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

IPC: G06T7/00 ,  G06K9/62

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.

公开(公告)号：US10740651B2

公开(公告)日：2020-08-11

申请号：US15796379

申请日：2017-10-27

Applicant: General Electric Company

Inventor： Alberto Santamaria-Pang ,  Daniel Eugene Meyer ,  Michael Ernest Marino ,  Qing Li ,  Dmitry V. Dylov ,  Aritra Chowdhury

IPC: G06K9/00 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G09B23/30 ,  G06T7/00 ,  G16H50/20 ,  G06K9/44 ,  G06T7/11 ,  G06T7/155 ,  G16H50/50

Abstract: The present approach relates to the use of trained artificial neural networks, such as convolutional neural networks, to classify vascular structures, such as using a hierarchical classification scheme. In certain approaches, the artificial neural network is trained using training data that is all or partly derived from synthetic vascular representations.

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

IPC: G06T7/00 ,  G06T11/60

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.

公开(公告)号：US08995740B2

公开(公告)日：2015-03-31

申请号：US13865036

申请日：2013-04-17

Applicant: General Electric Company

Inventor： Alberto Santamaria-Pang ,  Jens Rittscher ,  Dirk Padfield ,  Ali Can ,  Zhengyu Pang ,  Musodiq Bello ,  Fiona Ginty ,  Christopher Sevinsky ,  Qing Li ,  Megan Rothney ,  Brion Sarachan

IPC: G06K9/00 ,  G06T7/00 ,  G01N33/574

CPC classification number: G06K9/0014 ,  G06T7/0012 ,  G06T2207/10064 ,  G06T2207/30024

Abstract: Improved systems and methods for the analysis of digital images are provided. More particularly, the present disclosure provides for improved systems and methods for the analysis of digital images of biological tissue samples. Exemplary embodiments provide for: i) segmenting, ii) grouping, and iii) quantifying molecular protein profiles of individual cells in terms of sub cellular compartments (nuclei, membrane, and cytoplasm). The systems and methods of the present disclosure advantageously perform tissue segmentation at the sub-cellular level to facilitate analyzing, grouping and quantifying protein expression profiles of tissue in tissue sections globally and/or locally. Performing local-global tissue analysis and protein quantification advantageously enables correlation of spatial and molecular configuration of cells with molecular information of different types of cancer.

Abstract translation: 提供了改进的数字图像分析系统和方法。 更具体地，本公开提供了用于分析生物组织样本的数字图像的改进的系统和方法。 示例性实施方案提供：i）分段，ii）分组，和iii）根据亚细胞区室（细胞核，膜和细胞质）量化各细胞的分子蛋白质谱。 本公开的系统和方法有利地在亚细胞水平进行组织分割，以便于全局和/或局部地分析，分组和定量组织部位中组织的蛋白质表达谱。 进行局部全局组织分析和蛋白质定量有利于使细胞的空间和分子构型与不同类型癌症的分子信息的相关性。

公开(公告)号：US20140112557A1

公开(公告)日：2014-04-24

申请号：US13657255

申请日：2012-10-22

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Alberto Santamaria-Pang ,  Yuchi Huang ,  Jens Rittscher

IPC: G06K9/34

CPC classification number: G06K9/34 ,  G06K9/00147 ,  G06T7/0012 ,  G06T7/12 ,  G06T2207/10056 ,  G06T2207/20016 ,  G06T2207/20036 ,  G06T2207/20072 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20152 ,  G06T2207/30024

Abstract: A method of segmenting a digital image of biological tissue includes accessing a ranking model calculated from training data representing shapes of conforming and non-conforming biological unit exemplars. The ranking model may include support vectors defining a hyperplane in a vector space. The method further includes accessing image data representing the digital image, identifying a first shape and a set of second constituent shapes in the digital image, wherein the first shape comprises a union of the set of second constituent shapes, determining a rank of a first data point in the image data corresponding to the first shape and a rank of a second data point in the image data corresponding to the set of second constituent shapes into the vector space, and segmenting the digital image using the first shape or the set of second constituent shapes based on which data point has a greater respective rank.

Abstract translation: 分割生物组织的数字图像的方法包括访问由表示符合和不符合生物单元样本的形状的训练数据计算的排名模型。 排名模型可以包括在向量空间中定义超平面的支持向量。 该方法还包括访问表示数字图像的图像数据，识别数字图像中的第一形状和一组第二组成形状，其中第一形状包括该组第二组成形状的并集，确定第一数据的等级 将与第一形状相对应的图像数据和对应于该组第二构成形状的图像数据中的第二数据点的等级排列成向量空间，并且使用第一形状或一组第二成分分割数字图像 基于哪个数据点具有更大的相应等级的形状。