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公开(公告)号：US20250005748A1
公开(公告)日：2025-01-02
申请号：US18709536
申请日：2022-11-17
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Axel Saalbach , Nicole Schadewaldt , Matthias Lenga , Heinrich Schulz
Abstract: A computing system (122) includes a memory (130) with instructions (132) including a digitally reconstructed radiograph view optimization instruction (134), a processor (128) configured to execute the digitally reconstructed radiograph view optimization instruction to generate a plurality of digitally reconstructed radiographs based on a plurality of different sets of projection parameters and three-dimensional computed tomography image data and to identify an optimal sub-set of the plurality of digitally reconstructed radiographs for reading for the reason for acquiring the three-dimensional computed tomography image data, and an output device (126) configured to display the identified optimal sub-set of the plurality of digitally reconstructed radiographs for reading.
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公开(公告)号：US20230368386A1
公开(公告)日：2023-11-16
申请号：US18027931
申请日：2021-09-10
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Karsten Sommer , Matthias Lenga , Axel Saalbach
CPC classification number: G06T7/0014 , G16H30/40 , G06V10/761 , G06F16/532 , G06V10/82 , G06V10/464 , G06T2207/20081 , G06T2207/10088 , G06V2201/03 , G06T2207/30168 , G06T2207/10108 , G06T2207/10121 , G06T2207/10081 , G06T2207/30004 , G06T2207/20084 , G06T2207/10104 , G06T2207/10132
Abstract: Disclosed herein is a medical system comprising a memory storing machine executable instructions and at least one trained neural network. Each of the at least one neural network is configured for receiving a medical image as input. Each of the at least one trained neural network has been modified to provide hidden layer output. Execution of the machine executable instructions causes the computational system to: receive the medical image; receive the hidden layer output in response to inputting the medical image into each of the at least one trained neural network; provide an anonymized image fingerprint comprising the hidden layer output from each of the at least one trained neural network; and receive an image assessment of the medical image in response to querying a historical image database using the anonymized image fingerprint.
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公开(公告)号：US11657500B2
公开(公告)日：2023-05-23
申请号：US16955959
申请日：2018-12-14
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Rafael Wiemker , Tanja Nordhoff , Thomas Buelow , Axel Saalbach , Tobias Klinder , Tom Brosch , Tim Philipp Harder , Karsten Sommer
CPC classification number: G06T7/0012 , A61N5/1031 , G06T5/002 , G06T7/11 , G06T2207/10081 , G06T2207/20021 , G06T2207/20182 , G06T2207/30064 , G06T2207/30168
Abstract: The invention relates to a system for assessing a pulmonary image which allows for an improved assessment with respect to lung nodules detectability. The pulmonary image is smoothed for providing different pulmonary images (20, 21, 22) with different degrees of smoothing, wherein signal values and noise values, which are indicative of the lung vessel detectability and the noise in these images, are determined and used for determining an image quality being indicative of the usability of the pulmonary image to be assessed for detecting lung nodules. Since a pulmonary image shows lung vessels with many different vessel sizes and with many different image values, which cover the respective ranges of potential lung nodules generally very well, the image quality determination based on the different pulmonary images with different degrees of smoothing allows for a reliable assessment of the pulmonary image's usability for detecting lung nodules. The image quality is used to determine a radiation dose level to be applied for generating a next pulmonary image.
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公开(公告)号：US11320508B2
公开(公告)日：2022-05-03
申请号：US16759778
申请日：2018-10-22
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Karsten Sommer , Tom Brosch , Tim Philipp Harder , Jochen Keupp , Ingmar Graesslin , Rafael Wiemker , Axel Saalbach
Abstract: The invention relates to a magnetic resonance imaging data processing system (126) for processing motion artifacts in magnetic resonance imaging data sets using a deep learning network (146, 502, 702) trained for the processing of motion artifacts in magnetic resonance imaging data sets. The magnetic resonance imaging data processing system (126) comprises a memory (134, 136) storing machine executable instructions (161, 164) and the trained deep learning network (146, 502, 702). Furthermore, the magnetic resonance imaging data processing system (126) comprises a processor (130) for controlling the magnetic resonance imaging data processing system. Execution of the machine executable instructions (161, 164) causes the processor (130) to control the magnetic resonance imaging data processing system (126) to: receive a magnetic resonance imaging data set (144, 500, 800), apply the received magnetic resonance imaging data set (144, 500, 800) as an input to the trained deep learning network (146, 502, 702), process one or more motion artifacts present in the received magnetic resonance imaging data set (144, 500, 800) using the trained deep learning network (146, 502, 702).
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公开(公告)号：US11295451B2
公开(公告)日：2022-04-05
申请号：US16319701
申请日：2017-07-26
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Rafael Wiemker , Tobias Klinder , Alexander Schmidt-Richberg , Axel Saalbach , Irina Waechter-Stehle , Tim Philipp Harder , Jens von Berg
Abstract: An image processing system and related method. The system comprises an input interface (IN) configured for receiving an n[≥2]-dimensional input image with a set of anchor points defined in same, said set of anchor points forming an input constellation. A constellation modifier (CM) is configured to modify said input constellation into a modified constellation. A constellation evaluator (CE) configured to evaluate said input constellation based on said hyper-surface to produce a score. A comparator (COMP) is configured to compare said score against a quality criterion. Through an output interface (OUT) said constellation is output if the score meets said criterion. The constellation suitable to define a segmentation for said input image.
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Patent Agency Ranking
公开(公告)号：US11183293B2
公开(公告)日：2021-11-23
申请号：US15524322
申请日：2015-10-22
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Kongkuo Lu , Alexandra Groth , Yuechen Qian , Axel Saalbach , Ranjith Naveen Tellis , Daniel Bystrov , Ran Cohen , Bela Fadida , Lior Wolloch
Abstract: A system (100) for detecting and labeling structures of interest includes a current patient study database (102) containing a current patient study (200) with clinical contextual information (706), a statistical model patient report database (104) containing at least one or more prior patient documents containing clinical contextual information (706), an image metadata processing engine (118) configured to extract metadata for preparing an input for an anatomical structure classifier (608), a natural language processing engine (120) configured to extract clinical context information (706) from the prior patient documents, an anatomical structure detection and labeling engine (718) or processor (112), and a display device (108) configured to display findings from the current patient study. The anatomical structure detection and labeling engine (718) or processor (112) is configured to identify and label one or more structures of interest (716) from the extracted metadata and clinical context information (706) and aggregate series level data.
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公开(公告)号：US20210295524A1
公开(公告)日：2021-09-23
申请号：US16319701
申请日：2017-07-26
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Rafael Wiemker , Tobias Klinder , Alexander Schmidt-Richberg , Axel Saalbach , Irina Waechter-Stehle , Tim Philipp Harder , Jens von Berg
Abstract: An image processing system and related method. The system comprises an input interface (IN) configured for receiving an n[≥2]-dimensional input image with a set of anchor points defined in same, said set of anchor points forming an input constellation. A constellation modifier (CM) is configured to modify said input constellation into a modified constellation. A constellation evaluator (CE) configured to evaluate said input constellation based on said hyper-surface to produce a score. A comparator (COMP) is configured to compare said score against a quality criterion. Through an output interface (OUT) said constellation is output if the score meets said criterion. The constellation suitable to define a segmentation for said input image.
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公开(公告)号：US10984294B2
公开(公告)日：2021-04-20
申请号：US16463390
申请日：2017-12-01
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Rafael Wiemker , Tobias Klinder , Axel Saalbach , Jens Von Berg
Abstract: The invention relates to an apparatus for identifying a candidate object in image data and determining a likelihood that the candidate object is an object from an object class. The apparatus comprises an image data receiving unit for receiving image data of an object of the object class, a seed element selecting unit for selecting a portion of the image elements as seed elements, a contour point identifying unit for identifying, for each seed element (SE), contour points, the contour points of a seed element circumscribing a candidate object which comprises the seed element, and a seed score determining unit for determining, for each seed element, a seed score indicative of a likelihood that the candidate object is an object from the object class. The invention allows differentiation between an object of an object class of interest and artifacts.
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公开(公告)号：US20150026643A1
公开(公告)日：2015-01-22
申请号：US14345023
申请日：2012-09-17
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Juergen Weese , Irina Wächter-Stehle , Axel Saalbach
IPC: G06F3/0482 , G06F3/0484 , G06T7/00
CPC classification number: G06F3/0482 , G06F3/04842 , G06F19/00 , G06F19/321 , G06T7/0014 , G06T2207/20104 , G06T2207/30004 , G16H40/63
Abstract: System (100) for enabling an interactive inspection of a region of interest (122) in a medical image (102), the system comprising display means (160) for displaying user interface elements (310, 320, 330) of actions associated with the interactive inspection of the region of interest and a processor (180) for executing one of the actions when a user selects an associated one of the user interface elements, the system further comprising establishing means (120) for establishing the region of interest in the medical image, determining means (140) for determining an anatomical property (142) of the region of interest in dependence on an image property of the region of interest, and the display means (160) being arranged for (i), in dependence on the anatomical property, establishing a display configuration (162) of the user interface elements, and (ii) displaying the user interface elements in accordance with the display configuration.
Abstract translation: 系统（100），用于实现医疗图像（102）中的感兴趣区域（122）的交互式检查，所述系统包括显示装置（160），用于显示与所述医疗图像相关联的动作的用户界面元素（310,320,330） 感兴趣区域的交互式检查和用于当用户选择相关联的一个用户界面元素时执行其中一个动作的处理器（180），该系统还包括建立装置（120），用于在医疗中建立感兴趣的区域 图像，确定装置，用于根据感兴趣区域的图像属性来确定感兴趣区域的解剖特性（142），并且显示装置（160）被布置为：（i）根据 解剖属性，建立用户界面元素的显示配置（162），以及（ii）根据显示配置显示用户界面元素。
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公开(公告)号：US12131525B2
公开(公告)日：2024-10-29
申请号：US17620142
申请日：2020-06-25
Applicant: KONINKLIJKE PHILIPS N.V.
Inventor： Alexandra Groth , Axel Saalbach , Ivo Matteo Baltruschat , Jens Von Berg , Michael Grass
IPC: G06V10/00 , G06T7/00 , G06V10/44 , G06V10/764 , G06V10/774 , G06V10/82 , G06V10/96 , G06V20/70
CPC classification number: G06V10/82 , G06T7/0012 , G06V10/454 , G06V10/764 , G06V10/774 , G06V10/96 , G06V20/70 , G06T2207/10081 , G06T2207/10124 , G06T2207/20081 , G06T2207/20084 , G06V2201/03
Abstract: Multi-task deep learning method for a neural network for automatic pathology detection, comprising the steps: receiving first image data (I) for a first image recognition task; receiving (S2) second image data (V) for a second image recognition task; wherein the first image data (I) is of a first datatype and the second image data (V) is of a second datatype, different from the first datatype; determining (S3) first labeled image data (IL) by labeling the first image data (I) and determining second synthesized labeled image data (ISL) by synthesizing and labeling the second image data (V); training (S4) the neural network based on the received first image data (I), the received second image data (V), the determined first labeled image data (IL) and the determined second labeled synthesized image data (ISL); wherein the first image recognition task and the second image recognition task relate to a same anatomic region where the respective image data is taken from and/or relate to a same pathology to be recognized in the respective image data.
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