Abstract:
Methods for capturing and transmitting images by an in-vivo device comprise operating a pixel array in a superpixel readout mode to capture probe image, for example, according to a time interval. Concurrently to capturing of each probe image, the probe image is evaluated alone or in conjunction with other probe image(s), and if it is determined that no event of interest is detected by the last probe image, or by the last few probe images, the pixel array is operated in the superpixel readout mode and a subsequent probe image is captured. However, if it is determined that the last probe image, or the last few probe images, detected an event of interest, the pixel array is operated in a single pixel readout mode and a single normal image, or a series of normal image, is captured and transmitted, for example, to an external receiver.
Abstract:
Ulcer detection may include calculating ulcer head scores for image pixels, and calculating ulcer red region scores for pixels, each score correlated to the probability that the pixel color is typical to ulcer heads, ulcer red regions, or a other regions. Ulcer head scores may be compared to an ulcer head threshold level and, based on the results, ulcer head candidates may be obtained. Ulcer red region scores may be compared to a threshold and ulcer red region candidates may be obtained. Ulcer candidates may be formed by creating pairs including an ulcer head candidate and a potential ulcer red region candidate. A grade for ulcer candidates indicating the probability that the ulcer candidate is an ulcer may be used to generate a score for the image, the score indicating the probability that the image includes an ulcer.
Abstract:
Systems and methods may display and/or provide analysis of a number of selected images of a patient's gastrointestinal tract collected in-vivo by a swallowable capsule. Images may be displayed for review (e.g., as a study) and/or for further analysis by a user. A subset of images representing the stream of images and automatically selected according to a first selection method may be displayed. On user input, additional images corresponding to a currently displayed image may be displayed, where the additional images are automatically selected according to a second selection method. The second selection method may be based on a relation between images of the stream of in-vivo images and the currently displayed image.
Abstract:
An optimization unit controls electrical currents of a set of electromagnets to generate a wanted maneuvering magnetic field pattern (MMP) for moving an in-vivo device in the GI system. The optimization unit may calculate a magnetic force and a magnetic field to maneuver the in-vivo device from a current location and/or orientation to a new location and/or orientation. The optimization unit may solve a magnetic force optimization problem with respect to the magnetic force in order to determine electrical currents suitable for generating the wanted MMP. The optimization unit may additionally or alternatively solve a minimum electrical power optimization problem with respect to the electrical power to be consumed by the electromagnets in order to recalculate or adjust the electrical currents. The optimization unit may solve one or more of the optimization problems while complying with a set of constraints associated with or derived from each type of optimization objective.
Abstract:
An in-vivo imaging system and method to automatically detect a pathology frame sequence in an image stream captured in vivo. An image stream comprising a plurality of image frames captured in vivo may be received, and a pathology score for at least a portion of the image frames is received. A seed frame which includes at least one pathology candidate may be selected, and the position of the pathology candidate in the seed frame may be determined. A sequence of frames adjacent to the seed frame is defined comprising frames that depict the pathology candidate, and a pathology sequence score is calculated based on the sequence, the pathology sequence score correlating to the probability that the sequence of frames adjacent to the seed frame includes a pathology. If the pathology sequence score is within a range, a display method may be adapted, or the pathology score may be changed based.
Abstract:
A system and method for detecting a transition in a stream of images of a gastrointestinal (GI) tract may include selecting images from an in-vivo image stream; calculating a segment score for each selected image indicating in which segment of the GI tract the image was captured; applying a smoothing function on the scores; detecting a global step in the smoothed segment score signal indicating a substantial change in a parameter calculated based on segment score signal values of the segment score signal values; detecting a local step indicating a substantial change in a parameter calculated based on segment score signal values of a predetermined interval of the of the segment score signal values; combining the local step and the global step; and determining a point of transition in the stream from one anatomical segment to another, the point of transition correlating to the combined step.
Abstract:
A system and method for classification of images of an image stream may include receiving an image stream of unclassified images, for example produced by an in-vivo imaging device, and based on indirect user input, adapting an initial classification algorithm to classify images to groups based on at least a subset of the received image stream of unclassified images. The indirect user input may be used to generate user-based indications for the classification.
Abstract:
A system for diagnosing an esophageal disease includes at least one processor and at least one memory storing instructions. The instructions, when executed by the at least one processor, cause the system to: access, during a procedure involving an in-vivo device located within a person, data measured by the in-vivo device relating to an esophageal disease; evaluate, during the procedure while the in-vivo device is located within the person, a diagnosis for the esophageal disease for the person by applying a trained machine learning model to the data measured by the in-vivo device; and communicate, during the procedure while the in-vivo device is located within the person, the diagnosis for the esophageal disease.
Abstract:
Systems and methods are disclosed for identifying images that contain polyps. An exemplary method for identifying images includes: accessing images of a gastrointestinal tract (GIT) captured by a capsule endoscopy device, where: each image of the images is suspected to include a polyp and is associated with a probability of containing the polyp, and the images include seed images, where each seed image is associated with one or more images of the images. The image(s) associated with each seed image is identified as suspected to include the same polyp as the associated seed image. The method includes applying a polyp detection system on the seed images to identify seed images which include polyps, where the polyp detection system is applied to each seed image of based on the image(s) associated with the seed image and the probabilities associated with the seed image and with the associated image(s).
Abstract:
A method executed by a system for selecting images from a plurality of image groups originating from a plurality of imagers of an in-vivo device includes calculating, or otherwise associating, a general score (GS) for images of each image group, to indicate the probability that each image includes at least one pathology, dividing each image group into image subgroups, identifying a set Set(i) of maximum general scores (MGSs), a MGS for each image subgroup of each image group; and selecting images for processing by identifying a MGS|max in each set S(i) of MGSs; identifying the greatest MGS|max and selecting the image related to the greatest MGS|max. The method further includes modifying the set Set(i) of MGSs related to the selected image, and repeating the steps described above until a predetermined criterion selected from a group consisting of a number N of images and a score threshold is met.