Kabushiki Kaisha Toshiba

Toshiba Medical Systems Corporation

Toshiba Energy Systems & Solutions Corporation

National Institutes for Quantum and Radiological Science and Technology

Canon Medical Systems Corporation

National Institute of Radiological Sciences

National Institutes for Quantum Science and Technology

Yukinobu Sakata

Ryusuke Hirai

Yasunori Taguchi

Tomoyuki Takeguchi

Akiyuki Tanizawa

Kyoka Sugiura

Shuhei Nitta

Shinichiro Mori

Takeshi Mita

Keiko Okaya

Atsushi Yaguchi

Tomoya Okazaki

Taichiro Shiodera

Shigehide Kuhara

Fumi Maruyama

Nobuyuki Matsumoto

Yasuhiko Abe

Wataru Asano

Toshiyuki Ono

Yasuko Fujisawa

Tomoki Watanabe

Saori Asaka

Shogo Fukuda

Yasutoyo Takeyama

Satoshi Kawata

Takamasa Sugiura

Koji Ando

Yurika Ogawa

A medical image processing device of an embodiment includes first and second image acquirers, a generator, and a calculator. The first image acquirer acquires a first fluoroscopic image of a patient. The second image acquirer acquires a second fluoroscopic image according to radiation of the patient at a time point different from that of acquisition of the first image using a detector. The generator generates a reconstructed image by reproducing the second image from the first image virtually arranged in a three-dimensional space on the basis of a position of the detector in the three-dimensional space. The calculator obtains a suitable position on the first image in the three-dimensional space on the basis of a degree of similarity between the second image and the reconstructed image. The generator generates the reconstructed image for use in the calculator and has a range larger than a range corresponding to the second image.

Medical image processing device, storage medium, medical device, and treatment system

According to one embodiment, a learning apparatus includes a setting unit, a training unit, and a display. The setting unit sets one or more second training conditions based on a first training condition relating to a first trained model. The training unit trains one or more neural networks in accordance with the one or more second training conditions and generates one or more second trained models which execute a task identical to a task executed by the first trained model. The display displays a graph showing an inference performance and calculation cost of each of the one or more second trained models.

Learning apparatus, method, and non-transitory computer readable medium

A medical image processing device of an embodiment includes a comparator and a position determination region determiner. The comparator compares a first image obtained by photographing a patient with a comparative image which is an image used in previous radiation treatment and in which a valid region used in position alignment in the radiation treatment is designated. The position determination region determiner determines a position determination region similar to the valid region included within the first image on the basis of a comparison result of the comparator.

According to one embodiment, a defect classification apparatus includes a processor. The processor acquires a first design image which is an image based on design data created by design software and relates to a first inspection target, and acquires a first real image which is captured by imaging the first inspection target produced based on the design data. The processor converts the first design image to a reference image represented by using a second real image captured by imaging a second inspection target without a defect. The processor calculates a reliability of the reference image. The processor detects a defect in the first inspection target by comparing the reference image and the first real image and classifies the defect, based on the reliability.

Defect classification apparatus, method and program

According to one embodiment, a defect inspection apparatus includes a storage and a processor. The storage stores dictionary in which a first design image generated by software is associated with a first real image corresponding to the first design image. The processor acquires a second design image generated by the software. The processor searches for a similar first design image similar to the second design image. The processor generates a reference image that is a pseudo real image of a second inspection object, based on the second design data and free of defect, by using the first real image associated with the similar first design image. The first design image includes information of an image area wider than the first real image.

Defect inspection apparatus, method and program

A treatment system of embodiments includes an imaging system including one or more radiation sources and a plurality of detectors, a first acquirer, a second acquirer, a first deriver, a second deriver, and a calibrator. The radiation sources radiate radiation to an object in a plurality of different directions. The plurality of detectors detect the radiation at different positions. The first acquirer acquires images based on the radiation. The second acquirer acquires position information of a first imaging device in a three-dimensional space. The first deriver derives the position of the object in the images. The second deriver derives the position of a second imaging device in the three-dimensional space based on the position of the object in the images, the position of the first imaging device, and the like. The calibrator performs calibration of the imaging system based on a derivation result of the second deriver.

Treatment system, calibration method, and storage medium

A learning device includes a structure search unit that searches for a first learned model structure obtained by selecting search space information in accordance with a target constraint condition of target hardware for each of a plurality of convolution processing blocks included in a base model structure in a neural network model; a parameter search unit that searches for a learning parameter of the neural network model in accordance with the target constraint condition; and a pruning unit that deletes a unit of at least one of the plurality of convolution processing blocks in the first learned model structure based on the target constraint condition and generates a second learned model structure.

Learning device, learning system, and learning method

A robot system according to an embodiment includes one or more processors. The processors acquire first input data predetermined as data affecting an operation of a robot. The processors calculate a calculation cost of inference processing using a machine learning model for inferring control data used for controlling the robot, on the basis of the first input data. The processors infer the control data by the machine learning model set according to the calculation cost. The processors control the robot using the inferred control data.

Robot system and driving method

A learning device according to an embodiment includes one or more hardware processors configured to function as a generation unit, an inference unit, and a training unit. The generation unit generates input data with which an error between a value output from each of one or more target nodes and a preset aimed value is equal to or less than a preset value, the target nodes being in a target layer of a plurality of layers included in a first neural network. The inference unit causes the input data to propagate in a forward direction of the first neural network to generate output data. The training unit trains a second neural network differing from the first neural network by using training data including a set of the input data and the output data.

Learning device, learning method, and computer program product

A providing apparatus according to an embodiment of the present disclosure includes a memory and a hardware processor coupled to the memory. The hardware processor is configured to: store, in the memory, a first machine learning model capable of changing an amount of calculation of a model of a neural network; acquire device information; set, based on the device information, extraction conditions representing conditions for extracting second machine learning models from the first machine learning model; extract the second machine learning models from the first machine learning model based on the extraction conditions; and provide the second machine learning models to a device specified by the device information.

Providing apparatus, providing method, and computer program product

A defect inspection device includes at least one memory storing instructions and at least one processor. The at least one processor is configured to execute the instructions to acquire a first image of an inspection target created in a first creation method, acquire a second image obtained by photographing the inspection target, extract index data similar to the acquired first image with reference to a database, the index data being a third image created in the first creation method or a feature quantity obtained by the third image, the database including the index data associated with correct data that is used as a comparison target of the index data and is an image determined not to be defective in previous inspection, acquire the correct data associated with the extracted index data in the database, generate a reference image on the basis of the acquired correct data, and estimate a pixel associated with a defective position on the inspection target photographed in the second image by comparing the reference image with the second image.

Defect inspection device, defect inspection method, and storage medium

An object positioning apparatus comprising: a radiographic image input interface configured to acquire a radiographic image that is generated by causing a fluoroscopic imaging apparatus to image an object and includes a first region and a second region, the first region depicting an index region for positioning of the object, the second region depicting a non-index region other than the index region; and a positioning processor configured to perform the positioning of the object by performing matching processing between a previously generated reference image and the first region that is specified from the radiographic image based on three-dimensional model information of the non-index region.

Object positioning apparatus, object positioning method, object positioning program, and radiation therapy system

A magnetic resonance imaging apparatus according to an embodiment includes a processor and a memory. The memory stores processor-executable instructions that cause the processor to detect cross-sectional positions of a plurality of cross-sectional images to be acquired in an imaging scan from volume data; acquire the cross-sectional images in sequence based on the cross-sectional positions by executing the imaging scan; and after the first cross-sectional image is acquired in the imaging scan, generate a display image, and display the display image on a display, the display image being an image in which a cross-sectional position of a second cross-sectional image which is detected from the volume data is superimposed on the first cross-sectional image, the second cross-sectional image being a cross-sectional image before being acquired and intersecting with the first cross-sectional image.

Magnetic resonance imaging apparatus

A medical image processing apparatus comprising: a first input interface configured to acquire a three-dimensional volume image of an object which is provided with at least one marker, the three-dimensional volume image being generated by imaging the object using a medical examination apparatus; a second input interface configured to acquire geometry information of an imaging apparatus which is used for imaging the object to generate a fluoroscopic image of the object; and a specific-setting-information generator configured to generate specific setting information based on the three-dimensional volume image and the geometry information, the specific setting information being used for setting of imaging for generating an image depicting the at least one marker or setting of image processing of the image depicting the at least one marker.

Medical image processing apparatus, medical image processing method, computer-readable medical-image processing program, moving-object tracking apparatus, and radiation therapy system

A medical diagnostic apparatus according to one embodiment comprises processing circuitry. The processing circuitry is configured to acquire a three-dimensional shape of a first part based on a contour of the first part in each of a plurality of sectional images intersecting each other along an extending direction of the first part connecting to a cardiac chamber; acquire a three-dimensional shape of a second part, based on a contour of the second part in a sectional image along an extending direction of the second part connecting to the cardiac chamber; and generate a three-dimensional image representing at least some of the first part, the second part, and the cardiac chamber using a three-dimensional shape representing the cardiac chamber and the three-dimensional shapes of the first part and the second part acquired.

Growing community of inventors

Kawasaki, Japan

Yukinobu Sakata