The invention concerns automated methods for assessing tissue morphometry in digital images of tissue sections derived from small intestine biopsy samples from patients submitted for evaluation of celiac disease. The methods generally involve digital image analysis of tissue section images, and specifically involve post-processing each image to produce a binary mask capturing the tissue area footprint on the glass slide. Virtual stereology probes are placed on each image and assessed to estimate the ratio of the surface area to volume of the tissue specimen. The surface area to volume ratio is used to diagnose celiac disease and make inferences about the severity of celiac disease in those individuals with a positive diagnosis of celiac disease.

Methods and systems for generating a visualization of a surface of an internal body cavity, such as an internal organ like the bladder, are provided. The approach generally includes inserting an endoscope into an internal body cavity, acquiring a video of the tissue surfaces defining the internal body cavity, stitching video frames together to generate a panoramic map of the tissue surfaces defining the internal body cavity, and displaying the panoramic map.

A method for dynamic contrast enhanced (DCE) image processing and kinetic modeling of an organ's region-of-interest is provided. The method includes deriving at least a contour of an exterior of the organ's region-of-interest from one or more of a plurality of images; generating a spline function in response to the derived contour of the exterior of the organ's region-of-interest from the one or more of the plurality of images; registering the plurality of images wherein the organ's region-of-interest has been segmented; deriving a tracer curve for the organ's region-of-interest in the registered images, the tracer curve indicating a change in concentration of a contrast agent flowing through the organ's region-of-interest over a time period; and kinetic modeling by fitting a kinetic model to the tracer curve to generate one or more maps of tissue physiological parameters associated with the kinetic model.

Embodiments discussed herein facilitate predicting response to therapy in Crohn's disease. A first set of embodiments discussed herein relates to accessing a radiological image of a region of tissue demonstrating Crohn's disease associated with a patient; defining a mesenteric fat region by segmenting mesenteric fat represented in the radiological image; extracting a set of radiomic features from the mesenteric fat region; providing the set of radiomic features to a machine learning classifier configured to compute a probability of response to therapy in Crohn's disease based, at least in part, on the set of radiomic features; receiving, from the machine learning classifier, a probability that the region of tissue will respond to therapy; generating a classification of the patient as a responder or non-responder based, at least in part, on the probability; and displaying the classification.

A landmark estimating method estimates a position of a landmark that is a hole existing in an object and is a site through which an insertion portion penetrates, in an endoscope image obtained by picking up an image of the object by an endoscope with the insertion portion bent. The landmark estimating method includes estimating an axis of the insertion portion, estimating a boundary of the insertion portion and the object, and estimating the position of the landmark based on the axis and the boundary that are estimated.

The present invention relates to a system for diagnosing small bowel cleanliness. The system may comprise: a similarity analysis unit for analyzing to select a representative image of similar small bowel images from among a plurality of small bowel images; an image classification unit for, when a series of a plurality of small bowel images in which cleanliness is to be diagnosed are received in a state where the plurality of small bowel images have been learned, classifying small bowel cleanliness according to scores by predicting the small bowel cleanliness by applying the representative image to a learning result; and a cleanliness diagnosis unit for calculating final small bowel cleanliness for the series of the plurality of small bowel images on the basis of a score for small bowel cleanliness of the representative image and the number of small bowel images similar to the representative image.

This disclosure relates to representing an interior of a volume, such as but not limited to, a lumen. Examples of lumens may comprise a colon or bronchus. An input port receives the captured image data of the interior of the volume. The processor selects one of the multiple candidates such that the selected one of the multiple candidates corresponds to the captured image data. Each candidate is associated with simulated image data of the interior of the volume. The processor stores an association of the selected candidate with the captured image data to represent the interior of the volume. Aspects of the disclosure include computer implemented methods, computer systems and software.

Obtaining a surface image captured by an endoscope inserted in a tubular organ associated with a surrounding blood vessel and representing an inner surface of a wall of the organ, generating, from a three-dimensional image representing a three-dimensional area including the organ, an adjacent blood vessel image depicting a portion of the blood vessel adjacent to the wall from a viewpoint in the three-dimensional image corresponding the viewpoint of the surface image, generating, from a three-dimensional image representing a three-dimensional area including a surrounding area of the organ, a surrounding blood vessel image depicting the blood vessel from a viewpoint in the three-dimensional image corresponding the viewpoint of the surface image, and causing the surface image, adjacent blood vessel image, and surrounding blood vessel image to be displayed in this order on a display unit.

In an approach for classifying regions of tissue captured in multispectral videos into medically meaningful classes using GPU accelerated perfusion estimation, a processor receives one or more multispectral videos of a subject tissue of a patient. A processor extracts one or more fluorescence time series profiles from the one or more multispectral videos. A processor estimates one or more sets of perfusion parameters based on the one or more fluorescence time series profiles. A processor inputs one or more feature vectors into a classifier, wherein the one or more feature vectors are derived the one or more sets of perfusion parameters. A processor receives a classification result for each of the one or more feature vectors, wherein the classification result comprises a set of medically relevant labels for each of the one or more feature vectors with a level of certainty for each label of the set of medically relevant labels.

A system and method for capturing in-vivo images of an endoluminal network and generating a pathway for directing an endoluminal robot to drive a catheter to a desired location.