Automated cytological specimen classification system and method
AssigneeAutoCyte North Carolina, L.L.C.
InventorsMark R. Rutenberg, Thomas L. Hall
Life of this patent
Estimating the term…
Abstract
An automated screening system and method for cytological specimen classification in which a neural network is utilized in performance of the classification function. Also included is an automated microscope and associated image processing circuitry.
Claims26
A semi-automated method of classifying a specimen, comprising the steps of:
- a) obtaining the specimen;
- and
- b) classifying the specimen to determine the likelihood that individual objects in the specimen have attributes justifying further evaluation, said classifying including
- i) ranking individual objects in the specimen in an order according to the likelihood that an object has attributes justifying further evaluation, and
- ii) identifying location coordinates of one or more of the objects for review or further classification by a human.
A method for providing interactive review of objects in a specimen indicative of the highest likelihood of abnormality in the specimen, comprising the steps of:
- a) obtaining the specimen;
- and
- b) classifying the specimen to determine the likelihood that individual objects in the specimen have attributes of cell abnormality justifying further evaluation, said classifying including
- i) assigning individual objects in the specimen a value according to the likelihood that an object has attributes of cell abnormality, and
- ii) selecting location coordinates of one or more of the objects to provide for viewing and further classification by a human.
A method of providing location-guided screening of a specimen for objects in the specimen having a likelihood of cell abnormality, comprising the steps of:
- a) obtaining the specimen;
- and
- b) classifying the specimen to determine the likelihood that individual objects in the specimen have attributes of cell abnormality justifying further evaluation, said classifying including
- i) ranking objects in the specimen in an order according to the likelihood that an object has attributes of cell abnormality, and
- ii) identifying locations of one or more of the objects to provide viewing and further classification by a human.
Dependencies are inferred from the claim text.