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公开(公告)号：US11301977B2
公开(公告)日：2022-04-12
申请号：US16845324
申请日：2020-04-10
Applicant: General Electric Company
Inventor： Alberto Santamaria-Pang , Yousef Al-Kofahi , Aritra Chowdhury , Shourya Sarcar , Michael John MacDonald , Peter Arjan Wassenaar , Patrick Joseph Howard , Bruce Courtney Amm , Eric Seth Moderbacher
Abstract: An image inspection computing device is provided. The device includes a memory device and at least one processor. The at least one processor is configured to receive at least one sample image of a first component, wherein the at least one sample image of the first component does not include defects, store, in the memory, the at least one sample image, and receive an input image of a second component. The at least one processor is also configured to generate an encoded array based on the input image of the second component, perform a stochastic data sampling process on the encoded array, generate a decoded array, and generate a reconstructed image of the second component, derived from the stochastic data sampling process and the decoded array. The at least one processor is further configured to produce a residual image, and identify defects in the second component.
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公开(公告)号：US11407179B2
公开(公告)日：2022-08-09
申请号：US16359031
申请日：2019-03-20
Applicant: General Electric Company
Inventor： Joanna Mechelle Jayawickrema , Thomas Spears , Yousef Al-Kofahi , Ali Can
IPC: B29C64/393 , B29C64/153 , G06F30/00 , B33Y10/00 , B33Y50/02
Abstract: A system monitoring an additive manufacturing (AM) machine recoat operation includes an automatic defect recognition subsystem having a predictive model catalog each applicable to a product and to one recoat error indication having a domain dependent feature, the predicative models representative of a recoat error indication appearance at a pixel level of an image captured during recoat operations. The system includes an online monitoring subsystem having an image classifier unit that classifies recoat error indications at the pixel level based on predictive models selected on their metadata, a virtual depiction unit that creates a virtual depiction of an ongoing AM build from successive captured image, and a processor unit to monitor the build for recoat error indications, classify a detected indication, and provide a determination regarding the severity of the detected indication on the ongoing build. A method and a non-transitory computer-readable medium are also disclosed.
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公开(公告)号：US20210319544A1
公开(公告)日：2021-10-14
申请号：US16845324
申请日：2020-04-10
Applicant: General Electric Company
Inventor： Alberto Santamaria-Pang , Yousef Al-Kofahi , Aritra Chowdhury , Shourya Sarcar , Michael John MacDonald , Peter Arjan Wassenaar , Patrick Joseph Howard , Bruce Courtney Amm , Eric Seth Moderbacher
Abstract: An image inspection computing device is provided. The device includes a memory device and at least one processor. The at least one processor is configured to receive at least one sample image of a first component, wherein the at least one sample image of the first component does not include defects, store, in the memory, the at least one sample image, and receive an input image of a second component. The at least one processor is also configured to generate an encoded array based on the input image of the second component, perform a stochastic data sampling process on the encoded array, generate a decoded array, and generate a reconstructed image of the second component, derived from the stochastic data sampling process and the decoded array. The at least one processor is further configured to produce a residual image, and identify defects in the second component.
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Patent Agency Ranking
公开(公告)号：US10019796B2
公开(公告)日：2018-07-10
申请号：US15270061
申请日：2016-09-20
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Yousef Al-Kofahi , Anup Sood , Fiona Ginty , Qing Li , Christopher James Sevinsky , Alberto Santamaria-Pang
CPC classification number: G06T7/0012 , G06T7/12 , G06T7/143 , G06T7/174 , G06T7/181 , G06T7/194 , G06T11/008 , G06T11/60 , G06T2207/10004 , G06T2207/10056 , G06T2207/10064 , G06T2207/20076 , G06T2207/20081 , G06T2207/30024 , G06T2207/30101 , G06T2207/30172 , G06T2207/30204 , G06T2211/404
Abstract: Disclosed are novel computer-implemented methods for creating a blood vessel map of a biological tissue. The methods comprise the steps of, accessing image data corresponding to multi-channel multiplexed image of a fluorescently stained biological tissue manifesting expression levels of a primary marker and at least one auxiliary marker of blood vasculature, and extracting features of blood vessels using the primary marker as an input to create a single channel segmentation of the blood vessels. The method further comprises the steps of extracting features of blood vessels using the auxiliary marker to create auxiliary channels as a second input and apply multi-channel blood vessel enhancement. Blood vessel maps are created using the features and tracing the blood vasculature by iteratively extending the centerlines of the initial segmentation using statistical models and geometric rules.
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5.发明申请SYSTEMS AND METHODS FOR USING AN IMMUNOSTAINING MASK TO SELECTIVELY REFINE ISH ANALYSIS RESULTS 有权使用免疫屏蔽的系统和方法选择性地改进ISH分析结果公开(公告)号：US20140153811A1
公开(公告)日：2014-06-05
申请号：US13693406
申请日：2012-12-04
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Antti Seppo , Yousef Al-Kofahi , Dirk R. Padfield
IPC: G06T7/00
CPC classification number: G06T7/0012 , G06K9/0014 , G06K9/00147 , G06T7/0014 , G06T2207/10064 , G06T2207/30024 , G06T2207/30072
Abstract: A computer-implemented method of processing image data representing biological units in a tissue sample includes receiving a first image of the tissue sample containing signals from an immunofluorescent (IF) morphological marker, wherein the tissue sample is stained with the IF morphological marker, and receiving a second image of the same tissue sample containing signals from a fluorescent probe, wherein the tissue sample is hybridized in situ with the fluorescent probe. The method further includes classifying each biological unit in the tissue sample into one of at least two classes based on a mean intensity of the signals from the IF morphological marker in the first image, performing a fluorescence in situ hybridization (FISH) analysis of the tissue sample in the second image to obtain results therefrom, and filtering the results of the FISH analysis to produce a subset of the results pertaining to biological units classified in one class.
Abstract translation: 处理组织样本中表示生物单元的图像数据的计算机实现方法包括接收含有来自免疫荧光（IF）形态学标志的信号的组织样本的第一图像，其中组织样本用IF形态学标记染色，并接收 包含来自荧光探针的信号的相同组织样本的第二图像，其中组织样本与荧光探针原位杂交。 该方法还包括基于来自第一图像中的IF形态标志物的信号的平均强度将组织样本中的每个生物单元分类为至少两个类别中的一个，对组织进行荧光原位杂交（FISH）分析 在第二个图像中采样以获得结果，并且过滤FISH分析的结果以产生归类于一个类别中的生物单元的结果的子集。
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公开(公告)号：US20190147215A1
公开(公告)日：2019-05-16
申请号：US15814511
申请日：2017-11-16
Applicant: General Electric Company
Inventor： Yousef Al-Kofahi , Mirabela Rusu
CPC classification number: G06K9/0014 , G06K9/00134 , G06T7/0012 , G06T7/0014 , G06T7/11 , G06T7/136 , G06T7/143 , G06T7/194 , G06T2207/10056 , G06T2207/10064 , G06T2207/20021 , G06T2207/20076 , G06T2207/20081 , G06T2207/20152 , G06T2207/20156 , G06T2207/30024
Abstract: The present disclosure relates to a computer-implemented system and its associated method for single channel whole cell segmentation of a sample image of a biological sample. The biological sample may be stained with one or more non-nuclear cell marker stains, and the system and the method are configured to transform the sample image of the biological sample stained with the one or more non-nuclear cell marker stains into a segmented image having one or more cells with delineated nuclei and cytoplasm regions.
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公开(公告)号：US20170109880A1
公开(公告)日：2017-04-20
申请号：US15270061
申请日：2016-09-20
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Yousef Al-Kofahi , Anup Sood , Fiona Ginty , Qing Li , Christopher James Sevinsky , Alberto Santamaria-Pang
IPC: G06T7/00
CPC classification number: G06T7/0012 , G06T7/12 , G06T7/143 , G06T7/174 , G06T7/181 , G06T7/194 , G06T11/008 , G06T11/60 , G06T2207/10004 , G06T2207/10056 , G06T2207/10064 , G06T2207/20076 , G06T2207/20081 , G06T2207/30024 , G06T2207/30101 , G06T2207/30172 , G06T2207/30204 , G06T2211/404
Abstract: Disclosed are novel computer-implemented methods for creating a blood vessel map of a biological tissue. The methods comprise the steps of, accessing image data corresponding to multi-channel multiplexed image of a fluorescently stained biological tissue manifesting expression levels of a primary marker and at least one auxiliary marker of blood vasculature, and extracting features of blood vessels using the primary marker as an input to create a single channel segmentation of the blood vessels. The method further comprises the steps of extracting features of blood vessels using the auxiliary marker to create auxiliary channels as a second input and apply multi-channel blood vessel enhancement. Blood vessel maps are created using the features and tracing the blood vasculature by iteratively extending the centerlines of the initial segmentation using statistical models and geometric rules
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8.发明授权Systems and methods for using an immunostaining mask to selectively refine ISH analysis results 有权使用免疫染色掩模的系统和方法来选择性地改进ISH分析结果公开(公告)号：US09135694B2
公开(公告)日：2015-09-15
申请号：US13693406
申请日：2012-12-04
Applicant: General Electric Company
Inventor： Antti Seppo , Yousef Al-Kofahi , Dirk R. Padfield
CPC classification number: G06T7/0012 , G06K9/0014 , G06K9/00147 , G06T7/0014 , G06T2207/10064 , G06T2207/30024 , G06T2207/30072
Abstract: A computer-implemented method of processing image data representing biological units in a tissue sample includes receiving a first image of the tissue sample containing signals from an immunofluorescent (IF) morphological marker, wherein the tissue sample is stained with the IF morphological marker, and receiving a second image of the same tissue sample containing signals from a fluorescent probe, wherein the tissue sample is hybridized in situ with the fluorescent probe. The method further includes classifying each biological unit in the tissue sample into one of at least two classes based on a mean intensity of the signals from the IF morphological marker in the first image, performing a fluorescence in situ hybridization (FISH) analysis of the tissue sample in the second image to obtain results therefrom, and filtering the results of the FISH analysis to produce a subset of the results pertaining to biological units classified in one class.
Abstract translation: 处理组织样本中表示生物单元的图像数据的计算机实现方法包括接收含有来自免疫荧光（IF）形态学标志的信号的组织样本的第一图像，其中组织样本用IF形态学标记染色，并接收 包含来自荧光探针的信号的相同组织样本的第二图像，其中组织样本与荧光探针原位杂交。 该方法还包括基于来自第一图像中的IF形态标志物的信号的平均强度将组织样本中的每个生物单元分类为至少两个类别中的一个，对组织进行荧光原位杂交（FISH）分析 在第二个图像中采样以获得结果，并且过滤FISH分析的结果以产生归类于一个类别中的生物单元的结果的子集。
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