Endoscopic image analysis, endoscopic procedure analysis, and/or component control systems, methods and techniques are disclosed that can analyze images of an endoscopic system and/or affect an endoscopic system to enhance operation, user and patient experience, and usability of image data and other case data.

A computer aided assistance system for use in endoscopic colonoscopy procedures. The computer aided assistance system including: at least one videoendoscopic instrument configured to capture image data; a controller comprising hardware, the controller being connected with the at least one videoendoscopic instrument; and a display connected or integral with the controller, wherein the controller being configured to automatically select a treatment guideline based on a combination of both a size and a classification of a lesion shown in the image data and to display the selected treatment guideline on the display.

A method is provided for enhancing video images in a medical device. The method includes receiving a first image frame and a second image frame from an image sensor. First image sub-blocks are generated by dividing the first image frame. Second image sub-blocks are generated by dividing the second image frame based on the first image sub-blocks. Histogram data of the first image sub-blocks is generated. Histogram data of the second image sub-blocks is generated based on the histogram data of the first image sub-blocks. A histogram enhanced image frame is generated based on the histogram data of the second image sub-blocks. A video image stream is generated based on the histogram enhanced image frame.

Systems and methods for processing colon image data are provided. Image data related to a first ROI may be obtained, wherein the first ROI may include a soft tissue represented by a plurality of voxels, and each voxel may have a voxel value. A first virtual scene may be visualized based on the image data, wherein the first virtual scene may reveal at least one portion of the first ROI. A collision detection may be performed between at least one portion of the first ROI and a virtual object in the first virtual scene. A feedback force may be determined from at least one portion of the first ROI based on the collision detection. At least one of the plurality of voxels corresponding to a second ROI may be determined based on the feedback force, wherein the second ROI may relate to the soft tissue in the first ROI.

A method of analyzing stool material captured during a preoperative bowel cleansing preparation to assess the adequacy of said preoperative bowel cleansing by color-comparison with a known specimen.

An image processing method is provided, including: obtaining image data of a cavity wall of an organ; unfolding the cavity wall; and generating an image of the unfolded cavity wall. The unfolding of the cavity wall may include: obtaining a mask and a centerline of the organ; obtaining a connected region of the mask; dividing the connected region into at least one equidistant block; determining an orientation of the equidistant block in a three-dimensional coordinate system including a first direction, a second direction and a third direction; determining an initial normal vector and an initial tangent vector of a center point of the centerline; assigning a projection of the initial normal vector to a normal vector of a light direction of the center point; assigning the third direction or an reverse direction of the third direction to a tangent vector of the light direction of the center point.

A platform is provided for generating 3D models of a tumor segmented from a series of 2D medical images and for identifying from these 3D models, radiomic features that may be used for diagnostic, prognostic, and treatment response assessment of the tumor. The radiomic features may be shape-based features, intensity-based features, textural features, and filter-based features. The radiomic features are compared to remove sufficiently redundant features, thereby producing a reduced set of radiomic features, which is then compared to separate genomic data and/or outcome data to identify clinically and biologically significant radiomic features for diagnostic, prognostic, and treatment response assessment, other applications.

A diagnostic assistance method for a disease based on an endoscopic image of a digestive organ with use of a convolutional neural network (CNN) trains the CNN using a first endoscopic image of the digestive organ and at least one final diagnosis result of the positivity or the negativity for the disease in the digestive organ, or information corresponding to a severity level, the final diagnosis result being corresponding to the first endoscopic image, and the trained CNN outputs at least one of a probability of the positivity and/or the negativity for the disease in the digestive organ, a severity level of the disease, or a probability corresponding to the invasion depth (infiltration depth) of the disease, based on a second endoscopic image of the digestive organ.

There is provided a medical support system including: a derivation device that derives an assessment value for an affected site based on an affected site image obtained by imaging the affected site; and a display device that presents the assessment value to a user, in which the derivation device includes: a cut-out unit that cuts out the affected site image as a plurality of tile images having tile shapes; and an assessment derivation unit that derives a tile assessment value representing an assessment of the affected site in the plurality of the tile images by using a determiner obtained by machine learning.

Disclosed herein are computer-implemented method, system, and computer-program product (computer-readable storage medium) embodiments of image scoring for intestinal pathology. An embodiment includes receiving, via at least one processor, an output of an imaging device. The output of the imaging device may include a plurality of image frames forming at least a subset of a set of image frames depicting an inside surface of a digestive organ of a given patient; and decomposing, via at least one machine learning (ML) algorithm, at least one image frame of the plurality of image frames into a plurality of regions of interest. The at least one region of interest may be defined by determining that an edge value exceeds a predetermined threshold. At least one processor may automatically assign a first score based at least in part on the edge value for each region of interest and automatically shuffle the set of image frames.