edge side of the image with respect to the central region in three or more directions from the central region toward an outer edge of the image. ### Claim 13 (depends on 1) A display device that displays information corresponding to the lumen direction information output by the processor of the image processing device according to claim 1. ### Claim 14 (depends on 1) An endoscope device comprising: the image processing device according to any one of claim 1; and the endoscope. ### Claim 15 An image processing method comprising: acquiring a lumen direction that is a direction in which an endoscope is inserted, from an image obtained by imaging a tubular organ via a camera provided in the endoscope, in accordance with a trained model obtained through machine learning based on a positional relationship between a plurality of division regions obtained by dividing the image and a lumen corresponding region included in the image; and outputting lumen direction information that is information indicating the lumen direction. ### Claim 16 A non-transitory computer-readable storage medium storing an image processing program executable by a first computer to execute image processing comprising: acquiring a lumen direction that is a direction in which an endoscope is inserted, from an image obtained by imaging a tubular organ via a camera provided in the endoscope, in accordance with a trained model obtained through machine learning based on a positional relationship between a plurality of division regions obtained by dividing the image and a lumen corresponding region included in the image; and outputting lumen direction information that is information indicating the lumen direction.