Konica Minolta, Inc.

Icad, Inc.

Shinsuke Katsuhara

Hitoshi Futamura

Keiko Itaya

Shikou Kaneko

Satoshi Kasai

Hirotake Minami

Nodoka Iida

Hiroaki Matsumoto

Noritsugu Matsutani

Sho Noji

Akinori Tsunomori

Jeffrey C Wehnes

David S Harding

Ryohei Ito

James H Pike

Tsuyoshi Kobayashi

Amai Shimizu

Muhammad Sabir

Ryoichi Watanabe

Eri Suzui

Sridharan Kamalakanan

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

Provided is an information processing device that is interposed between an imaging control device and an image management device in communication. The information processing device including an abnormality detector and a first hardware processor. The abnormality detector performs detection of an abnormality in a medical image received from the imaging control device. The first hardware processor that determines, based on a result of the detection by the abnormality detector, content or a destination of transmission of the medical image or the result of the detection.

Information processing device and medical image system

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

An image processing apparatus includes a hardware processor. The hardware processor calculates a characteristic amount from each frame images of a moving image. The characteristic amount indicates change in a subject. The moving image is obtained by radiographing a dynamic state of the subject. The hardware processor extracts a frame image having the characteristic amount satisfying a predetermined condition, processes the extracted frame image by applying an image processing parameter to the extracted frame image prior to a remaining frame image, and causes a display to display the processed frame image.

Image processing apparatus and computer-readable storage medium

A radiation image processing apparatus includes a display and a hardware processor. The display displays an image. The hardware processor is configured to perform the following, acquire radiographic moving image data comprising a plurality of frame images, subject the moving image data to predetermined analytical processing, generate an analyzed moving image comprising a plurality of analyzed frame images, select a plurality of specific analyzed frame images from the analyzed frame images of the analyzed moving image, derive a calculation signal value based on signal values of pixels having common coordinates positioned in common coordinates in the selected specific analyzed frame images, and cause a calculated image based on the calculation signal values generated according to coordinates to appear on the display.

Radiation image processing apparatus and radiation image capturing system

A dynamic analysis system includes a hardware processor. A first plurality of frame images showing a dynamic state of a chest of an intubated first examinee is obtained by radiation imaging of the chest of the first examinee. Based on the first plurality of frame images, the hardware processor calculates a signal value change amount and/or a shape change amount of a predetermined structure of the chest in the first plurality of frame images. Based on the calculated signal value change amount and/or shape change amount, the hardware processor generates assessment information to assess a respiratory status of the first examinee at time of intubation or extubation.

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.

Lung segmentation and bone suppression techniques are helpful pre-processing steps prior to radiographic analyzes of the human thorax, as may occur during cancer screenings and other medical examinations. Autonomous lung segmentation may remove spurious boundary pixels from a radiographic image, as well as identify and refine lung boundaries. Thereafter, autonomous bone suppression may identify clavicle, posterior rib, and anterior rib bones using various image processing techniques, including warping and edge detection. The identified clavicle, posterior rib, and anterior rib bones may then be suppressed from the radiographic image to yield a segmented, bone suppressed radiographic image.

Lung segmentation and bone suppression techniques for radiographic images

A medical image processing apparatus which processes a medical image including a plurality of structures, the apparatus including: a memory in which a previous shape model including a set of contours of a specific structure based on an anatomical position is stored as previous information; and a hardware processor that: locates the previous shape model in the medical image; calculates, for the medical image, image feature amounts at positions which overlap the contours in the located previous shape model; and extracts contours of the specific structure by detecting candidate points of the specific structure based on the contours in the previous shape model and the image feature amounts.

Growing community of inventors

Kodaira, Japan

Shinsuke Katsuhara