Konica Minolta, Inc.

Hitoshi Futamura

Sho Noji

Koichi Fujiwara

Akinori Tsunomori

Shinsuke Katsuhara

Satoshi Kasai

Hiroaki Matsumoto

Noritsugu Matsutani

Hirotake Minami

Tsuyoshi Kobayashi

Nodoka Iida

Eri Suzui

A learning apparatus includes a hardware processor. The hardware processor obtains a first medical image. The hardware processor generates a second medical image in which a feature according to feature information related to a portion other than a recognition target of the first medical image is made close to the feature according to standard feature information which is to be a predetermined standard. The second medical image is used to train a machine learning model.

Learning apparatus, learning system, learning method of machine learning model, and storage medium

A selection support system includes one or more hardware processors. A hardware processor among the one or more hardware processors obtains biological information of a subject. The hardware processor layers and accumulates, in an accumulator, input operation information about an input operation performed on the obtained biological information by a user. In response to obtaining new biological information, according to the input operation information accumulated in the accumulator, the hardware processor generates recommendation information that includes identification information identifying an application for analyzing the new biological information and additional information indicating a characteristic of the application.

Selection support system and storage medium

An information processing device includes: a plurality of identifiers that discriminates whether abnormal data that is information indicating an abnormal value is included in treatment data and detects the abnormal data; and a hardware processor that: customizes evaluation data used when evaluating discrimination accuracy of the plurality of identifiers; calculates the discrimination accuracy of each of the plurality of identifiers using the evaluation data customized by the hardware processor; and selects one identifier out of the plurality of identifiers as an identifier used for discrimination based on a calculation result by the hardware processor.

Information processing device and information processing method

A dynamic analysis apparatus includes: an obtainment unit configured to set a region of interest in dynamic images obtained by photographing a dynamic state by irradiation of a check target part with radial rays, and obtain movement information on movement of the region of interest; a determination unit configured to determine an emphasis level of a pixel signal value of an attentional pixel corresponding to a pixel in the region of interest on the basis of the movement information of the region of interest obtained by the obtainment unit; and a correction unit configured to correct the pixel signal value of the attentional pixel of the dynamic images or analysis result images generated by analyzing the dynamic images, on the basis of the emphasis level determined by the determination unit.

Dynamic analysis apparatus

A dynamic analysis system includes a diagnostic console which calculates at least one index value representing variation in a target portion of a human body from at least one dynamic image acquired by performing radiographic imaging to a subject containing the target portion, and evaluates flexibility of the target portion based on the calculated index value.

Dynamic analysis system

A dynamic analysis system includes a hardware processor. The hardware processor: analyzes a dynamic image for a dynamic state of a living body; generates an analysis result image showing the analysis result; determines, for each pixel of the dynamic image or the analysis result image, whether a pixel value is within a predetermined range of values; classifies the pixels into groups according to the determination result; extracts, as each border pixel, a pixel in a group adjacent to a pixel classified into a different group; generates a border between the groups based on the extracted border pixels; superimposes the border on, between the dynamic image and the analysis result image, an image not subjected to the classification, thereby generating a combined image; and causes an output device to output the combined image.

A dynamic analysis device includes a processor. The processor performs filtering on a dynamic image in a feature quantity space formed of two or more axes with feature quantities of the respective axes. The dynamic image is obtained by dynamic imaging performed by emitting radiation to an examination target site. Further, the processor calculates a feature quantity relating to a dynamic state of the examination target site based on the filtered dynamic image.

Dynamic analysis device, dynamic analysis system, dynamic analysis method and non-transitory computer readable storage medium

A dynamic analysis apparatus may include a setting section which sets a target region in a lung region of a chest dynamic image; a conversion section which calculates a representative value of a pixel signal value in the target region, and converts the pixel signal value; an extraction section which extracts a pulmonary blood flow signal from the image; and a calculation section which calculates a change in the pulmonary blood flow signal, and calculates a feature amount regarding pulmonary blood flow. The setting section may determine a size of the target region based on a size of a body part other than a lung blood vessel, a movement amount of a body part other than the lung blood vessel or subject information of the chest dynamic image, the subject information regarding a subject of the radiation imaging, and the setting section may set the target region.

Dynamic analysis apparatus and dynamic analysis system

A medical image system includes: a storage unit which stores a first medical image; a selection unit which selects a medical image to be associated with a second medical image; a designation unit which designates an abnormal shadow candidate; a reception unit which receives the second medical image newly created; a determination unit which determines whether or not the first medical image selected is an image suited for association, and the abnormal shadow candidate designated and the second medical image received satisfy a predetermined condition; an association unit which stores the second medical image in association with the abnormal shadow candidate; and a monitoring unit which monitors the association, outputs a warning, and stores the abnormal shadow candidate or the second medical image in association with information indicating there is no second medical image or abnormal shadow candidate to be associated, respectively.

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Hachioji, Japan

Hitoshi Futamura