Systems and methods for detecting, counting and analyzing the blood content of a surgical textile are provided, utilizing an infrared or depth camera in conjunction with a color image.

A microprocessor is operably coupled to a camera from which patient vital signs are determined. A temporal-variation-amplifier of at least two images is operable to generate a temporal variation, a vital-sign generator is operable to generate at least one vital sign from the temporal variation and a display device is operable to display the at least one vital sign.

According to one embodiment, a medical imaging analyzer includes an imaging unit, a calculator, and an analyzer. The imaging unit divides an area including an object of a subject to be captured into a plurality of partial areas such that the partial areas overlap each other to form an overlapping area, and administers a contrast agent to each of the partial areas to capture a plurality of time-series images. The calculator calculates, based on the transition of the pixel value in one of the time-series images having the overlapping area, the transition of the pixel value in the other time-series image having the overlapping area. The analyzer analyzes the time-series images based on the transition of the pixel value in the one and the other of the time-series images to obtain the hemodynamics of the subject.

The present invention relates to a device and method for obtaining pulse transit time and/or pulse wave velocity information of a subject (14). Based on a set of image frames (19) of a subject (14) and detected motion of body parts of the subject (14) regions of interest are selected from different non-moving body parts and pulse transit time and/or pulse wave velocity information is obtained from acquired PPG signals extracted from different regions of interest and the respective determined physical distance between the respective regions of interest.

Blood flow beneath a user's skin, for example, in a user's face may be visually rendered. In some aspects, a plurality of differences is determined in the intensity of pixels of a first image and the corresponding pixels of a subsequent second image. In some aspects, this plurality of differences is enhanced to accentuate a characteristic associated with the first image and the second image. The enhanced plurality of differences is visually rendered for each subsequent comparison of pixel intensity values.

Methods and systems for suppressing shadowgraphic flow projection artifacts in OCT angiography images of a sample are disclosed. In one example approach, normalized OCT angiography data is analyzed at the level of individual A-scans to classify signals as either flow or projection artifact. This classification information is then used to suppress projection artifacts in the three dimensional OCT angiography dataset.

Systems and methods are disclosed for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics. One method includes: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for part of the individual's vascular system; training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals; acquiring, for a patient, a geometric model and blood flow characteristics for part of the patient's vascular system; and executing the machine learning algorithm on the patient's geometric model and blood flow characteristics to determine, based on the predictive features, plaque information of the patient for at least one point in the patient's geometric model.

An imaging apparatus, an imaging method, methods, and image processing apparatuses. There is provided an imaging apparatus including: a light separation unit configured to separate coherent light into object light and reference light, wherein the object light irradiates an object; an optical element configured to cause interference between: scattered light emitted from a region irradiated by the object light and the reference light; an imaging unit configured to image a plurality of interference fringes having different phase differences between the object light and the reference light; and an image formation unit configured to form, from a plurality of images of the plurality of interference fringes, a three dimensional image including a speckle component comprising a random interference or diffraction pattern.

A method and system for anatomical object detection using marginal space deep neural networks is disclosed. The pose parameter space for an anatomical object is divided into a series of marginal search spaces with increasing dimensionality. A respective deep neural network is trained for each of the marginal search spaces, resulting in a series of trained deep neural networks. Each of the trained deep neural networks can evaluate hypotheses in a current parameter space using discriminative classification or a regression function. An anatomical object is detected in a medical image by sequentially applying the series of trained deep neural networks to the medical image.

Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.