A tissue analysis system and method process images to derive a remote PPG perfusion map from PPG amplitude levels obtained from the images as well as a PPG delay map from PPG relative delays between the PPG signals for each image region (pixel) with respect to a reference signal. The images are segmented into one or more tissue regions based on the PPG delay map (or segmentation information identifying tissue regions derived from the PPG delay map are received as input). The different tissue regions have distinct PPG delay characteristics. A level of perfusion can then be determined separately for each tissue region. Thus PPG delay information is used to enable different tissue types to be identified.

A computer-implemented method for determining a flow rate for a given vessel includes obtaining, via a processor, dynamic three-dimensional (3D) images of a subject utilizing nuclear medicine imaging. The method also includes obtaining, via the processor, injection parameters for a radiotracer bolus injected into the subject via an automated injector. The method further includes generating, via the processor, time activity curves (TACs) for the radiotracer bolus from the 3D images. The method even further includes estimating, via the processor, the flow rate for the given vessel based on a morphology of the one or more TACs and the injection parameters.

Systems and methods for enhanced parallel operation of user interface for cardiac index determination. An example method includes accessing medical images depicting a portion of a patient's heart. A unified user interface is presented which allows for a user to select medical images for analysis, with the unified user interface allowing a user to select a lesion and adjust detected vessels on a first medical image while the system is detecting vessels on a different medical image displayed on the unified user interface without navigating to a later user interface.

To provide means for measuring the blood flow rate of gastric subepithelial microvessel in real time. An endoscope system for measuring the blood flow rate of gastric subepithelial microvessel comprises a magnifying endoscope and a blood flow video data processing unit for processing blood flow video data obtained using the magnifying endoscope, and (A) the magnifying endoscope shoots a blood flow video of the gastric subepithelial microvessel and transmits the blood flow video to the blood flow video data processing unit, and (B) after receiving the blood flow video, the blood flow video data processing unit performs the following data processing (B1) to (B5): (B1) processing of decomposing the obtained blood flow video into frames, (B2) processing of removing a translation component by comparing an image of frame 1 and an image of subsequent frame 2, (B3) processing of calculating a difference in a red component between the images from which the translation component has been removed, (B4) processing of segmenting a part for which the difference in the red component has been calculated, and (B5) processing of calculating a segment size of obtained segment data.

Disclosed are methods and systems for determining an extent of PAD. The method may include a step of comparing a pre-PAD treatment oxygenation readings by obtaining a supine reading and limb raise reading to post-PAD treatment oxygenation readings of the limb or the portion thereof using a non-contact NIRS camera of a limb or a portion thereof, distal to a PAD treatment site using a supine reading and limb raise reading to post-PAD treatment oxygenation readings of the limb or the portion thereof using a second supine reading and a second limb raise reading. An indication may be presented of an improvement or a non-improvement in PAD in the limb based on a difference in oxygenation from pre-PAD treatment readings to post-PAD treatment oxygenation readings of the limb.

Systems and methods of predicting future medical events are based on the processing of medical images. The prediction of premature birth and estimation of gestational age based on ultrasound images are presented as illustrative examples. The new abilities to estimate the probability of future medical events, before they otherwise could be predicted, provides new avenues for the development of preventative treatments.

Described herein are systems, methods, and instrumentalities associated with automatically detecting and enhancing multiple objects in medical scan images. The detection and/or enhancement may be accomplished utilizing artificial neural networks such as one or more classification neural networks and/or one or more graph neural networks. The neural networks may be used to detect areas in the medical scan images that may correspond to the objects of interest and cluster the areas belonging to a same object into a respective cluster. These tasks may be accomplished, for example, by representing the areas corresponding to the objects of interest and their interrelationships with a graph and processing the graph through the one or more graph neural networks so that the areas belonging to each object may be properly labeled and clustered. The clusters may then be used to enhance the objects of interests in one or more output scan images.

A computer-implemented method comprises: obtaining input image data that defines image values for a first number of input image voxels; establishing a respective vesselness measure for at least parts of the input image voxels, wherein the respective vesselness measure defines an established degree of similarity of a structure mapped in a respective input image voxel to a mapping of a vessel; and applying a filter algorithm to the input image data for providing filtered image data, or scaling the image values of the input image voxels for providing scaled image data. A parameter value of at least one filter parameter of the filter algorithm is dependent upon at least one vesselness measure, or a respective scaling factor used for scaling a respective image value is dependent upon at least one vesselness measure.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and/or track coronary artery disease.

A smartphone-based hemoglobin (Hgb) assessment application quantitatively analyzes pallor in patient-sourced photos using image analysis algorithms to enable a noninvasive, accurate quantitative smartphone app for detecting anemia. A user takes a photo of his/her fingernail beds using the app and receives an accurate displayed Hgb level. Since fingernails do not contain melanocytes, the primary source of color of these anatomical features is blood Hgb. At the same time, quality control software minimizes the impact of common fingernail irregularities (e.g. leukonychia and camera flash reflection) on Hgb level measurement. Metadata recorded upon capturing the image is leveraged for determining a users' Hgb level thereby eliminating the need for external equipment. A personalized calibration of image data with measured Hgb levels improves the accuracy of the application.