Intelligent classification of regions of interest of an organism from multispectral video streams using perfusion models
AssigneeInternational Business Machines Corporation
InventorsJonathan Epperlein, Sergiy Zhuk, Rahul Nair, Pol MacAonghusa
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Abstract
Embodiments for implementing intelligent classification of region of interest in an organism in a computing environment by a processor. Time series data of a contrast agent in one or more regions of interest captured from multispectral image streams may be collected. 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.
Claims20
A method for implementing intelligent classification of regions of interest of an organism by a processor, comprising:
- collecting time series data of a contrast agent, injected into a patient and perfusing through one or more regions of interest, from multispectral image streams;
- classifying the one or more regions of interest into one of a plurality of classes by using a machine learning operation to apply one or more perfusion models representing spatio-temporal behavior of the contrast agent reflected by the time series data, wherein the classification is performed by the machine learning operation in real-time and eliminates a waiting period, from injection to a stationary phase in which the contrast agent is persisted or flushed from the one or more regions of interest, otherwise required to perform a medical diagnosis.
A system for implementing intelligent classification of region of interest of an organism, comprising:
- one or more computers with executable instructions that when executed cause the system to:
- collect time series data of a contrast agent, injected into a patient and perfusing through one or more regions of interest, from multispectral image streams;
- classify the one or more regions of interest into one of a plurality of classes by using a machine learning operation to apply one or more perfusion models representing spatio-temporal behavior of the contrast agent reflected by the time series data wherein the classification is performed by the machine learning operation in real-time and eliminates a waiting period, from injection to a stationary phase in which the contrast agent is persisted or flushed from the one or more regions of interest, otherwise required to perform a medical diagnosis.
A computer program product for implementing intelligent classification of region of interest of an organism by a processor, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:
- an executable portion that collects time series data of a contrast agent, injected into a patient and perfusing through one or more regions of interest, from multispectral image streams;
- an executable portion that classifies the one or more regions of interest into one of a plurality of classes by using a machine learning operation to apply one or more perfusion models representing spatio-temporal behavior of the contrast agent reflected by the time series data, wherein the classification is performed by the machine learning operation in real-time and eliminates a waiting period, from injection to a stationary phase in which the contrast agent is persisted or flushed from the one or more regions of interest, otherwise required to perform a medical diagnosis.
Dependencies are inferred from the claim text.