BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates in general to computing systems, and more particularly, to various embodiments for intelligent boundary delineation and classification of a region of interest of an organism in two spatial dimensions using models of perfusion from time series data captured from multispectral video streams using a computing processor.

Description of the Related Art

In today's society, consumers, business persons, health care professionals, and others use various computing systems with increasing frequency in a variety of settings. The prevalence of health problems presents a challenge for computing systems to detect and assist in proper diagnosis of various types of diseases. Current methods of computer assisted diagnosis of a patient's condition involve a combination of different types of analyses performed on clinical, molecular (genomic, proteomic, metabolic, etc.) and environmental data. For some complex cases, timely detection and proper diagnosis of a disease is critical and imperative to managing, containing, preventing, or even eradicating the disease.

SUMMARY OF THE INVENTION

Various embodiments for implementing intelligent boundary delineation and classification of a region of interest of an organism in two spatial dimensions using models of perfusion from time series data captured from multispectral video streams by a processor, are provided. In one embodiment, by way of example only, a method for implementing intelligent delineation and classification of regions of interest in an organism (e.g., tissue in a patient), again by a processor, is provided. Time series data of a contrast agent (e.g., a fluorescent dye) in one or more regions of interest captured from multispectral image streams may be collected. One or more regions of interest having one or more perfusion patterns may be identified from the time series data. Boundaries of the one or more regions of interest may be delineated into at least two spatial dimensions, wherein the boundaries of the one or more regions of interest include one or more selected labels. One or more regions of interest having one or more perfusion patterns from the time series data may be identified. The one or more perfusion patterns of the one or more regions of interest may be classified into one of a plurality of classes by applying one or more perfusion models representing spatio-temporal behavior of the contrast agent reflected by the time series data and by using a machine learning operation.

