In one embodiment, an imaging device determines color information for a portion of organic tissue from one or more captured color images of the tissue. The imaging device identifies one or more optical properties of the portion of tissue based on the determined color information. The imaging device adjusts fluorescence data captured via one or more fluorescence images of the portion of organic tissue. The imaging device provides the adjusted fluorescence data to an electronic display for display.

An image processing apparatus, comprises an information acquiring unit that acquires three-dimensional data representing characteristic information on an object at a plurality of voxels; a shape information acquiring unit that acquires information on a surface shape of the object; a distance calculating unit that calculates, for each of the voxels, a distance between a surface of the object and a position inside the object corresponding to the voxel, based on the information on the surface shape; a filtering unit that performs, for each of the voxels, filtering processing, including blur processing in accordance with the calculated distance; and an image generating unit that generates a two-dimensional image, based on the three-dimensional data after the filtering processing.

Systems and methods are disclosed for predicting coronary plaque vulnerability, using a computer system. One method includes acquiring anatomical image data of at least part of the patient's vascular system; performing, using a processor, one or more image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis on the anatomical image data; predicting, using the processor, a coronary plaque vulnerability present in the patient's vascular system, wherein predicting the coronary plaque vulnerability includes calculating an adverse plaque characteristic based on results of the one or more of image characteristics analysis, geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis of the anatomical image data; and reporting, using the processor, the calculated adverse plaque characteristic.

The present invention provides an FFR determination method and apparatus based on multi-modal medical image, a device, and a medium. The method includes: obtaining an intravascular image comprising a blood vessel segment of interest; obtaining an extravascular image comprising a blood vessel segment to be detected, where the blood vessel segment to be detected at least partially coincides with the blood vessel segment of interest; performing registration on the intravascular image and the extravascular image to obtain a registration result; and determining a target fractional flow reserve based on multi-modal medical image by using the registration result on the basis of the intravascular image and the extravascular image. In the FFR determination method of the present invention, calculation of the fractional flow reserve is optimized by utilizing the registration result of the intravascular image and the extravascular image and integrating advantageous information of the intravascular image and the extravascular image, so as to obtain the fractional flow reserve which is based on multi-modal medical image and has both blood vessel segment integrity and local accuracy, thereby improving the accuracy and stability of fractional flow reserve calculation.

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.

Systems and methods of fluid or air passageway cross-sectional area determination in an anatomy are disclosed. In some examples, the methods may include generating a model of a structure based on a plurality of images of the structure, the structure comprising at least one fluid or air flow path. In some examples, the methods may also include identifying an obstruction element in the model of the structure, the obstruction element affecting the at least one fluid or air flow path in the model. In some examples, the methods may also include determining a region of the at least one fluid or air flow path for flow analysis.

Systems and methods for determining corresponding locations of points of interest in a plurality of input medical images are provided. A plurality of input medical images comprising a first input medical image and one or more additional input medical images is received. The first input medical image identifies a location of a point of interest. A set of features is extracted from each of the plurality of input medical images. Features between each of the sets of features are related using a machine learning based relational network. A location of the point of interest in each of the one or more additional input medical images that corresponds to the location of the point of interest in the first input medical image is identified based on the related features. The location of the point of interest in each of the one or more additional input medical images is output.

In an automated detection system for acute ischemic stroke, a preprocessor performs registration on a whole-brain image and a standard-brain spatial template to extract individual brain region masks from the whole-brain image. A deep learning encoder performs feature extraction on the whole-brain image and the individual brain region masks, thereby converting the whole-brain image into 2D whole-brain slice images. A first processor maps the individual brain masks onto the whole-brain slice images for registration, thereby generating sets of brain region slice images. A second processor computes the stroke-related weight values of the slice images of each of the sets of brain region slice images and sums the weight values to obtain the characteristic value of each brain region. A disparity-aware classifier determines whether any brain region has acute ischemic stroke according to the characteristic value of each brain region.

A three dimensional classification system for recognizing cross-sectional images automatically contains a processor that executes: (1) rescaling of a plurality of cross-sectional images; and feeding the rescaled plurality of cross-sectional images into two branches; (2) feeding the rescaled plurality of cross-sectional images into a first branch for performing a plurality of convolutions on the rescaled plurality of cross-sectional images directly to learn features for distinguishing phases; (3) feeding the rescaled plurality of cross-sectional images into a second branch for reducing resolution, and then performing a plurality of convolutions on the reduced resolution plurality of cross-sectional images to learn features for distinguishing phases; and (4) concatenating convolutional output channels from the two branches to fuse global and local features, on which two fully-connected layers are stacked as a classifier to recognize cross-sectional volumetric images accurately and quickly.

Techniques for efficiently generating time-varying image data for use in training a machine learning model are disclosed. An aspect of the present disclosure relates to a machine learning model trained using training data that includes at least one piece of training time-varying image data of second time-varying image data and third time-varying image data, the second time-varying image data being obtained by standardizing first time-varying image data in a time direction, the first time-varying image data being based on a reception signal for image generation received by an ultrasound probe, third time-varying image data being based on the second time-varying image data, and, and ground truth data including a detection target corresponding to the at least one piece of training time-varying image data.