Abstract: 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.

Abstract: 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.

Abstract: The present disclosure relates generally to improving operating room scheduling and resource management efficiency, and more specifically to techniques for monitoring various aspects of an operating room. An exemplary method for adjusting a surgical schedule specifying timing information for one or more surgeries to occur in an operating room comprises: receiving one or more images of the operating room captured by one or more cameras; detecting one or more surgical milestones of a plurality of predefined surgical milestones using one or more trained machine-learning models based on the received one or more images, wherein the one or more machine learning models are trained using a plurality of training images depicting the one or more surgical milestones; and adjusting, based on the detected one or more surgical milestones, the surgical schedule specifying timing information for the one or more surgeries to occur in the operating room.

Abstract: 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.

Abstract: A tool for use in endoscopic surgical procedures includes a shaft, wherein the shaft includes a first end, a second end, and a first axis; and a pointer at the first end of the shaft, wherein the pointer includes: a tip located on the first end of the pointer, and a plurality of fiducial markers disposed on the pointer, wherein at least one of the fiducial markers is disposed on a surface that extends transversely to the first axis, wherein the plurality of fiducial markers are configured for providing information for locating the tip of the pointer in an endoscopic image captured by an endoscopic imaging device.

Abstract: The present disclosure relates generally to medical imaging, and more specifically to extracting a subset of images from a series of images (e.g., surgical video feeds) for training machine-learning models and/or conducting various downstream analyses. The system can hash image data for each image of a series of video images of the surgery to obtain a series of hash values; calculate a plurality of difference values for the series of hash values, each of the plurality of difference values indicative of a difference between two consecutive hash values in the series of hash values; generate a plurality of image clusters by clustering the plurality of distance values; select one or more image clusters from the plurality of image clusters; and produce a subset of surgical images from the series of video images using the selected one or more image clusters from the plurality of image clusters.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure relates generally to medical imaging, and more specifically to machine-learning techniques for clarifying and enhancing intraoperative images. The system can receive one or more intraoperative images depicting a biological tissue and smoke; input the one or more intraoperative images into a trained neural network to obtain a clarified image depicting the biological tissue that is less obscured by smoke than at least one of the received one or more intraoperative images; enhance, using an equalization algorithm, contrast in the clarified image to obtain an enhanced clarified intraoperative image; and display, on a display, the enhanced clarified intraoperative image, which can be used for decision making within or outside surgeries.

Abstract: The present disclosure relates generally to medical imaging, and more specifically to machine-learning techniques to generate intraoperative fluorescence images of a subject (e.g., to aid a surgery, to aid diagnosis and treatment of diseases). The system can receive an intraoperative white light image of the subject, input the intraoperative white light image of the subject into a generator of a trained generative adversarial network (GAN) model trained. In some examples, the GAN model is trained using a plurality of training image pairs, and each training image pair comprises an intraoperative white light training image and an intraoperative fluorescence training image of a same tissue. The system can obtain, from the generator, the generated intraoperative fluorescence image of the subject and display, on a display, the generated intraoperative fluorescence image of the subject.

Abstract: The present disclosure relates generally to medical imaging, and more specifically to machine-learning techniques to analyze and generate medical images to characterize tissue of a subject (e.g., to aid diagnosis and/or treatment of diseases). An exemplary method of displaying risk assessment of a tissue of a subject comprises: receiving a fluorescence image of the tissue of the subject; providing the fluorescence image to a trained generative adversarial (“GAN”) model, wherein the GAN model is trained using a plurality of unlabeled training images associated with a normal future outcome; obtaining, based on the GAN model, one or more pixel-wise anomaly scores associated with one or more pixels of the fluorescence image, wherein the one or more pixel-wise anomaly scores are indicative of abnormality of the tissue of the subject; and displaying, based on the one or more pixel-wise anomaly scores, the risk assessment of the tissue in the fluorescence image.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.