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    CONSECUTIVE APPROXIMATION CALCULATION METHOD, CONSECUTIVE APPROXIMATION CALCULATION DEVICE, AND PROGRAM
    2.
    发明申请
    CONSECUTIVE APPROXIMATION CALCULATION METHOD, CONSECUTIVE APPROXIMATION CALCULATION DEVICE, AND PROGRAM 有权

    公开(公告)号：US20220253508A1

    公开(公告)日：2022-08-11

    申请号：US17294181

    申请日：2019-11-14

    Applicant: SHIMADZU CORPORATION

    Inventor： Yusuke TAGAWA ,  Akira NODA ,  Wataru TAKAHASHI ,  Tetsuya KOBAYASHI

    IPC: G06F17/17 ,  G06T11/00

    Abstract: A computer calculates interference fringe phase estimated value data (30) of a phase-restored object image by performing iterative approximation calculation using interference fringe intensity data (10) measured by a digital holography apparatus and interference fringe phase initial value data (20), which is an estimated initial phase value of the image of the object. The interference fringe phase initial value data (20) is calculated by an initial phase estimator (300). The initial phase estimator (300) is constructed by implementing machine learning using interference fringe intensity data and the like for learning. The computer acquires reconfigured intensity data (40) and reconfigured phase data (50) by performing optical wave propagation calculation using the interference fringe phase estimation value data (30) of the image of the object acquired through phase restoration, and the interference fringe intensity data (10) used as input data for the initial phase estimator (300). This provides an iterative approximation calculation method and the like capable of making an initial value of a solution used in the iterative approximation calculation method a value close to the true value.

    TRACKING DEVICE FOR RADIATION TREATMENT, POSITION DETECTION DEVICE, AND METHOD FOR TRACKING MOVING BODY
    3.
    发明申请
    TRACKING DEVICE FOR RADIATION TREATMENT, POSITION DETECTION DEVICE, AND METHOD FOR TRACKING MOVING BODY 审中-公开

    公开(公告)号：US20200155870A1

    公开(公告)日：2020-05-21

    申请号：US16627148

    申请日：2017-12-27

    Applicant: Shimadzu Corporation

    Inventor： Wataru TAKAHASHI ,  Shota OSHIKAWA

    IPC: A61N5/10 ,  A61B6/12

    Abstract: A control part 30 includes: a DRR image creation part 31 that creates a DRR image including a specific site; a specific site projection part 32 that creates a projection region image representing the region of the specific site; a discriminator learning part 33 that learns a discriminator for recognizing the region of the specific site by performing machine learning with use of the DRR image and the projection region image as a training label image; a specific site region detection part 34 that detects the region of the specific site by performing discrimination using the discriminator learned by the discriminator learning part 33 on an X-ray fluoroscopic image including the specific site; and a radiation signal generation part 35 that transmits a treatment beam radiation signal to an irradiation device 90 when the region of the specific site detected by the specific site region detection part 34 is included in the radiation region of a treatment beam.

    RADIATION FLUOROSCOPY APPARATUS
    4.
    发明申请
    RADIATION FLUOROSCOPY APPARATUS 审中-公开

    公开(公告)号：US20190240510A1

    公开(公告)日：2019-08-08

    申请号：US15887617

    申请日：2018-02-02

    Applicant: SHIMADZU CORPORATION

    Inventor： Michel DARGIS ,  Frederic HUDON ,  Wataru TAKAHASHI ,  Kodai NAGAE

    IPC: A61N5/10 ,  A61B6/00

    Abstract: A radiation fluoroscopy apparatus detects a marker and includes a control element, an image generation element 61 that generates an image including an embedded marker inside the body of the subject based on a transmitted X-ray. A device candidate detection element 62 detects the candidate of the marker, the local structure detection element 63 detects the local structure in the target region in a proximity of the candidate point of the marker, the device determination element 64 determines whether the local structure is the device such as, the marker or not, the device location acquisition element 66 acquires the gravity center coordinate of the local structure, and the device tracking element 67 tracks the marker based on the location of the marker in each frame.

    X-RAY FLUOROSCOPIC APPARATUS
    5.
    发明申请
    X-RAY FLUOROSCOPIC APPARATUS 审中-公开

    公开(公告)号：US20180154180A1

    公开(公告)日：2018-06-07

    申请号：US15497263

    申请日：2017-04-26

    Applicant: SHIMADZU CORPORATION

    Inventor： Shinichiro MORI ,  Wataru TAKAHASHI

    IPC: A61N5/10 ,  A61B6/00 ,  G21K4/00

    CPC classification number: A61N5/1049 ,  A61B6/4266 ,  A61B6/487 ,  A61N5/1068 ,  A61N2005/1061 ,  A61N2005/1062 ,  G21K4/00

    Abstract: An X-ray fluoroscopic apparatus is capable of irradiating exactly a therapeutic beam considering a specific-regional area, and in addition is able to perform an easy confirmation of the specific region even when the specific region is difficult to visually recognize by a user. A control element 30 has a DRR image generation element 31, an X-ray fluoroscopic radiograph generation element 32, a template area selection element 33, a template generation element 34, a position detection element 35, a radiation area projection element 36, a specific region projection element 37, a superimposition element 38, an image display element 39, a gating element 40 and a memory storing element 41 that stores a variety of data including image data.

    MACHINE LEARNING METHOD, MACHINE LEARNING DEVICE, AND MACHINE LEARNING PROGRAM
    6.
    发明申请
    MACHINE LEARNING METHOD, MACHINE LEARNING DEVICE, AND MACHINE LEARNING PROGRAM 有权

    公开(公告)号：US20210358129A1

    公开(公告)日：2021-11-18

    申请号：US17041364

    申请日：2018-06-28

    Applicant: Shimadzu Corporation

    Inventor： Wataru TAKAHASHI ,  Shota OSHIKAWA

    IPC: G06T7/11 ,  G06N3/02 ,  G06N20/00

    Abstract: A full-size training image is reduced by an image reduction unit (11) and input to an FCN (Fully Convolutional Neural Network) computation unit (13), and the FCN computation unit (13) performs calculation under a set filter coefficient and outputs a reduced label image. The reduced label image is enlarged to a full size by an image enlargement unit (14), the error calculation unit (15) calculates an error between the enlarged label image and a full-size ground truth based on a loss function, and the parameter update unit (16) updates a filter coefficient depending on the error. By repeating learning under the control of the learning control unit 17, it is possible to generate a learning model for performing optimal segmentation including an error generated at the time of image enlargement. Further, by including the image enlargement processing in the learning model, a full-size label image can be output, and the accuracy evaluation of the model can also be performed with high accuracy.

    CELLULAR IMAGE ANALYSIS METHOD, CELLULAR IMAGE ANALYSIS DEVICE, AND LEARNING MODEL CREATION METHOD
    7.
    发明申请
    CELLULAR IMAGE ANALYSIS METHOD, CELLULAR IMAGE ANALYSIS DEVICE, AND LEARNING MODEL CREATION METHOD 有权

    公开(公告)号：US20210133963A1

    公开(公告)日：2021-05-06

    申请号：US16976680

    申请日：2018-03-08

    Applicant: Shimadzu Corporation

    Inventor： Wataru TAKAHASHI ,  Ayako AKAZAWA,

    IPC: G06T7/00 ,  G06K9/00 ,  G06T7/11

    Abstract: A phase image is formed by calculation from a hologram image of a cell, and segmentation is performed for each pixel for the phase image using a fully convolution neural network to identify an undifferentiated cell region, a deviated cell region, a foreign substance region, and the like. When learning, when a learning image included in a mini-batch is read, the image is randomly inverted vertically or horizontally and then is rotated by a random angle. A part that has been lost within the frame by the pre-rotation image is compensated for by a mirror-image inversion with an edge of a post-rotation image as an axis thereof. Learning of a fully convolution neural network is performed using the generated learning image. The same processing is repeated for all mini-batches, and the learning is repeated by a predetermined number of times while shuffling the training data allocated to the mini-batch. The precision of the learning model is thus improved. In addition, since rotationally invariant characteristics can be learned, it is possible to identify cell colonies of various shapes with good precision.

    Cell Image Analysis Method and Cell Image Analyzer
    9.
    发明申请
    Cell Image Analysis Method and Cell Image Analyzer 有权

    公开(公告)号：US20220148282A1

    公开(公告)日：2022-05-12

    申请号：US17578179

    申请日：2022-01-18

    Applicant: SHIMADZU CORPORATION

    Inventor： Wataru TAKAHASHI

    IPC: G06V10/22 ,  G06V20/69 ,  G06V10/46 ,  G06V10/774

    Abstract: A cell image analysis method includes converting a first image into a label image by performing a segmentation process to identify a region of a cell that has already started differentiation and a region of an undifferentiated cell in the first image, acquiring a shape feature amount from the label image, and determining whether or not a cell colony includes a colony region that is a candidate for a search target based on the shape feature amount and a determination criterion.

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