A system and method for automatically monitoring a ruminant animal. The system includes a 3D camera system that obtains images from a region of interest, in particular the paralumbar fossa. An image processor determines the surface curvature in the region of interest, as a function of time. Based on the frequency with which this function attains local maxima, a health indication for the animal is generated.

The present disclosure provides a method of DDCE-MRF. The method can include: a) introducing two or more contrast agents to a region of interest (ROI) of a subject, the two or more contrast agents having different relaxivities; b) measuring a T1 relaxation time and a T2 relaxation time for locations within the ROI using magnetic resonance fingerprinting (MRF); c) determining, using equations that relate the different relaxivities, the T1 relaxation time, the T2 relaxation time, and concentrations of the two or more contrast agents, the concentrations of the two or more contrast agents for each of the locations within the ROI; and d) producing an image depicting the ROI based, at least in part, on the concentrations of the two or more contrast agents.

A cell observation system according to the present invention includes: a first image-acquisition device disposed in an incubator and acquires a first image of cells in a culturing vessel; a second image-acquisition device disposed outside the incubator; a processing device connected to the first and second image-acquisition devices; and a display. The second image-acquisition device includes a second image-acquisition unit that acquires a second image of the interior of the culturing vessel that removed from the incubator, a support that supports the second image-acquisition unit and the culturing vessel, and a position measuring unit that measures a position between the culturing vessel and the second image-acquisition unit at the time of acquiring the second image. The processing device extracts target cells in the first image, calculates positions of the target cells, and displays the relationship between the positions of the target cells and the second image.

An information processing apparatus 100 comprising: first inference unit configured to perform a first inference to medical image data and obtain information related to a diagnostic name identified from the medical image data as a first inference result; and second inference unit configured to perform a second inference to the medical image data and the information related to the diagnostic name and obtain information related to an image finding as a second inference result.

The present disclosure provides a method, an apparatus for acquiring blood vessel evaluation parameter based on physiological parameter, and a storage medium. The method for acquiring blood vessel evaluation parameter based on physiological parameter comprises acquiring a physiological parameter (S000); acquiring a blood flow velocity v (S020); acquiring, in real time, an aortic pressure waveform changing over time (S020); acquiring a coronary artery blood vessel evaluation parameter according to the blood flow velocity v, the aortic pressure waveform and the physiological parameter (S030). The coronary artery blood vessel evaluation parameter is acquired according to the blood flow velocity v, the aortic pressure waveform and the physiological parameter.

A method and system are provided for determining a navigation pathway for an invasive medical instrument in a blood vessel is provided. The method includes receiving a first medical image. The method further includes determining one or more parameters associated with the first medical image. Additionally, the method includes identifying a second medical image in a computer memory, based on the determined one or more parameters. Furthermore, the method includes modifying the second medical image based on the first medical image. The method also includes determining from the modified second medical image the navigation pathway for the invasive medical instrument.

Systems and methods for processing electronic images from a medical device comprise receiving a first image frame and a second image frame from a medical device, and determining a region of interest by subtracting the first image frame from the second image frame, the region of interest corresponding to a visual obstruction in the first image frame and/or second image frame. Image processing may be applied to the first image frame and/or second image frame based on a comparison between a first area of the first image frame corresponding to the region of interest and a second area of the second image frame corresponding to the region of interest, and the first image frame and/or second image frame may be provided for display to a user.

One or more implementations allow for detecting B-lines in ultrasound video and images for diagnostic purposes through analysis of Q-mode images for B-line detection.

A system and a method measure volumetric changes of brain structures. The method includes initializing an intensity value of all voxels of a 3D voxel dataset representing the brain of a subject to an initial value preferentially equal to 0. For all voxels that belong to a segmented brain structure for which reference data of a longitudinal reference model exists, automatically executing the following steps: calculating a deviation of a volume change for the segmented brain structure from the longitudinal reference model, normalizing the deviation to obtain a quantitative value of the volume change on a same scale for voxel's belonging to different brain structures; and setting the intensity value of the voxels to the previously obtained quantitative value Q. The voxels of the 3D voxel dataset are displayed in a form of a longitudinal deviation map.

Methods and apparatus to adapt medical imaging interfaces based on learning are disclosed. An example apparatus includes a use monitor to monitor, in a first session, user actions and medical content data pertaining to operation of a clinical image display, a learning device including a processor to implement a learning network to develop a model for a subsequent session based on the user actions in relationship to a context of the medical content data. The model developed by defining contextual patterns of the user actions based on the context and the medical content data. The learning device is to update, prior to or during a second session subsequent the first session, a user interface based on the model.