An endoscope system that illuminates an object and captures reflected light from the object includes a control processor. The control processor acquires an examination image and determines whether the examination image shows a swallowing state or a non-swallowing state. In addition, the control processor detects a high pixel value region from the examination image and determines that the examination image shows the swallowing state in a case in which an area of the high pixel value region is equal to or greater than a first threshold value. Further, the control processor performs grayscale conversion on the examination image to obtain a grayscale image and performs a binarization process for obtaining the high pixel value region in a case in which a density value of a pixel of the grayscale image is equal to or greater than a second threshold value.

Tomographic images are obtained by processing a tilt series of 2D images by aligning and combining images withing a group of neighbor images. The tilt series generally includes sparsely sampled images. Images of the tilt series at tilt angles associated with the sparsely sample images are selected as reference frames, grouped with neighbor images, and the group of images aligned. The aligned images are combined to produce replacement frames and a replacement frame tilt series that can be used for tomographic reconstruction.

A quantification system (700) is described that includes: at least one input (710) configured to provide two input medical images and two locations of interest in said input medical images that correspond to a same anatomical region; and a mapping circuit (725) configured to compute a direct quantification of change of said input medical images from the at least one input (710).

A method includes obtaining image data, selecting image datasets from the image data, creating three-dimensional (3D) matrices based on the selected image dataset, refining the 3D matrices, applying one or more matrix operations to the refined 3D matrices, selecting corresponding matrix columns from the 3D matrices, applying big data convolution algorithm to the selected corresponding matrix columns to create a two-dimensional (2D) matrix, and applying a reconstruction algorithm to create a super-resolution biomarker map image.

Systems and methods for super-resolution ultrasound imaging of microvessels in a subject are described. Ultrasound data are acquired from a region-of-interest in a subject who has been administered a microbubble contrast agent. The ultrasound data are acquired while the microbubbles are moving through, or otherwise present in, the region-of-interest. The region-of-interest may include, for instance, microvessels or other microvascuiature in the subject. By isolating, localizing, tracking, and accumulating the microbubbles in the ultrasound data, super-resolution images of the microvessels can be generated.

An ultrasound diagnostic apparatus 1 includes an image acquisition unit 3 that generates an ultrasound image, an image recognition unit 9 that performs image recognition for the ultrasound image to calculate recognition scores, an index value calculation unit 10 that calculates index values of a plurality of parts on the basis of the recognition scores calculated for a predetermined number of ultrasound images, an order decision unit 11 that decides a determination order in which part determination is performed for the plurality of parts on the basis of the index values, and a part determination unit 12 that determines an imaging part of a subject on the basis of the recognition scores calculated according to the determination order.

An apparatus and a method for contrast-enhanced ultrasound (CEUS) including use of a fluid dynamics model for the analysis of dynamic contrast-enhanced ultrasound (DCEUS).

A microscope system includes an eyepiece, an objective, a tube lens that is disposed between the eyepiece and the objective, a projection apparatus that projects a projection image onto an image plane on which an optical image is formed by the tube lens, and a processor that performs processes. The processes include performing for digital image data of the sample at least one analysis process selected from a plurality of analysis processes, and generating projection image data representing the projection image on the basis of the analysis result and the at least one analysis process. The projection image data indicates the analysis result in a display format including an image color corresponding to the at least one analysis process. The generating the projection image data includes determining a color for the projection image in accordance with the at least one analysis process selected from the plurality of analysis processes.

The disclosure provides a theseometer or proprioceptometer for objectively quantifying the proprioceptive performance of a subject such as a human. The disclosed theseometer is a device comprising a clear, rigid material or screen having or exhibiting a distinguishable target embraced by a series of concentric rings, a digital camera with a lens concentric to the target, a base unit comprising an electronic processor and memory for analyzing data and, optionally, a wheeled base to provide mobility and portability.

The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.