公开(公告)号：US20130290006A1

公开(公告)日：2013-10-31

申请号：US13459999

申请日：2012-04-30

Applicant: Vidya Pundalik Kamath ,  Colin Craig McCulloch ,  Megan Rothney ,  Alberto Santamaria-Pang ,  Brion Daryl Sarachan

Inventor： Vidya Pundalik Kamath ,  Colin Craig McCulloch ,  Megan Rothney ,  Alberto Santamaria-Pang ,  Brion Daryl Sarachan

IPC: G06Q50/22

CPC classification number: G06F19/345 ,  G06Q50/22 ,  G16H50/20

Abstract: Embodiments disclosed herein include methods, systems, and devices for determining a positive or negative correlation between a clinical outcome and one or more features of biological tissue. An exemplary user interface enables a user to select a clinical outcome and one or more aspects of a displayed field-of-view of biological tissue. Exemplary embodiments may automatically perform correlation analysis between the selected clinical outcome and one or more features characteristic of the user-selected aspects of the field-of-view of biological tissue. For example, exemplary embodiments may automatically perform correlation analysis between the selected clinical outcome and one or more features characteristic of one or more biological units in the biological tissue.

Abstract translation: 本文公开的实施方案包括用于确定临床结果与生物组织的一个或多个特征之间的正相关或负相关的方法，系统和装置。 示例性用户界面使得用户能够选择生物组织的显示视野的临床结果和一个或多个方面。 示例性实施例可以自动执行所选择的临床结果与生物组织的视野的用户选定方面特征的一个或多个特征之间的相关性分析。 例如，示例性实施例可以自动执行所选临床结果与生物组织中一个或多个生物单元特征的一个或多个特征之间的相关性分析。

公开(公告)号：US20120271553A1

公开(公告)日：2012-10-25

申请号：US13252080

申请日：2011-10-03

Applicant: Colin Craig McCulloch

Inventor： Colin Craig McCulloch

IPC: G06F19/10

CPC classification number: G06K9/00147 ,  G01N33/5091 ,  G01N33/57434 ,  G01N2800/52 ,  G01N2800/56 ,  G01N2800/60 ,  G06K9/6231

Abstract: A data set of cell profile data is stored. The cell profile data includes multiplexed biometric image data describing the expression of a plurality of biomarkers. Cell profile data is generated from tissue samples drawn from a cohort of patients having an assessment related to the commonality. Multiple sets of clusters of similar cells are generated from the data set; the proportion of cells in each cluster is examined for an association with a diagnosis, a prognosis, or a response; and a predictive set of clusters is selected based on model performance. One predictive set of clusters is selected based on a comparison of the performance of at least one model of the plurality of sets of clusters. Display techniques that aid in understanding the characteristics of a cluster are disclosed.

Abstract translation: 存储小区简档数据的数据集。 细胞谱图数据包括描述多个生物标志物的表达的多重生物统计图像数据。 从具有与共性相关的评估的患者队列中抽取的组织样品产生细胞谱图数据。 从数据集中生成多组类似的单元; 检查每个组中细胞的比例与诊断，预后或反应的关联; 并基于模型性能选择一组预测集群。 基于多组聚类中的至少一个模型的性能的比较来选择一组预测集群。 公开了有助于理解集群特征的显示技术。

公开(公告)号：US08873827B2

公开(公告)日：2014-10-28

申请号：US13539187

申请日：2012-06-29

Applicant: Colin Craig McCulloch ,  Christopher James Sevinsky ,  Jingyu Zhang

Inventor： Colin Craig McCulloch ,  Christopher James Sevinsky ,  Jingyu Zhang

IPC: G06K9/00

CPC classification number: G06T7/0012 ,  G06T2207/10056 ,  G06T2207/30024

Abstract: Exemplary embodiments enable determination of spatial proximity between two or more features in biological tissue. An exemplary method includes identifying a morphological feature in an image of the biological tissue based on expression levels of a first biomarker indicative of the morphological feature, and receiving a result of a segmentation analysis performed on the image of the biological tissue identifying a set of morphological units in the image external to the morphological feature. An exemplary method includes determining an expression level of a second biomarker corresponding to each unit in the set of morphological units in the image of the biological tissue, and determining a spatial distance between the morphological feature and each unit in the set of morphological units. An exemplary method further includes automatically determining a relationship between expression levels of the second biomarker and corresponding spatial distance from the morphological feature of the set of morphological units.

Abstract translation: 示例性实施例使得能够确定生物组织中两个或更多个特征之间的空间接近度。 一种示例性方法包括基于指示形态学特征的第一生物标志物的表达水平鉴定生物组织的图像中的形态学特征，以及接收对生物组织的图像执行的识别一组形态学的分割结果的结果 图像中的单位在形态特征之外。 一种示例性方法包括确定与生物组织的图像中的形态单元组中的每个单元相对应的第二生物标志物的表达水平，以及确定该形态单位组中的形态特征与每个单位之间的空间距离。 一种示例性方法还包括自动确定第二生物标志物的表达水平与相应的空间距离之间的关系，与该组形态学单元的形态学特征。

公开(公告)号：US20130226830A1

公开(公告)日：2013-08-29

申请号：US13407623

申请日：2012-02-28

Applicant: Kete Long ,  Colin Craig McCulloch ,  Sean Coleman Keenan

Inventor： Kete Long ,  Colin Craig McCulloch ,  Sean Coleman Keenan

IPC: G06Q40/06

CPC classification number: G06Q40/00

Abstract: Transactional risk and return analysis systems provided herein include a transaction database and a market database. The transaction database includes data regarding transactions with associated attributes and the market database includes market data. A portfolio model uses such data to estimate a risk prediction for each transaction. A risk prediction model is generated based on the portfolio model and estimates a risk prediction for a prospective transaction, and a case cash flow analyzer produces a risk-breakeven spread. A transaction evaluator uses the risk prediction model and the risk-breakeven spread to calculated transaction risk and return data for a prospective transaction.

Abstract translation: 本文提供的交易风险和回报分析系统包括交易数据库和市场数据库。 交易数据库包括关于具有关联属性的交易的数据，并且市场数据库包括市场数据。 投资组合模型使用这些数据来估计每笔交易的风险预测。 基于投资组合模型生成风险预测模型，并估计潜在交易的风险预测，案例现金流量分析仪产生风险承受偏差。 交易评估者使用风险预测模型和风险 - 盈亏平衡差来计算交易风险和预期交易的回报数据。

公开(公告)号：US20120269419A1

公开(公告)日：2012-10-25

申请号：US13252078

申请日：2011-10-03

Applicant: Colin Craig McCulloch

Inventor： Colin Craig McCulloch

IPC: G06K9/00

CPC classification number: G06K9/00147 ,  G01N33/5091 ,  G01N33/57434 ,  G01N2800/52 ,  G01N2800/56 ,  G01N2800/60 ,  G06K9/6231

Abstract: A stored data set comprises cell profile data including multiplexed biometric image data describing the expression of biomarkers. The data set includes an association of the cell profile data with a field of view level or a patient-level assessment. Cell features are calculated based on the cell's expression of each of the biomarkers. Moments of cell features are calculated from the data set, and combinations are examined for an association with an assessment. A predictive set of moments is selected based on the performance of the combination.

Abstract translation: 存储的数据集包括细胞轮廓数据，包括描述生物标志物的表达的复制的生物统计图像数据。 数据集包括细胞概况数据与视野级别或患者级评估的关联。 基于每个生物标志物的细胞表达计算细胞特征。 从数据集计算细胞特征的时刻，并检查组合与评估的关联。 基于组合的性能选择一组预测的时刻。

公开(公告)号：US20110270450A1

公开(公告)日：2011-11-03

申请号：US12895521

申请日：2010-09-30

Applicant: Vineel Chandrakanth Gujjar ,  Colin Craig McCulloch ,  Philippe Giguere ,  Abhishek Kumar Srivastava ,  Angshuman Saha ,  Pameet Singh

Inventor： Vineel Chandrakanth Gujjar ,  Colin Craig McCulloch ,  Philippe Giguere ,  Abhishek Kumar Srivastava ,  Angshuman Saha ,  Pameet Singh

IPC: G06F1/26 ,  H02P9/04

CPC classification number: G06Q10/00 ,  F03D17/00 ,  H02J3/386 ,  Y02E10/763

Abstract: The present disclosure relates to an approach by which low-performing turbines may be identified from among a plurality of wind turbines, such as may be present at a wind power plant. In accordance with one embodiment, low-performing turbines are identified from among pairs of turbines and based upon a comparison of the observed and expected performance of the turbines within each pair.

Abstract translation: 本公开涉及一种可以从多个风力涡轮机中识别低性能涡轮机的方法，例如可能存在于风力发电厂。 根据一个实施例，从成对的涡轮机中识别低性能涡轮机，并且基于每对中的涡轮机的观测和预期性能的比较。

公开(公告)号：US20120269418A1

公开(公告)日：2012-10-25

申请号：US13252072

申请日：2011-10-03

Applicant: Colin Craig McCulloch ,  Christopher J. Sevinsky ,  Fiona Ginty

Inventor： Colin Craig McCulloch ,  Christopher J. Sevinsky ,  Fiona Ginty

IPC: G06K9/00 ,  G01N33/577

CPC classification number: G06K9/00147 ,  G01N33/5091 ,  G01N33/57434 ,  G01N2800/52 ,  G01N2800/56 ,  G01N2800/60 ,  G06K9/6231

Abstract: A data set of cell profile data is stored. The cell profile data includes multiplexed biometric image data describing the expression of a plurality of biomarkers with respect to a field of view in which individual cells are delineated and segmenting into compartments. Each cell in the field is assigned to a single cluster in a selected set of clusters based on cell similarity; cell similarity is determined based on a plurality of selected cell attributes. The proportion of cells in each cluster is observed. The observed proportions are examined for an association with a diagnosis, a prognosis, or a response derived from a known association of the selected set of clusters with at least one piece of meta-information including a field of view level assessment or a patient-level assessment.

Abstract translation: 存储小区简档数据的数据集。 细胞谱图数据包括描述多个生物标志物相对于其中单个细胞被描绘和分割成隔室的视野的表达的多重生物统计图像数据。 基于小区相似性，将该字段中的每个小区分配给所选集群中的单个群集; 基于多个选择的小区属性来确定小区相似性。 观察每个簇中细胞的比例。 检查观察到的比例与来自所选择的集合集合的已知关联的诊断，预后或响应的关联与至少一个元信息（包括视野级别评估或患者级别） 评定。

公开(公告)号：US08111889B2

公开(公告)日：2012-02-07

申请号：US10813758

申请日：2004-03-31

Applicant: Samit Kumar Basu ,  Bruno Kristiaan Bernard De Man ,  Peter Michael Edic ,  Ricardo Scott Avila ,  James Vradenburg Miller ,  Colin Craig McCulloch ,  Deborah Joy Walter ,  Paulo Ricardo dos Santos Mendonca ,  William Macomber Leue ,  Thomas Baby Sebastian

Inventor： Samit Kumar Basu ,  Bruno Kristiaan Bernard De Man ,  Peter Michael Edic ,  Ricardo Scott Avila ,  James Vradenburg Miller ,  Colin Craig McCulloch ,  Deborah Joy Walter ,  Paulo Ricardo dos Santos Mendonca ,  William Macomber Leue ,  Thomas Baby Sebastian

IPC: G06K9/00

CPC classification number: G06T11/005 ,  G06T2211/421

Abstract: A technique is disclosed for generating variance data and a variance map from measured projection data acquired from a tomography system. The method comprises accessing the measured projection data from the tomography system. The method further comprises generating the variance map from the measured projection data and displaying, analyzing or processing the variance map. The variance data is determined based upon a statistical model from measured image data, and may be used for image analysis, data acquisition, in computer aided diagnosis routines, and so forth.

Abstract translation: 公开了用于从断层摄影系统获取的测量投影数据产生方差数据和方差图的技术。 该方法包括从断层摄影系统访问所测量的投影数据。 该方法还包括从所测量的投影数据生成方差图并显示，分析或处理方差图。 方差数据基于来自测量图像数据的统计模型确定，并且可以用于图像分析，数据采集，计算机辅助诊断例程等。

公开(公告)号：US20110091081A1

公开(公告)日：2011-04-21

申请号：US12580651

申请日：2009-10-16

Applicant: Brion Daryl Sarachan ,  Thomas Paul Repoff ,  Colin Craig McCulloch ,  Fiona Mary Ginty ,  Megan Pearl Rothney ,  Zhengyu Pang

Inventor： Brion Daryl Sarachan ,  Thomas Paul Repoff ,  Colin Craig McCulloch ,  Fiona Mary Ginty ,  Megan Pearl Rothney ,  Zhengyu Pang

IPC: G06K9/00 ,  C12Q1/02 ,  C12Q1/68 ,  G06F19/00

CPC classification number: C12Q1/6881 ,  G01N33/5023 ,  G01N33/5082 ,  G06T7/11 ,  G06T7/174 ,  G06T2207/10056 ,  G06T2207/20221 ,  G06T2207/30024

Abstract: The present application discloses a technique for obtaining and storing data on expression of multiple biomarkers in individual cells or the compartments of individual cells in a tissue specimen and methods of utilizing that data to create groups, the members of which share some degree of similarity greater than the general population from which the data is drawn, by an analysis of digital images of a portion of the tissue specimen which has been iteratively stained to generate optical signals, typically fluorescent, reflective of the amount of each of the biomarkers examined. The analysis of the images involves a segmentation routine whereby each pixel of the examined images is assigned to an individual cell or a compartment of an individual cell, the intensity of the signal representative of each biomarker is measured for each pixel, a dataset is created in which each cell or compartments of each cell is associated with a signal intensity for each biomarker examined, and the dataset is interrogated with appropriate numerical tools to create groups. It also discloses the display of such groups on images of the tissue examined such that the individual cells belonging to a particular group are marked or indicated on one of the images examined. It further discloses examining the biomarker data for each group for possible association with a biological condition or process in cases in which tissue specimens drawn from different subjects or different portions of the tissue of a subject have been examined. It also discloses using this examination in the diagnoses, prognoses or assessment of the response to therapy of a condition or disease.

Abstract translation: 本申请公开了一种用于获得和存储组织样本中个体细胞或个体细胞的多个生物标志物表达的数据的技术，以及利用该数据创建组的方法，其成员具有一定程度的相似性，大于 通过对经过迭代染色以产生光信号的组织标本的一部分的数字图像的分析，通常是荧光的，反映了所检查的每个生物标志物的量，绘制数据的一般群体。 图像的分析涉及分割程序，其中检查的图像的每个像素被分配给单个单元的单个单元或隔室，针对每个像素测量代表每个生物标志物的信号的强度，数据集被创建在 每个细胞的每个细胞或隔室与所检测的每个生物标志物的信号强度相关联，并且用适当的数值工具询问数据集以产生组。 它还公开了在所检查的组织的图像上显示这样的组，使得属于特定组的各个细胞在所检查的图像之一上被标记或指示。 它还公开了在从受试者的不同受试者或组织的不同部分抽取的组织标本已经被检查的情况下，检查每组的生物标志物数据与生物学条件或过程的可能关联。 它还公开了在对病症或疾病的治疗反应的诊断，预后或评估中使用该检查。

公开(公告)号：US07058210B2

公开(公告)日：2006-06-06

申请号：US09683111

申请日：2001-11-20

Applicant: Joseph Leagrand Mundy ,  Colin Craig McCulloch ,  Ricardo Scott Avila ,  Shannon Lee Hastings ,  Robert August Kaucic, Jr. ,  William Edward Lorensen ,  Matthew William Turek

Inventor： Joseph Leagrand Mundy ,  Colin Craig McCulloch ,  Ricardo Scott Avila ,  Shannon Lee Hastings ,  Robert August Kaucic, Jr. ,  William Edward Lorensen ,  Matthew William Turek

IPC: G06K9/00

CPC classification number: G06T7/0012 ,  A61B6/00 ,  G06T2207/30061

Abstract: A method for processing medical images for use in the detection and diagnosis of disease comprises classifying regions of interest within the medical images based on a hierarchy of anatomical models and signal models of signal information of an image acquisition device used to acquire the medical images. The anatomical models are derived to be representative of anatomical information indicative of a given disease. A computer-aided system for use in the diagnosis and detection of disease comprises an image acquisition device for acquiring a plurality of image data sets and a processor adapted to process the image data sets. The processor is adapted to classify selected tissue types within the image data sets based on a hierarchy of signal and anatomical models and the processor is further adapted to differentiate anatomical context of the classified tissue types for use in the diagnosis and detection of disease.