[Problem] To evaluate objective cell groups by estimating a mixing ratio of objective cell group without affecting treatment itself and its production process when the objective cell groups include plural kinds of cell groups having different attributes, and to provide an image analysis system and a culture management system capable of accurately perform a quality control and a production control with low cost. [Solution] The cell quality evaluation system 1 detects a feature amount in each cell easily analyzable from images, and estimates a mixing ratio of each of plural kinds of cell groups included in the objective cell groups based on a distribution of the detected feature amount and pre-recorded information of the feature amount. As the feature amount, the embodiment uses a migration speed of each cell, easily distinguishable by analyzing tracking of each cell from plural images in time series.

A greenhouse information automatic monitoring method, adopting a multi-sensor system, using binocular vision multi-function cameras combining with a laser ranging sensor and an infrared temperature measuring sensor, realizing online patrol monitoring of greenhouse crop comprehensive information of image and infrared temperature characteristics of plant nutrition, water, pest and disease damage as well as plant crown width, plant height, fruit and growth characteristics. The multi-sensor system is mounted on a suspension slide platform and combines with a lifting mechanism and an electric control rotation pan-tilt, such that not only accurate positioning and stationary point detection in the detection travelling direction can be realized, but also multi-sensor information patrol detection at different detection distances, different top view fields and different detection angles is realized.

A diagnosis support system having a processor configured to acquire medical images; detect a medicine and/or equipment used when the medical images are captured, from the medical images by image recognition; detect a region of interest from the medical images by image recognition; assign, to the medical image from which the medicine and/or equipment is detected, first detection information indicating the detected medicine and/or equipment; and assign, to the medical image from which the region of interest is detected, second detection information indicating the detected region of interest, display, on a display device, the medical images in a list in a display form according to the first detection information and the second detection information.

A harmonization system for a brain activity classifier harmonizing brain measurement data obtained at a plurality of sites to realize a discrimination process based on brain functional imaging: obtains data, for a plurality of traveling subjects as common objects of measurements at each of the plurality of measurement sites, resulting from measurements of brain activities of a predetermined plurality of brain regions of each of the traveling subjects; calculates, for each of the traveling subjects, prescribed elements of a brain functional connectivity matrix representing the temporal correlation of brain activities of a set of the plurality of brain regions; using a generalized linear mixed model, calculates measurement bias data 3108 for each element of the brain functional connectivity matrix, as a fixed effect at each measurement site with respect to an average of the corresponding element across the plurality of measurement sites and across the plurality of traveling subjects; and thereby executes a harmonizing process.

A deep learning model may be trained to provide an estimated image of the interior of a patient, based on a number of image sets, each image set comprising an interior image of the interior of a person and a contour image of the person's outer contour at a specific point in time. The model is trained to establish an optimized parametrized conversion function G specifying the correlation between the interior of the person and the persons outer contour based on the image sets. The conversion function G can then be used to provide estimated images of patient's interior based on their contours.

An image acquisition unit acquires a phase contrast image consisting of a plurality of phases for each of three spatial directions, in which a pixel value of each pixel represents a velocity of fluid for each of the three directions, the phase contrast image being acquired by imaging a subject including a structure inside which fluid flows by a three-dimensional cine phase contrast magnetic resonance method. An identification unit identifies a region of the structure in the phase contrast image on the basis of a maximum value of the velocity of the fluid between corresponding pixels in each of the phases of the phase contrast image.

A control apparatus includes a processor. The processor detects an image pickup scene based on a picked-up image acquired by an image pickup apparatus disposed in an insertion section included in an endoscope and calculates operation information corresponding to the image pickup scene using an approach of machine learning.

There is provided a cell evaluation device comprising an acquisition unit that acquires an image set of a plurality of cell images in which a plurality of cells having a protrusive structure are imaged in time series during culture; a reception unit that receives designation of a cell of interest which is a cell of interest to a user, the cell being at least one cell among the plurality of cells which are shown in one cell image of interest other than an oldest cell image in the image set; a specifying unit that captures a growth process of the protrusive structure based on the image set to specify a protrusive structure of interest, which is the protrusive structure extending from the cell of interest in the cell image of interest; and a display control unit that performs control to display the protrusive structure of interest in a display form different from that of other protrusive structures in the cell image of interest.

A medical imaging method for detecting a movement and a magnetic resonance imaging system, wherein the method includes: receiving, through a plurality of channels, a plurality of original first time domain signals recording a movement of an object under examination; transforming, based on a plurality of respiratory frequency components as bases, the plurality of first time domain signals into a vector matrix including representations of phases; computing an eigenvector based on the vector matrix; transforming the first time domain signals into second time domain signals based on the eigenvector, and removing a maximum energy term related to a respiratory movement from the second time domain signals; and determining whether a portion of a non-respiratory body movement is detected, and determining to execute a sequence based on only a first time domain signal in which a portion of a non-respiratory body movement is not detected.

An information processing apparatus comprising: an extraction unit configured to extract a predetermined region of an anatomical part from an examination image of a subject; a first feature amount acquisition unit configured to acquire a first feature amount of the predetermined region related to a movement of the anatomical part; a second feature amount acquisition unit configured to acquire a second feature amount of the predetermined region related to the movement; a division unit configured to divide the predetermined region; and an integration unit configured to integrate the first feature amount and the second feature amount based on a result of division by the division unit.