Systems and methods for polyp size estimation
AssigneeGiven Imaging Ltd.
InventorsEyal Dekel, Dov Eilot, Dori Peleg
Life of this patent
Estimating the term…
Abstract
A computer-implemented method for estimating a size of a suspected polyp in an image includes accessing an image of in at least a portion of a gastrointestinal tract (GIT) captured by a capsule endoscopy device, wherein the image includes a suspected polyp; receiving an indication to an approximated periphery of the suspected polyp in the image; determining, without human intervention, a 3D measurement of the polyp based on peripheral points in the approximated periphery indication; and estimating a size of the suspected polyp based on the determined 3D measurement.
Claims9
A computer-implemented method for estimating a size of a polyp without human intervention using semantic segmentation by deep learning convolutional neural networks for polyp detection and localization followed by region property analysis to determine extreme peripheral points of the estimated polyp followed by size estimation, the computer-implemented method comprising:
- accessing a number k of light images of at least a portion of a gastrointestinal tract (GIT) captured during a capsule endoscopy procedure using light, wherein k>1, wherein each light image of the unstructured light images is associated with a respective estimated size of a suspected polyp determined based on the respective light image, the estimated sizes of the suspected polyp comprising a first size value size1 and a second size value size2, wherein the light images are determined to include the same suspected polyp;
- determining a weight of each light image of the light images indicative of a probability of the respective light image including a polyp, the weights comprising a first weight value weight1 and a second weight value weight2;
- and
- estimating a weighted polyp size for the suspected polyp based on the determined weights of the light images and based on the estimated sizes of the suspected polyp according to the light images,
- wherein the estimating the weighted polyp size comprises computing:
- (weight1·size1+weight2·size2).
An apparatus for estimating a size of a polyp without human intervention using semantic segmentation by deep learning convolutional neural networks for polyp detection and localization followed by region property analysis to determine extreme peripheral points of the estimated polyp followed by size estimation, the apparatus comprising:
- at least one processor;
- and
- at least one memory having stored thereon instructions which, when executed by the at least one processor, cause the apparatus at least to perform:
- accessing a number k of light images of at least a portion of a gastrointestinal tract (GIT) captured during a capsule endoscopy procedure using light, wherein k>1, wherein each light image of the light images is associated with a respective estimated size of a suspected polyp determined based on the respective light image, the estimated sizes of the suspected polyp comprising a first size value size1 and a second size value size2, wherein the light images are determined to include the same suspected polyp;
- determining a weight of each light image of the light images indicative of a probability of the respective light image including a polyp, the weights comprising a first weight value weight1 and a second weight value weight2;
- and
- estimating a weighted polyp size for the suspected polyp based on the determined weights of the light images and based on the estimated sizes of the suspected polyp according to the light images,
- wherein the estimating the weighted polyp size comprises computing:
- (weight1·size1+weight2·size2).
A computer-readable medium having stored thereon instructions for estimating a size of a polyp without human intervention using semantic segmentation by deep learning convolutional neural networks for polyp detection and localization followed by region property analysis to determine extreme peripheral points of the estimated polyp followed by size estimation, the instructions, when executed by at least one processor of an apparatus, cause the apparatus at least to perform:
- accessing a number k of light images of at least a portion of a gastrointestinal tract (GIT) captured during a capsule endoscopy procedure using light, wherein k>1, wherein each light image of the light images is associated with a respective estimated size of a suspected polyp determined based on the respective light image, the estimated sizes of the suspected polyp comprising a first size value size1 and a second size value size2, wherein the light images are determined to include the same suspected polyp;
- determining a weight of each light image of the light images indicative of a probability of the respective light image including a polyp, the weights comprising a first weight value weight1 and a second weight value weight2;
- and
- estimating a weighted polyp size for the suspected polyp based on the determined weights of the light images and based on the estimated sizes of the suspected polyp according to the light images,
- wherein the estimating the weighted polyp size comprises computing:
- (weight1·size1+weight2·size2).
Dependencies are inferred from the claim text.