Stryker European Operations Limited

Stryker Corporation

Novadaq Technologies Ulc

Lina Gurevich

Jorgen Walle-Jensen

Gregory Vincent Browne

Arthur E Bailey

Lori Ann Swalm

A method for visualizing tissue of a subject during a medical procedure includes receiving, during the medical procedure, a first imaging modality frame generated by imaging a region of the tissue of the subject according to a first imaging modality and a second imaging modality frame generated by imaging a region of tissue of the subject according to a second imaging modality; determining whether an attribute of the second imaging modality frame is below a threshold; and in accordance with determining that the attribute of the second imaging modality frame is below the threshold: generating an artificial second imaging modality frame using a machine learning model and the first imaging modality frame, and displaying, during the medical procedure, the first imaging modality frame in combination with the artificial second imaging modality frame.

Systems and methods for persistent ureter visualization

The present disclosure relates generally to medical imaging, and more specifically to enhancing medical images (e.g., images taken in low-light conditions) using machine-learning techniques. An exemplary method of obtaining an enhanced fluorescence medical image of a subject comprises: receiving a fluorescence medical image of the subject (e.g., NIR images); providing the fluorescence medical image to a generator of a trained generative adversarial network (GAN) model trained using a plurality of white light images; and obtaining, from the generator, the enhanced fluorescence medical image of the subject.

Medical imaging

Methods and systems for alignment of a subject for medical imaging are disclosed, and involve capturing an image of an anatomical region comprising a target tissue and determining, based on the image and based on a reference image, that a current alignment of the image capture device is aligned with a predefined alignment. The determination is performed before initiating capturing of a time series of fluorescence medical images from fluorescence emission. In response to the determining that the current alignment is aligned with the predefined alignment, capturing of the time series of fluorescence medical images from the fluorescence emission is initiated.

Methods and systems for alignment of a subject for medical imaging

Methods and systems for characterizing tissue of a subject are disclosed. The method includes retrieving a time series of angiography images of tissue of a subject, defining a plurality of calculation regions, generating a time-intensity curve for each respective calculation region, calculating a rank value for each respective calculation region based on one or more parameters derived from the time-intensity curve; and generating a viewable image in which on the image position of each calculation region an indication is provided of the calculated rank value for that calculation region. Also disclosed are methods and systems for generating first and second time-intensity curves for respective first and second calculation regions, calculating first and second rank values for the respective calculation regions based on first and second pluralities of parameters selected to approximate the respective time-intensity curves, and generating a spatial map of the first and second calculated rank values.

Methods and systems for characterizing tissue of a subject

A method for visualizing tissue of a subject includes receiving a first series of first imaging modality frames generated by imaging a region of tissue of the subject, and a first series of second imaging modality frames generated by imaging the region of tissue; displaying the first series of first imaging modality frames in combination with the first series of second imaging modality frames; storing a plurality of first imaging modality frames and a plurality of second imaging modality frames of the first series of second imaging modality frames in a memory; receiving a second series of first imaging modality frames generated by imaging the region of tissue; and displaying the second series of first imaging modality frames in combination with one or more of the second imaging modality frames of the first series of second imaging modality frames stored in the memory for visualizing the region of tissue.

Methods and systems for alignment of a subject for medical imaging are disclosed, and involve providing a reference image of an anatomical region of the subject, the anatomical region comprising a target tissue, processing the reference image to generate an alignment reference image, displaying the alignment reference image concurrently with real-time video of the anatomical region, and aligning the real-time video with the alignment reference image to overlay the real-time video with the alignment reference image. Following such alignment, the subject may be imaged using, for example, fluorescence imaging, wherein the fluorescence imaging may be performed by an image acquisition assembly aligned in accordance with the alignment.

Methods and systems for characterizing tissue of a subject include acquiring and receiving data for a plurality of time series of fluorescence images, identifying one or more attributes of the data relevant to a clinical characterization of the tissue, and categorizing the data into clusters based on the attributes such that the data in the same cluster are more similar to each other than the data in different clusters, wherein the clusters characterize the tissue. The methods and systems further include receiving data for a subject time series of fluorescence images, associating a respective cluster with each of a plurality of subregions in the subject time series of fluorescence images, and generating a subject spatial map based on the clusters for the plurality of subregions in the subject time series of fluorescence images. The generated spatial maps may then be used as input for tissue diagnostics using supervised machine learning.

Methods and systems for characterizing tissue of a subject utilizing a machine learning

Methods and systems for characterizing tissue of a subject are disclosed. The method includes receiving a time series of fluorescence images of the tissue of the subject wherein the images define a plurality of calculation regions, generating a plurality of time-intensity curves for the plurality of calculation regions, creating a set of parameter values for each calculation region, generating a total rank value for each calculation region by comparing the sets of parameter values, and converting the total rank value into a ranking map image. Also disclosed are methods and systems for characterizing a wound in tissue by generating a wound index value.

A method for assessing healing of a subject includes receiving a time series of signal intensity data capturing the transit of an imaging agent through tissue over a period of time, wherein the tissue comprises a plurality of calculation regions and wherein signal intensity in each calculation region over the period of time may be approximated by a time-intensity curve corresponding to the calculation region; determining, for each calculation region, a coefficient value that is related to at least a portion of the time-intensity curve corresponding to the calculation region; and converting the coefficient values across the plurality of calculation regions into a coefficient-derived image map.

Methods and systems for assessing healing of tissue

Methods and systems for assessing tissue of a subject include receiving a time series of signal intensity data capturing the transit of an imaging agent through tissue over a period of time, wherein the tissue comprises a plurality of calculation regions and wherein signal intensity in each calculation region over the period of time may be approximated by a time-intensity curve corresponding to the calculation region; determining, for each calculation region, a coefficient value that is related to at least a portion of the time-intensity curve corresponding to the calculation region; and converting the coefficient values across the plurality of calculation regions into a coefficient-derived image map.

Growing community of inventors

Vancouver, Canada

Lina Gurevich