The purpose is to provide an ultrasound imaging device capable of automatically detecting a boundary of a biological tissue in an ultrasound image. An ultrasound imaging device includes an image generation module which receives ultrasound waves transmitted from a surface of an analyte toward an inside of the analyte and reflected therein to generate an ultrasound image inside the analyte, a reference point setting module which sets a reference point of a tissue of interest of the ultrasound image, a first seed point imparting module which imparts one or more seed points to the ultrasound image with reference point, and a region demarcating module which demarcates a region to which the seed point belongs and divides an image region of the analyte included in the ultrasound image into a plurality of regions according to a type of tissue.

The present disclosure is related to systems and methods for image processing. The method may include obtaining an image including at least one of a first type of artifact or a second type of artifact. The method may include determining, based on a trained machine learning model, at least one of first information associated with the first type of artifact or second information associated with the second type of artifact in the image. The trained machine learning model may include a first trained model and a second trained model. The first trained model may be configured to determine the first information. The second trained model may be configured to determine the second information. The method may include generating a target image based on at least part of the first information and the second information.

An endoscopic system includes a processor. The processor generates from a measured value of pressure in a lumen of a subject a first temporal change image of the pressure in the lumen, generates an observation image for observing an opened/closed state of a valve portion in the lumen of the subject in real time from an image pickup signal obtained by picking up an image of the opened/closed state of the valve portion in the lumen in real time using an endoscope, and generates a superposition image by superposing the first temporal change image and the observation image in a temporally synchronized manner.

In a case where a first specific scene recognized at a time of insertion of an endoscope is stored in a specific scene memory and a recognized scene recognized by a scene recognition unit is a scene on a side deeper than the first specific scene in a direction of movement of the endoscope, the recognized scene is output without being changed. In a case where the first specific scene is stored in the specific scene memory and the recognized scene is a scene on a side shallower than the first specific scene in the direction of movement of the endoscope, the recognized scene is changed and output.

The present disclosure describes a method, an apparatus, and storage medium for recognizing medical image. The method includes obtaining, by a device, a medical image. The device includes a memory storing instructions and a processor in communication with the memory. The method further includes determining, by the device, the medical image through a first recognition model to generate a lesion recognition result used for indicating whether the medical image comprises a lesion; and in response to the lesion recognition result indicating that the medical image comprises a lesion, recognizing, by the device, the medical image through a second recognition model to generate a lesion degree recognition result of the medical image used for indicating a degree of the lesion. Manual analysis and customization of a feature extraction solution are not required, so that the efficiency and accuracy of medical image recognition are improved.

A computerized-method for a mucosal assessment of a mucosal disease in a Gastrointestinal Tract (GIT) of a subject, including receiving a stream of images of at least a portion of the GIT, parsing the stream into a plurality of segments, wherein each segment corresponds to a region of the at least portion of the GIT, obtaining a set of values for each segment, wherein the set of values refers to the pathological involvement of the segment in the mucosal disease and to severity of mucosal manifestation of the mucosal disease in the segment, and based on said set of values for each segment, generating a representation indicating the location and severity of the mucosal manifestation of the mucosal disease in the entirety of the at least portion of the subject's GIT, thereby allowing to assess the condition of the mucosal disease in the at least portion of the GIT.

Systems and methods are provided for delivering consistent high quality, cost efficient results in fixed or mobile endoscopy facilities, without requiring the continuous real-time involvement of a fellowship trained gastroenterologist, by integrating patient specific information into decision support systems and AI/machine learning systems employed during the planning and examination phases of the endoscopy procedure.

An apparatus includes a sensor configured to capture an image of an affected part, and a processor configured to obtain information about a size of the affected part in the captured image, and control timing to capture an image of the affected part or control timing to prompt a user to perform an imaging operation based on the information about the size of the affected part.

There are provided a medical image processing device, and endoscope system, a diagnosis support method, and a program which can support diagnosis by avoiding an inappropriate report in a case where any of site information of an observation target and lesion type information detected from a medical image is incorrect. The medical image processing device includes at least one processor. The at least one processor acquires the medical image, acquires the site information indicating a site of an observation target included in the medical image, in a human body, detects a lesion from the medical image to acquire the lesion type information indicating a lesion type, determines presence or absence of a contradiction between the site information and the lesion type information, and decides a report mode of the site information and the lesion type information on the basis of a determination result.

A system includes a processor, a memory configured to store a digital model of the colon and previous anomalies in the digital model based on previous colonoscopy data and to include instructions stored therein. The instructions, when executed by the processor, cause the system to access a frame image captured by a colonoscope, process the frame image to detect current anomalies and debris, display the frame image on a display, display a first shape at positions of the previous anomalies in the frame image based on correlation between the frame image and the digital model, and display a second shape at positions of the current anomalies in the frame image.