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; 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 acquired endoscopic image is input; acquiring, by the controller, a setting image associated with the discrimination result output by the first learning model; and outputting second system information using a second learning model that outputs the second system information in a case in which the acquired setting image and the part of the subject are input.

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.

An electronic endoscope system includes a plotting unit which plots pixel correspondence points, which correspond to pixels that constitute an intracavitary color image that has a plurality of color components, on a target plane according to color components of the pixel correspondence points, the target plane intersecting the origin of a predetermined color space; an axis setting unit which sets a reference axis in the target plane based on pixel correspondence points plotted on the target plane; and an evaluation value calculating unit which calculates a prescribed evaluation value with respect to the captured image based on a positional relationship between the reference axis and the pixel correspondence points.

An electronic endoscope system includes a plotting unit which plots pixel correspondence points, which correspond to pixels that constitute a color image that has multiple color components, on a first color plane according to color components of the pixel correspondence points, the first color plane intersecting the origin of a predetermined color space; an axis setting unit which sets a predetermined reference axis in the first color plane; a transform unit which defines a second color plane that includes the reference axis, and subjecting the pixel correspondence points on the first color plane to projective transformation onto the second color plane; and an evaluation value calculating unit which calculates a prescribed evaluation value with respect to the color image based on the pixel correspondence points subjected to projective transformation onto the second color plane.

In one embodiment, a computer-implemented method provides a prediction of a medical target variable. The computer-implemented method includes receiving medical imaging data of an examination area, the examination area including a plurality of lesions of an anatomical structure, wherein each lesion of the plurality of lesions of the anatomical structure spaced apart from any other lesion of the plurality of lesions of the anatomical structure; calculating a spread parameter based on the medical imaging data, the spread parameter being indicative of a spread of a spatial distribution of the plurality of lesions of the anatomical structure; calculating the prediction of the medical target variable based on the spread parameter; and providing the prediction of the medical target variable.

A processor for an endoscope includes an image processing unit that obtains a severity of a lesion in which a degree of progression is represented by one value, wherein the image processing unit includes a feature amount calculation unit configured to calculate a first pixel evaluation value indicating a degree of a first feature of appearance appearing in a lesion part, a representative value calculation unit configured to calculate a first representative evaluation value by integrating the first pixel evaluation values, and an integration unit configured to adjust an influence degree of the first representative evaluation value indicating a change in the severity with respect to a change in the first representative evaluation value based on information on a color component of an image, and calculate the severity of the lesion based on at least the first representative evaluation value by using the adjusted influence degree.

A method of processing gastrointestinal images is disclosed. According to embodiments of the present invention, a plurality of images from an endoscope, wherein the plurality of images is captured by the endoscope when the endoscope travels through a GI (gastrointestinal) tract are received. Corresponding anatomical parts associated with the plurality of images are then determined. A selected image process is applied to a target image from the plurality of images, where one or more parameters of the selected image process are determined according to a corresponding anatomical part determined for the target image.

Systems and methods for de-smoking images of a surgical scene are described. Methods include receiving a video of a surgical scene including an image frame. Methods include determining that the image frame includes a smoke occlusion. Methods include determining an estimated un-occluded color of one or more pixels of the image frame using a lookup table, the lookup table mapping between a color space and a set of color bins including the estimated un-occluded color. Methods include determining a respective estimated true color for the one or more pixels of the subset using the imaged color, the estimated un-occluded color, and the smoke color. Methods also include generating a de-smoked image frame using the respective estimated true colors of the one or more pixels, the de-smoked image exhibiting a reduction of the smoke occlusion relative to the image frame.

Described herein are methods and systems for analyzing a sample by applying time resolved laser induced fluorescence spectroscopy to the sample to measure lifetime time decay profile data relating to the sample, and applying multivariate analysis to process the data so as to classify a sample as, for example, normal or abnormal. The sample may be cells, fluid or tissue from any organ. The sample may be in vitro or in vivo. The data may be obtained in situ or in vitro.

A computer-implemented method for evaluating colonoscopy performance includes: (S1) splitting a video acquired during a colonoscopy examination into a plurality of colonoscopy images; (S2) assigning each of the colonoscopy images into a fold-inspection group or a non-fold-inspection group according to a first classification criterion and a second classification criterion, wherein the first classification criterion comprises at least one of clarity, exposure, level of tissue wrinkling, and level of occlusion in each of the colonoscopy images; and the second classification criterion comprises at least one of an amount of haustrum, an amount of colonic lumen, and a position of the colonic lumen in each of the colonoscopy images; and (S3) determining a performance rating of the colonoscopy examination according to an elapsed time of the fold-inspection group. The method classifies colonoscopy images more accurately and reliably, thereby providing an effective tool for quality assessment and guidance of colonoscopy examinations.