H. Lee Moffitt Cancer Center and Research Institute, Inc.

University of South Florida

Florida Analytical Imaging Solutions, Llc.

Dansheng Song

Tatyana A Zhukov

Melvyn S Tockman

Wei Qian

Victoria Link

A method and system for selecting a region of interest (ROI) in a tissue or groups of cells, in which one or more of the steps can be automated. In various exemplary embodiments, the method and system can be fully-automated. In various exemplary embodiments, the method and system can be applied to centrosome analysis with excellent accuracy and precision. Centrosome analysis is an important tool in very early cancer diagnosis and cancer prognosis.

Automatic region of interest selection in centrosome analysis

The subject invention concerns methods for the detection, diagnosis, and/or prognosis of cancer by analyzing centrosomal features. In one embodiment, a method includes receiving an image of one or more cells; selecting a region of interest in one cell; segmenting the region of interest to delineate at least one centrosomal; extracting one or more features from the segmented image; and analyzing the extracted features to diagnose cancer. In another embodiment, the progression of cancer can be predicted through analysis and classification of the extracted features. In one embodiment, the method can be performed by a quantitative cancer analysis system including a diagnosis module and/or a prognosis module. In one embodiment, the method can be performed using an image processing system.

Methods and apparatus for diagnosis and/or prognosis of cancer

The present invention is a computer-aided pathological diagnosis method for the classification of cancer cells in a tissue specimen based on a digital cellular image of the tissue specimen. The method of the present invention includes the steps of, extracting the histological characteristic features of the cellular image using preprocessing algorithms having adaptive strategies to enhance the cellular image, declustering the extracted histological characteristic features of the cellular image to isolate the individual cells and the nuclei inside the cells, segmenting the declustered cellular image, labeling the segmented cellular image and classifying the cells in the labeled cellular image as cancer cells or non-cancer cells.

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Tampa, FL, United States of America

Dansheng Song