A method of determining a clinical value for an individual based on a tumor in an image by an apparatus including processing circuitry may include executing, by the processing circuitry, instructions that cause the apparatus to determine a lymphocyte distribution of lymphocytes in the tumor based on the image; apply a classifier to the lymphocyte distribution to classify the tumor, the classifier having been trained to classify tumors into a class selected from at least two classes respectively associated with lymphocyte distributions; and determine the clinical value for the individual based on prognoses of individuals with tumors in the class into which the classifier classified the tumor.

A system for automatically analyzing a video recording of a colonoscopy includes a processor and memory storing instructions, which when executed by the processor, cause the processor to receive the video recording of the colonoscopy performed on the colon and detect informative frames in the video recording. A frame is informative if the clarity of the frame is above a threshold or if the frame includes clinically relevant information about the colon. The instructions cause the processor to generate scores indicating severity levels of a disease for a plurality of the informative frames, estimate locations of the plurality of the informative frames in the colon, and generate an output indicating a distribution of the scores over one or more segments of the colon by combining the scores generated for the plurality of the informative frames and the estimated locations of the plurality of the informative frames in the colon.

A method of detecting a protrusion in an intestinal tract in a computer according to an embodiment of the present disclosure includes acquiring a three-dimensional model of the intestinal tract scanned by a scanning device, the three-dimensional model comprising three-dimensional data of the intestinal tract; mapping, in the computer, the three-dimensional model to a two-dimensional plane in an area-preserving manner; and detecting an area of the protrusion in the two-dimensional plane. The method can replace traditional modes such as enteroscopy, and the protrusion in the intestinal tract is detected in a painless and low-cost mode.

Embodiments of the invention are related to a system and method of controlling a display of image stream. The system may include a memory to store the image stream; the image stream may comprise a plurality of image frames. The system may further include a processor configured to execute the method. The moving image stream may be displayed to a user in an image stream display area of a screen and a frame rate control interface may be generated on a speed control area of the screen, such that the image stream display area is horizontally adjacent to the speed control area. An indication of a desired frame rate for displaying the image stream of the image frames may be received from the user, in that the frame rate may be selected according to a location of a pointing indicator in the speed control area.

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.

Described herein are means for generating pre-trained models for nnU-Net through the use of improved transfer learning techniques, in which the pre-trained models are then utilized for the processing of medical imaging. According to a particular embodiment, there is a system specially configured for segmenting medical images, in which such a system includes: a memory to store instructions; a processor to execute the instructions stored in the memory; wherein the system is specially configured to: execute instructions via the processor for executing a pre-trained model from Models Genesis within a nnU-Net framework; execute instructions via the processor for learning generic anatomical patterns within the executing Models Genesis through self-supervised learning; execute instructions via the processor for transforming an original image using distortion and cutout-based methods; execute instructions via the processor for learning the reconstruction of the original image from the transformed image using an encoder-decoder architecture of the nnU-Net framework to identify the generic anatomical representation from the transformed image by recovering the original image; and wherein architecture determined by the nnU-Net framework is utilized with Models Genesis and is trained to minimize the L2 distance between the prediction and ground truth. Other related embodiments are disclosed.

Some embodiments are directed to a process for quantifying changes in the intestinal mucosa caused by a chronic inflammatory intestinal disease in individuals, an ex vivo process for diagnosing a chronic inflammatory intestinal disease in individuals, and an ex vivo process for the differential diagnosis of Crohn's disease versus ulcerative colitis in individuals.

Provided are: an image diagnosis assistance apparatus capable of assisting diagnosis of an endoscopic image captured by an endoscopist; a data collection method; an image diagnosis assistance method; and an image diagnosis assistance program. The image diagnosis assistance apparatus is provided with: a lesion assessment unit that assesses, by a convolutional neural network, the denomination and the position of a lesion which is present in a digestive system endoscopic image of a patient captured by a digestive system endoscopic imaging device and information about accuracies thereof; and a display control unit that performs control for generating an analysis result image in which the denomination and the position of the lesion and the accuracies thereof are displayed and for displaying the image on the digestive system endoscopic image.

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.