Abstract: Systems and methods are directed to computer-assisted navigation of images of a cytological specimen, including the acts of analyzing a first image of the cytological specimen to identify a plurality of objects of interest within the cytological specimen, displaying a plurality of images each comprising one of the plurality of identified objects of interest within the cytological specimen and including a second image of at least one object of interest, and displaying, responsive to receiving a user selection of the second image of the at least one object of interest, a field of view of the at least one object of interest and neighboring objects of interest, wherein the second image of the at least one object of interest has a first magnification, and wherein the field of view of the at least one object of interest is displayed at a second magnification different than the first magnification.

Abstract: Systems and methods of use to facilitate classification of cytological specimens are discussed. The system acquires or imports image data of a cytological specimen. The imported image data may include, or the system may otherwise perform an image analysis to identify one or more objects of interest in a respective specimen image dataset, including feature attributes for the identified objects. The system analyzes the feature attributes by predetermined criteria and/or optionally with user inputted criteria. The system includes an analysis tool that assists the user in identifying cytologically abnormal objects, if present in a particular specimen, by manipulating and viewing images of objects selected as a function of feature attributes. More generally, the analysis tool aides the user to find, extract, and display abnormal objects from within a large dataset of images and facilitates navigation through large amounts of image data and enables the efficient classification of the entire specimen.

Abstract: Systems and methods of use to facilitate classification of cytological specimens are discussed. The system acquires or imports image data of a cytological specimen. The imported image data may include, or the system may otherwise perform an image analysis to identify one or more objects of interest in a respective specimen image dataset, including feature attributes for the identified objects. The system analyzes the feature attributes by predetermined criteria and/or optionally with user inputted criteria. The system includes an analysis tool that assists the user in identifying cytologically abnormal objects, if present in a particular specimen, by manipulating and viewing images of objects selected as a function of feature attributes. More generally, the analysis tool aides the user to find, extract, and display abnormal objects from within a large dataset of images and facilitates navigation through large amounts of image data and enables the efficient classification of the entire specimen.