An image processing apparatus includes a tomographic signal obtaining unit that obtains tomographic signals of light beams, the light beams respectively having a different polarization from each other and being obtained by splitting combined light obtained by combining return light and reference light, the return light being from an object to be inspected that is irradiated with measuring light, and an information obtaining unit that obtains three-dimensional polarization tomographic information, and three-dimensional motion contrast information of the object to be inspected, by commonly using at least one of the obtained tomographic signals. In addition, an extracting unit extracts a specific region of the object to be inspected using the obtained three-dimensional polarization tomographic information, and an image generating unit generates a motion contrast enface image of the extracted specific region using the obtained three-dimensional motion contrast information.

The present application relates to a method and system for analyzing blood flow conditions. The method includes: obtaining images at multiple time phases; constructing multiple vascular models corresponding to the multiple time phases; correlating the multiple vascular models; setting boundary conditions of the multiple vascular models respectively based on the result of correlation; and determining condition of blood vessel of the vascular models.

Optical coherence tomography (OCT) angiography (OCTA) data is generated by one or more machine learning systems to which OCT data is input. The OCTA data is capable of visualization in three dimensions (3D) and can be generated from a single OCT scan. Further, motion artifact can be removed or attenuated in the OCTA data by performing the OCT scans according to special scan patterns and/or capturing redundant data, and by the one or more machine learning systems.

Systems and methods of predicting future medical events are based on the processing of medical image. 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.

Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.

The present invention provides a computer device and a computer-implemented method to quantify the extent of functional coronary artery disease. In addition, the invention provides a computer device for determining the functional pattern of coronary disease in a mammal. It is shown that a mismatch in the extent of CAD between anatomical and physiological evaluations is predictable for an improvement in epicardial conductance with percutaneous revascularization. More particularly the invention provides methods to select a mammal suffering from coronary disease to benefit from a percutaneous coronary intervention.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for jointly training an encoder neural network and a decoder neural network. In one aspect, a method comprises, for each latent dimension in a proper subset of a plurality of latent dimensions of a latent space: processing a predefined embedding that represents the latent dimension using the decoder neural network to generate multi-modal data, having a plurality of feature dimensions, that defines a predicted multi-modal data archetype corresponding to the latent dimension; and updating the values of the set of decoder parameters using gradients of an archetype loss function that measures an error between: (i) a predicted multi-modal data archetype corresponding to the latent dimension, and (ii) a target multi-modal data archetype corresponding to the latent dimension.

Anatomical and functional assessment of coronary artery disease (CAD) using machine learning and computational modeling techniques deploying methodologies for non-invasive Fractional Flow Reserve (FFR) quantification based on angiographically derived anatomy and hemodynamics data, relying on machine learning algorithms for image segmentation and flow assessment, and relying on accurate physics-based computational fluid dynamics (CFD) simulation for computation of the FFR.

There is provided a method of generating a dataset of synthetic images, comprising: for each real image each depicting a real human anatomical structure: extracting and preserving a real anatomical structure region(s) from the real image, generating a synthetic image comprising a synthetic human anatomical structure region and the preserved real anatomical structure region(s), designating pairs of images, each including the real image and the synthetic image, feeding the pair into a machine learning model trained to recognize anatomical structure parts to obtain an outcome of a similarity value denoting an amount of similarity between the real image and the synthetic image, verifying that the synthetic image does not depict the real human anatomical structure when the similarity value is below a threshold, wherein an identity of the real human anatomical structure is non-determinable from the synthetic image, and including the verified synthetic image in the dataset.

An image processing device includes a processor, in which the processor is configured to acquire a flow velocity vector of a fluid in a structure, generate a flow velocity vector image obtained by visualizing a spatial distribution of the flow velocity vector, and perform display control for changing a display aspect according to a magnitude of fluctuation of the flow velocity vector, in the flow velocity vector image.