System for diagnosing Barrett's esophagus and related methods
AssigneeErasmai LLC
InventorsDaniel L. Franklin, Domenico Coppola, Anthony M. Magliocco
Life of this patent
Estimating the term…
Abstract
A system is for diagnosing BE in a patient. The system includes a processor and memory, and the processor is configured to receive a WSI from an esophagus sample from the patient. The processor is configured to segment the WSI into image segments, extract features from each image segment using a CNN, and assign each image segment an attention score based upon the features. The processor is also configured to filter the image segments based upon the attention score of each image segment, and generate a BE metric value for the patient based upon the filtered image segments. The BE metric value indicates a probability that tissue with a propensity to progress to BE exists within the WSI.
Claims20
A system for diagnosing Barrett's esophagus (BE) in a patient, the system comprising:
- a processor and memory coupled thereto, the processor configured to
- receive a whole slide image (WSI) from an esophagus sample from the patient,
- segment the WSI into a plurality of image segments,
- extract a plurality of features from each image segment using a convolutional neural network (CNN),
- assign each image segment an attention score based upon the plurality of features,
- filter the plurality of image segments based upon the attention score of each image segment, the filtering comprising
- clustering the plurality of image segments based upon respective attention scores and upper and lower thresholds, and
- removing a portion of the image segments with attention scores between the upper and lower thresholds,
- generate at least one BE metric value for the patient based upon the filtered image segments, the at least one BE metric value indicating a probability that tissue with a propensity to progress to BE exists within the WSI.
A system for diagnosing Barrett's esophagus (BE) in a patient, the system comprising:
- a processor and memory coupled thereto, the processor configured to
- receive a whole slide image (WSI) from an esophagus sample from the patient,
- segment the WSI into a plurality of image segments,
- directly ingest the plurality of image segments into a convolutional neural network (CNN),
- extract a plurality of features from each image segment using the CNN,
- assign each image segment an attention score based upon the plurality of features,
- filter the plurality of image segments based upon the attention score of each image segment, the filtering comprising
- ingesting the plurality of features for each image segment into a cluster network,
- clustering the plurality of image segments with attention scores based upon respective attention scores and upper and lower thresholds, and
- removing a portion of the image segments with attention scores between the first and second thresholds, and
- generate at least one BE metric value for the patient based upon the filtered image segments, the at least one BE metric value indicating a probability that tissue with a propensity to progress to BE exists within the WSI.
A method for diagnosing Barrett's esophagus (BE) in a patient, the method comprising:
- receiving a whole slide image (WSI) from an esophagus sample from the patient;
- segmenting the WSI into a plurality of image segments;
- extracting a plurality of features from each image segment using a convolutional neural network (CNN);
- assigning each image segment an attention score based upon the plurality of features;
- filtering the plurality of image segments based upon the attention score of each image segment, the filtering comprising
- clustering the plurality of image segments based upon respective attention scores and upper and lower thresholds, and
- removing a portion of the image segments with attention scores between the upper and lower thresholds;
- and
- generating at least one BE metric value for the patient based upon the filtered image segments, the at least one BE metric value indicating a probability that tissue with a propensity to progress to BE exists within the WSI.
Dependencies are inferred from the claim text.