An image processing apparatus includes a corresponding region connecting unit that connects regions that depict the same target between a series of images captured in time series, thereby sets at least one connected region; a connected region feature data calculation unit that calculates feature data of the connected region; a digest index value calculation unit that calculates a digest index value corresponding to a degree at which the target depicted in the series of images is aggregated in each image of the series of images, based on the feature data; and a digest image detector that detects a digest image based on the digest index value.

A method and system for registering pre-operative images and intra-operative images using biomechanical simulations is disclosed. A pre-operative image is initially registered to an intra-operative image by estimating deformations of one or more segmented anatomical structures in the pre-operative image, such as the liver, surrounding tissue, and the abdominal wall, using biomechanical gas insufflation model constrained. The initially registered pre-operative image is then refined using diffeomorphic non-rigid refinement.

An image processing device includes a processor, and the processor decides whether or not to perform image processing on the medical image on the basis of an imaging condition of the medical image and/or an image analysis result obtained by analyzing the medical image. The processor performs, for the medical image on which the processor has decided to perform the image processing, at least one of calculation of an index value related to a stage of ulcerative colitis, determination of the stage of the ulcerative colitis, or determination of remission or non-remission of the ulcerative colitis on the basis of denseness of superficial blood vessels, intramucosal hemorrhage, and extramucosal hemorrhage that are obtained from the medical image.

This application provides an endoscopic image processing method and apparatus, an electronic device, and a storage medium. The method includes: obtaining an endoscopic video stream, the endoscopic video stream including an original endoscopic image; detecting the original endoscopic image in a corresponding video frame through a first thread, and transmitting a detection result of the original endoscopic image to an integration module; forming a control instruction through the integration module according to the detection result; and adjusting, in response to the control instruction and through a second thread, an output result in the second thread, the output result corresponding to a use environment of the endoscopic video stream, the first thread and the second thread being parallel threads.

To provide an information processing device and the like for presenting a determination reason together with a determination result regarding a disease. The information processing device includes: an image acquisition unit that acquires an endoscope image; a first acquisition unit that inputs the endoscope image acquired by the image acquisition unit to a first model that outputs diagnosis criteria prediction regarding diagnostic criteria of disease when the endoscope image is input, and acquires the output diagnosis criteria prediction; and an output unit that outputs the diagnosis criteria prediction acquired by the first acquisition unit in association with the diagnosis prediction regarding a state of the disease acquired based on the endoscope image.

A control apparatus includes a processor. The processor detects an image pickup scene based on a picked-up image acquired by an image pickup apparatus disposed in an insertion section included in an endoscope and calculates operation information corresponding to the image pickup scene using an approach of machine learning.

Methods and systems are provided for aggregating features in multiple video frames to enhance tissue abnormality detection algorithms, wherein a first detection algorithm identifies an abnormality and aggregates adjacent video frames to create a more complete image for analysis by an artificial intelligence detection algorithm, the aggregation occurring in real time as the medical procedure is being performed.

An endoscopy video feature enhancement platform (EVFEP) is connected to the output of any type endoscope system, and inputs and captures the output video. The video is visually augmented live with indicators of possible polyp detection and localization, polyp attributes, and procedure metrics, based on the collective learning of the output results of many different types of endoscopy systems on a large scale. An artificial intelligence model is trained on confirmed polyp detection previously determined by this and other EVFEP devices used with many different types of endoscope systems on a large scale. Augmented video, images and automatically generated short video clips of key procedure segments are passed to a reporting system, and supplemented with meta data.

The present invention relates to a method for defining a colour range specific for a chosen category of samples, for use when identifying presence of cells of different categories in a tissue or cell sample. Further, methods for using the obtained colour range in selection of sample images comprising cells of a first category is also disclosed.

Systems and methods for improving endoscopy procedures are described that provide not only a conventional real time image of the view obtained by an endoscope, but in addition, a near real time 3D model and/or a 2D flattened image of an interior surface of an organ, which model and image may be processed using AI software to highlight potential tissue abnormalities for closer examination and/or biopsy during the procedure. A navigation module interacts with other system outputs to further assist the endoscopist with navigational indicia, e.g., landmarks and/or directional arrows, that enhance the endoscopists' spatial orientation, and/or may provide navigational guidance to the endoscopist to assist manipulation of the endoscope.