BACKGROUND

The present invention relates generally to the field of classification, and more particularly to classifying regions of tissue captured in multispectral videos into medically meaningful classes using GPU accelerated perfusion estimation.

Fluorescent dyes are used in many medical domains, in which dye is administered to a patient and transported through the body via the blood stream, i.e., perfusion. For example, indocyanine green (ICG) is a cyanine dye that is used in medical diagnostics. ICG is used for determining cardiac output, hepatic function, liver and gastric blood flow, and for ophthalmic angiography. The presence of the dye in a segment of tissue in the patient leads to fluorescence, i.e., if light at a certain wavelength is shone onto the tissue, light at a certain different wavelength is emitted from the tissue. The fluorescence offers a non-invasive way of detecting the presence or absence of dye in tissues of interest.

SUMMARY

Aspects of an embodiment of the present invention disclose a method, computer program product, and computer system for classifying regions of tissue captured in multispectral videos into medically meaningful classes using GPU accelerated perfusion estimation. A processor receives a set of inputs, wherein the set of inputs includes one or more multispectral videos of a subject tissue of a patient. A processor extracts one or more fluorescence time series profiles from the one or more multispectral videos. A processor estimates one or more sets of perfusion parameters based on the one or more fluorescence time series profiles. A processor inputs one or more feature vectors into a classifier, wherein the one or more feature vectors are derived the one or more sets of perfusion parameters. A processor receives a classification result output by the classifier for each of the one or more feature vectors, wherein the classification result comprises a set of medically relevant labels for each of the one or more feature vectors with a level of certainty for each label of the set of medically relevant labels.

In some aspects of an embodiment of the present invention, the one or more multispectral videos is a live stream of a multispectral video of the subject tissue directly from a medical imaging device.

In some aspects of an embodiment of the present invention, the set of inputs further includes patient metadata for the patient.

In some aspects of an embodiment of the present invention, a processor extracts the one or more fluorescence time series profiles in a coordinate system fixed to the patient based on the received one or more multispectral videos of the subject tissue, wherein the coordinate system has time along an x-axis and a fluorescence intensity of the subject tissue along a y-axis.

In some aspects of an embodiment of the present invention, a processor extracts a fluorescence time series profile for each region of the subject tissue.

In some aspects of an embodiment of the present invention, a processor estimates a set of values for each of the one or more sets of perfusion parameters for each fluorescence time series profile of the one or more fluorescence time series profiles.

In some aspects of an embodiment of the present invention, a processor estimates the one or more sets of perfusion parameters for advection-diffusion equation based on the one or more fluorescence time series profiles.

In some aspects of an embodiment of the present invention, a processor estimates the one or more sets of perfusion parameters for advection-diffusion equation based on the one or more fluorescence time series profiles using a scalar transport equation, ∂_t u+∂_(x_i)(A_i u)=∂_(x_i)(D∂_(x_i)u)+S, wherein a spatiotemporal fluorescence intensity u(x,t) of the one or more fluorescence time series profiles can be modelled in terms of a spatially varying advective velocity field A(x) and diffusive scalar field D(x), wherein the advective velocity field and the diffusive scalar field are perfusion parameters of the one or more sets of perfusion parameters.

In some aspects of an embodiment of the present invention, a processor solves a boundary value problem (BVP) system to produce the set of values for each of the one or more sets of perfusion parameters, wherein the BVP system is partitioned to one or more graphics processing units (GPUs) to execute portions of the BVP system.

In some aspects of an embodiment of the present invention, each of the one or more feature vectors are derived from one of the one or more sets of perfusion parameters and the patient metadata.

