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公开(公告)号:US20230394673A1
公开(公告)日:2023-12-07
申请号:US18453319
申请日:2023-08-22
Applicant: FUJIFILM Corporation
Inventor: Satoshi IHARA
IPC: G06T7/12 , G06T5/30 , G06T7/13 , G06V10/44 , G06V10/764
CPC classification number: G06T7/12 , G06T5/30 , G06V2201/03 , G06V10/44 , G06V10/764 , G06T7/13
Abstract: A processor uses a semantic segmentation model that has been trained using an annotation image in which a first pixel corresponding to at least any one of one point corresponding to an object, a plurality of discrete points corresponding to a plurality of objects, or a line corresponding to an object having a line structure is set as a first pixel value and a second pixel other than the first pixel is set as a second pixel value different from the first pixel value, the model having been trained by assigning a greater weight to the first pixel than to the second pixel to calculate a loss, inputs an image to the model and outputs a feature amount map having a feature amount related to the one point, etc. in the image from the model, and identifies the one point, etc. in the image based on the map.
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公开(公告)号:US20160019694A1
公开(公告)日:2016-01-21
申请号:US14867210
申请日:2015-09-28
Applicant: FUJIFILM Corporation
Inventor: Satoshi IHARA
CPC classification number: G06T7/0012 , A61B6/032 , G06K9/4604 , G06T5/30 , G06T7/12 , G06T2207/10004 , G06T2207/10081 , G06T2207/30028
Abstract: An apparatus includes a gaseous region extraction unit that extracts a gaseous region from a lumen image, a residue candidate region extraction unit that extracts a candidate of a residue region from the lumen image as a residue candidate region, a boundary candidate region detection unit that detects a boundary candidate region that includes a boundary between the gaseous region and the residue candidate region, a representative direction component obtaining unit that obtains a representative direction component representing a plurality of directional components of an image in the boundary candidate region, a boundary region detection unit that detects a boundary region that includes a boundary between the gaseous region and the residue region from the boundary candidate regions based on the representative direction component, and a residue region extraction unit that extracts the residue candidate region that includes the boundary region as the residue region.
Abstract translation: 一种装置,包括从管腔图像提取气体区域的气体区域提取单元,从管腔图像中提取残留区域的候补的残差候选区域提取单元作为残差候选区域,边界候补区域检测单元,其检测 包括气体区域和残留候选区域之间的边界的边界候补区域,获取表示边界候补区域中的图像的多个方向分量的代表方向分量的代表方向分量获取单元,边界区域检测单元 基于代表方向分量,从边界候补区域检测包括气体区域和残留区域之间的边界的边界区域,以及残留区域提取单元,其提取包含边界区域的残差候选区域作为残留区域 。
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公开(公告)号:US20130243285A1
公开(公告)日:2013-09-19
申请号:US13874594
申请日:2013-05-01
Applicant: FUJIFILM CORPORATION
Inventor: Caihua WANG , Keigo NAKAMURA , Satoshi IHARA
IPC: G06T7/00
CPC classification number: G06T7/0012 , A61B6/03 , A61B6/463 , A61B6/505 , A61B6/5217 , A61B6/5223 , G06T7/33 , G06T2207/10072 , G06T2207/30012
Abstract: Generating, with respect to each of the three-dimensional image and the three-dimensional comparison image, a plurality of tomographic images orthogonal to a central axis of each vertebra of the subject along the central axis, calculating a first characteristic amount representing a profile in a direction orthogonal to the central axis at each point on the central axis based on the tomographic images, calculating a second characteristic amount representing a profile in a direction of the central axis at each point on the central axis based on the tomographic images, calculating a third characteristic amount representing regularity of disposition of each vertebra at each point on the central axis based on the calculated first and second characteristic amounts, and aligning positions of the third characteristic amount calculated from the three-dimensional image and the third characteristic amount calculated from the three-dimensional comparison image along the central axis.
Abstract translation: 相对于三维图像和三维比较图像中的每一个生成沿着中心轴与被检体的每个椎骨的中心轴正交的多个断层图像,计算表示轮廓的第一特征量 基于断层图像在中心轴的每个点处与中心轴正交的方向,基于断层图像计算表示在中心轴上的每个点的中心轴方向上的轮廓的第二特征量,计算 第三特征量,表示基于计算出的第一和第二特征量在中心轴上的每个点处的每个椎骨的布置的规律性,以及从三维图像计算的第三特征量和从第三特征量计算的第三特征量的位置 沿中心轴的三维比较图像。
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4.
公开(公告)号:US20230306608A1
公开(公告)日:2023-09-28
申请号:US18327027
申请日:2023-05-31
Applicant: FUJIFILM Corporation
Inventor: Satoshi IHARA
CPC classification number: G06T7/11 , G06T7/0012 , G06T3/40 , G06T2207/20084 , G06T2207/30056
Abstract: A processor is configured to: reduce a target image to derive a reduced image; extract a region of a target structure from the reduced image to derive a reduced structure image including the region of the target structure; extract a corresponding image corresponding to the reduced structure image from the target image; and input the corresponding image and the reduced structure image into an extraction model constructed by machine-learning a neural network to extract a region of the target structure included in the corresponding image from the extraction model.
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公开(公告)号:US20230394661A1
公开(公告)日:2023-12-07
申请号:US18453320
申请日:2023-08-22
Applicant: FUJIFILM Corporation
Inventor: Satoshi IHARA
CPC classification number: G06T7/0012 , G06T7/11 , G06T7/70 , G16H30/40 , G16H50/20 , G06T2207/20081 , G06T2207/30012 , G06T2207/30204 , G06T2207/10081
Abstract: An image processing apparatus comprising: a processor and a memory connected to or incorporated in the processor, in which the processor acquires an analysis target image in which a plurality of contiguous target objects of the same type appear, receives an input of a marker indicating positions of the target objects in the analysis target image, generates a marker position display map indicating a position of the marker in the analysis target image, inputs the analysis target image and the marker position display map to a semantic segmentation model, and outputs, from the semantic segmentation model, an output image in which the target objects are identified.
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公开(公告)号:US20220382544A1
公开(公告)日:2022-12-01
申请号:US17732537
申请日:2022-04-29
Applicant: FUJIFILM Corporation
Inventor: Satoshi IHARA
Abstract: A processor determines an exponent common to a plurality of numerical values, determines a mantissa for each of the plurality of numerical values based on the determined exponent, and performs four arithmetic operations using a sign, the determined exponent, and the determined mantissa.
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公开(公告)号:US20170287130A1
公开(公告)日:2017-10-05
申请号:US15473099
申请日:2017-03-29
Applicant: FUJIFILM Corporation
Inventor: Satoshi IHARA
IPC: G06T7/00
CPC classification number: G06T7/0012 , G06K9/00973 , G06T2207/10081
Abstract: Plural pieces of data where N (N>2) elements are arranged in a predetermined-direction in a specific-order are sorted into any one of N labels using a graph-cut-process. Each of the plural pieces of data has scores indicating element-likenesses for the plural respective elements. For each piece of data, weights are set about links along a first-direction directing from a node s to a node t so that a small weight is given to a link corresponding to an element having a maximum-score in the data. A weight for regulating cutting is set about links along a second-direction opposite to the first-direction and links along a direction in which the order of the respective pieces of data progresses. A graph-cut-process is executed on a graph for which the weights are set to determine links to be cut, and the N labels are allocated to the plural pieces of data.
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公开(公告)号:US20230306556A1
公开(公告)日:2023-09-28
申请号:US18325015
申请日:2023-05-29
Applicant: FUJIFILM Corporation
Inventor: Satoshi IHARA
CPC classification number: G06T3/4007 , G06T7/11 , G06T2207/20084
Abstract: A processor is configured to convert a size of a target image to derive a size-converted image, segment the size-converted image into regions of at least one class by using a segmentation model constructed by machine-learning a neural network to derive a plurality of class images in which a pixel value of each pixel represents class-likeness for the at least one class, convert a size of at least one class image into the size of the target image to derive at least one converted class image, and segment the target image based on a pixel value in each pixel of the at least one converted class image.
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公开(公告)号:US20150030226A1
公开(公告)日:2015-01-29
申请号:US14337889
申请日:2014-07-22
Applicant: FUJIFILM Corporation
Inventor: Satoshi IHARA
CPC classification number: G06T3/0068 , G06T7/11 , G06T7/162 , G06T7/187 , G06T2207/10081 , G06T2207/10088 , G06T2207/20036 , G06T2207/20072 , G06T2207/20101 , G06T2207/20128 , G06T2207/30028
Abstract: A first-image and a second-image representing the same organ of the same subject imaged at the same time are obtained, and an organ-region is extracted from the first-image. The extracted organ-region is displayed on a display screen. An input of an air-region included in the first-image and an input of exceeding or lacking portion information representing that the air-region is a lacking portion or an exceeding portion of the organ are received, and the received air-region and the received exceeding or lacking portion information corresponding to the air-region are obtained, as correction information. Corresponding positions are matched with other between the first-image and the second-image. The organ-region extracted from the first-image is corrected based on the correction information. At least an air-region in the second-image located at a position corresponding to the air-region corresponding to the correction information is extracted, as a part of the organ-region in the second-image.
Abstract translation: 获得表示同时成像的同一被摄体的同一器官的第一图像和第二图像,并从第一图像提取器官区域。 提取的器官区域显示在显示屏幕上。 接收包括在第一图像中的空域的输入和表示空域是器官的缺少部分或超出部分的超过或缺少部分信息的输入,并且接收到的空气区域和 获得与空域相对应的超过或不足的部分信息作为校正信息。 对应的位置与第一图像和第二图像之间的其他位置匹配。 基于校正信息来校正从第一图像提取的器官区域。 作为第二图像中的器官区域的一部分,提取位于对应于与校正信息对应的空气区域的位置的第二图像中的至少一个空气区域。
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公开(公告)号:US20240037927A1
公开(公告)日:2024-02-01
申请号:US18483532
申请日:2023-10-10
Applicant: FUJIFILM CORPORATION
Inventor: Satoshi IHARA
IPC: G06V10/82 , G06V10/764
CPC classification number: G06V10/82 , G06V10/764
Abstract: A processor is configured to: acquire training data that consists of a learning expression medium and a correct answer label for at least one of a plurality of types of classes included in the learning expression medium; input the learning expression medium to a neural network such that probabilities that each class included in the learning expression medium will be each of the plurality of types of classes are output; integrate the probabilities that each class will be each of the plurality of types of classes on the basis of classes classified by the correct answer label of the training data; and train the neural network on the basis of a loss derived from the integrated probability and the correct answer label of the training data.
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