A processor for an endoscope according to an aspect is characterized by including: a controller executing program code to perform: acquiring, by the controller, an endoscopic image captured using first system information; calculating, by the controller, parameter on the basis of the endoscopic image acquired by the controller; discriminating a part of a subject using a first learning model that outputs a discrimination result of discriminating the part of the subject in a case in which the calculated parameter is input; outputting second system information using a second learning model that outputs the second system information in a case in which the parameter and the discriminated part of the subject are input; and determining, by the controller, a difference between the second system information output by the second learning model and the first system information.

Systems, devices, and methods for integrating images from various sources and using the same to guide endoscopic procedures are disclosed. An endoscopic system comprises an endoscope to be positioned and navigated in a patient anatomy, and a processor configured to reconstruct a three-dimensional (3D) image of an anatomical target based on at least two images of the anatomical target. The at least two images can be calibrated and registered using respective landmarks. One or more secondary images can be integrated with the reconstructed 3D image. The reconstructed image or the integrated image, along with the endoscope navigation plan, can be displayed to a user. Based on the reconstructed or the integrated image, the processor can generate an endoscope navigation plan for use in an image-guided endoscopic procedure.

A compact, hand-held, robotic assisted endoscopic system configured to derive the position and/or orientation CamPose of a distal part of a single-use portion relative to coordinates HandPose of a reusable portion of an endoscope and display juxtaposed images of an object such a patient's organ being diagnosed or treated in a medical procedure with the endoscope and the distal part of the endoscope, and to provide guidance to the system user with display of images such as prior images of the object, standardized images of the object of tutorial images related to the medical procedure.

Video overlay synchronization for a digital surgical stereoscope is disclosed herein. In an example, a system provides synchronization by detecting how an image changes between frames. The image change corresponds to detecting the regular and predictable movement of human tissue, such as pulsing of a blood vessel. Peaks or maximum extents of tissue movement is tracked overtime to determine an estimation of the regular periodic movement. This periodic movement is used to align a short recording of images corresponding to a fluorescence imaging mode with a live or near-live recording of images in a visible light mode. The frame rate of fluorescence image data is adjusted to ensure close or almost exact alignment of tissue position shown in both the fluorescence image data and visible light image data. The system accordingly provides a fluorescence image overlay on visible light images for tissue that has regular movement patterns.

A program causes a computer to execute processing including: acquiring an endoscope image captured by an endoscope; inputting the acquired endoscope image into a plurality of learning models learned so as to output diagnosis support information regarding a lesion included in the endoscope image; acquiring a plurality of pieces of diagnosis support information output from each of the learning models; and outputting a plurality of pieces of the acquired diagnosis support information and information regarding each of the learning models in association with each other. Alternatively, the program causes the computer to execute the processing of inputting the acquired endoscope image into one learning model, executing a plurality of determination logics to acquire a plurality of pieces of output diagnosis support information, and outputting a plurality of pieces of the acquired diagnosis support information and information regarding each of the learning models in association with each other is executed.

A processor for an endoscope includes: an acquisition unit that acquires a detection value detected by an endoscope or a captured image captured by the endoscope; a specification unit that specifies operation information of a next stage based on the detection value or captured image acquired by the acquisition unit; and an output unit that outputs the operation information specified by the specification unit.

A method including: upon or during the insertion of an endoscope shaft into a trocar sleeve, detecting first brightness values at a distal end of the endoscope shaft by an image sensor over time and detecting a first temporal brightness profile depending on the detected first brightness values and the time, and/or upon or during the withdrawal of the endoscope shaft from the trocar sleeve, detecting second brightness values at the distal end of the endoscope shaft by the image sensor over time and detecting a second temporal brightness profile depending on the detected second brightness values and the time.

Provided are a diagnostic imaging device, diagnostic imaging method, diagnostic imaging program, and learned model with which gastric cancer diagnosis can be carried out in real time during endoscopic examination performed using NBI in combination with a magnifying endoscope. The diagnostic imaging device comprises an endoscopic video image acquisition unit which emits narrow-band light at a subject's stomach and acquires an endoscopic video image captured while the stomach is in a state of magnified observation, and an estimation unit which uses a convolutional neural network, which has been caused to learn using gastric cancer images and non-gastric cancer images as training data, to estimate the presence of gastric cancer in the acquired endoscopic video image, and outputs estimation results.

An endoscope system includes an endoscope that captures a living tissue in a body cavity, and an image processing unit. The endoscope includes an objective lens provided on a front side of a light receiving surface of an image sensor and configured to simultaneously form images of the living tissue, obtained through a plurality of windows, on the light receiving surface as the captured image. The image processing unit includes a three-dimensional expansion processor configured to calculate different directions of a feature part visible through the plurality of windows based on position information in each of images of the feature part, which is distinguishably identified from other parts and included in common in the plurality of images obtained through the plurality of windows in the captured image captured by the endoscope, and to expand two-dimensional information of the images of the feature part to three-dimensional information.

An object of the present invention is to provide a medical image processing apparatus, an endoscope system, a medical image processing method, and a medical image processing program that are capable of displaying a region of interest with appropriate distinguishability. A medical image processing apparatus according to an aspect of the present invention is a medical image processing apparatus including a processor. The processor is configured to perform an image acquisition process of acquiring an observation image of a subject; a region-of-interest recognition process of recognizing a region of interest from the observation image; a tool information recognition process of recognizing tool information from the observation image, the tool information being information on a tool used for treatment of the subject; and a display control process of causing a display device to distinguishably display the observation image in a manner in which the region of interest has distinguishability based on a recognition result of the tool information.