Disclosed is a beauty counseling information providing method and apparatus, which may generate and provide beauty counseling information by executing an artificial intelligence (AI) algorithm and/or a machine learning algorithm in a 5G environment connected for Internet-of-Things. A beauty counseling information providing method according to an embodiment of the present disclosure may include generating a first image set that has classified a plurality of images previously stored based on capturing information every predetermined period, generating a second image set that has classified a plurality of images included in the first image set based on the purpose of providing counseling information, calculating a body feature through comparative analysis of the plurality of images included in the second image set, and providing the beauty counseling information when the amount of change between the calculated body feature and previously stored existing body feature exceeds a predetermined value.

An imaging system, such as a DBT system, capable of providing an operator of the system with information concerning the location, magnitude and direction of motion detected by the system during performance of the scan to enhance image processing. The imaging system provides the motion information to the operator directly in conjunction with the images processed by the imaging system thereby providing the operator with sufficient information for decisions regarding the need for additional images for completing the scan with the imaging system before the patient is discharged, or even before the breast is decompressed.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing fundus images using fundus image processing machine learning models. One of the methods includes obtaining a model input comprising one or more fundus images, each fundus image being an image of a fundus of an eye of a patient; processing the model input using a fundus image processing machine learning model, wherein the fundus image processing machine learning model is configured to process the model input comprising the one or more fundus image to generate a model output; and processing the model output to generate health analysis data.

A system for monitoring anastomosis healing includes an imaging device for observing a first distance at a first location between first and second staple lines at a first instant in time, and a second distance at the first location at a second instant in time, and a programmable device configured to calculate a difference between the first and second distances and to compare the difference with known distances of anastomoses exhibiting known conditions.

A first sequence of cardiac image frames are received by a first neural network of the neural network system. The first neural network outputs a first set of feature values. The first set of feature values includes a plurality of data subsets, each corresponding to a respective image frame and relating to spatial features of the respective image frame. The first set of feature values are received at a second neural network of the neural network system. The second neural network outputs a second set of feature values relating to temporal features of the spatial features. Based on the second set of feature values, a cardiac phase value relating to a cardiac phase associated with a first image frame is determined.

The present invention relates to an image processing apparatus, an image processing method, and a recording medium for recording the same, the image processing apparatus including a storage configured to comprise a standard database (DB) established based on information about a predetermined anatomical entity; and at least one processor configured to obtain a local motion vector by registration between a first medical image and a second medical image taken by scanning an object including the anatomical entity, use a predictive local motion vector generated from the standard DB to normalize the local motion vector according to a plurality of regions in the anatomical entity, and make information about conditions of the anatomical entity based on the normalized local motion vector be provided according to the plurality of regions. By the normalization according to the regions, it is possible to provide distinguishable information about difference between the local motion vector of a pulmonary disease patient to be subjected to a diagnosis and a predictive local motion vector of a normal person generated from the standard DB through the statistical modeling using bio-information of a patient.

A quantification system (300) is described that includes: at least one input (310) configured to provide at least one input medical image and provide a location of interest in the at least one input medical image; and a mapping circuit (325). The mapping circuit (325) is configured to: compute a direct quantification result from the location of interest of the at least one input medical image to a quantification of interest and output a direct first quantification result therefrom; generate a saliency map as part of the computation of the direct quantification result; and derive a segmentation from the saliency map, such that the segmentation independently generates a second quantification result that is within a result range of the direct first quantification result.

The invention relates to a method, an image processor (26) and a medical observation device (1), such as a microscope or endoscope, for observing an object (4) containing a bolus of at least one fluorophore (12). The object (4) is preferably live tissue comprising several types (16, 18, 20) of tissue. According to the method, a set (34) of component signals (36) is provided. Each component signal (36) represents a fluorescence intensity development of the fluorophore (12) over time in a different type of tissue. A time series (8) of input frames (10) is accessed, one input frame (10) after the other. The input frames (10) represent electronically coded still images of the object (4) at subsequent time. Each input frame (10) contains at least one observation area (22) comprising at least one pixel (23). In the observation area (22) of the current input frame (10) of the time series (8), a fluorescent light intensity (I) is determined over at least one fluorescence emission wavelength (15) of the fluorophore (12). This fluorescent light intensity (I.sub.1) is joined with the fluorescence light intensities (I.sub.n) of the observation area (22) of preceding input frames (10) of the time series (8) to generate a time sequence (40) of fluorescent light intensities (I.sub.1, I.sub.n) of the observation area (22). This time sequence (40) is decomposed on in a preferably linear combination (72) of at least some of the component signals (36) of the set (34). A new set (34) of component signals (36) is provided which includes only those component signals (36) which are present in the combination (72). An output frame (46) is generated, in which the observation area (22) is assigned a color from a color space depending on the combination (72) of component signals (36).

A system and method for determining kinetic parameters associated with a kinetic model of an imaging agent in a liver is provided. An image reconstruction device can receive radiotracer activities corresponding to a predetermined time period. For example, these radiotracer activities can include PET scan data corresponding to a number of time frames. The radiotracer activities can be used to determine a liver time activity curve and a circulatory input function. The liver time activity curve and circulatory input function can be used along with a kinetic model of the liver to produce kinetic parameters. These kinetic parameters can be used to determine hepatic scores, such as a hepatic steatosis score, a hepatic inflammation score, and a cirrhosis score. These scores are indicative of diseases of the liver, including nonalcoholic fatty liver disease, nonalcoholic steatohepatitis, and hepatic fibrosis.

Relationships between morphological changes to an eye due to intraocular pressure changes and blood perfusion changes in the retina are determined by colocalizing retinal perfusion data and optic nerve head (ONH) mechanical deformation data. Perfusion changes from intraocular pressure (IOP) changes are determined by colocalizing retinal perfusion data with ONH mechanical deformation data. Optical coherence tomography-angiography (OCT-A) can be used to generate both retinal perfusion data and mechanical deformation data for an imaged volume. A three-dimensional model (e.g., connectivity map or connectivity model) of the vasculature can be generated from the OCT-A imaging data and used to predict changes in blood perfusion in various areas of the retina due to IOP-induced mechanical deformations.