公开(公告)号：US09414798B2

公开(公告)日：2016-08-16

申请号：US12729274

申请日：2010-03-23

申请人： Ute Feuerlein ,  Arun Krishnan ,  Xiang Sean Zhou

发明人： Ute Feuerlein ,  Arun Krishnan ,  Xiang Sean Zhou

IPC分类号： A61B6/00 ,  A61B6/03 ,  A61B5/00

CPC分类号： A61B6/504 ,  A61B5/7285 ,  A61B6/032 ,  A61B6/469 ,  A61B6/481 ,  A61B6/488 ,  A61B6/541

摘要： A method for bolus tracking includes acquiring one or more baseline images. One or more trigger regions are automatically established within the baseline images. A bolus is administered. The automatically established trigger regions are monitored for bolus arrival at the one or more trigger regions. Bolus arrival at a volume of interest is forecasted based on the bolus arrival at the one or more trigger regions. A diagnostic scan of the volume of interest is acquired at the forecasted time.

摘要翻译： 用于推注跟踪的方法包括获取一个或多个基线图像。 在基线图像内自动建立一个或多个触发区域。 施用推注。 监视自动建立的触发区域，以便推动到达一个或多个触发区域。 基于在一个或多个触发区域的推迟到达来预测到达感兴趣的体积。 在预测时间内获取感兴趣体积的诊断扫描。

公开(公告)号：US08494235B2

公开(公告)日：2013-07-23

申请号：US12121865

申请日：2008-05-16

申请人： Senthil Periaswamy ,  Xiang Zhou ,  Yiqiang Zhan ,  Arun Krishnan

发明人： Senthil Periaswamy ,  Xiang Zhou ,  Yiqiang Zhan ,  Arun Krishnan

IPC分类号： G06K9/00

CPC分类号： G06T7/0012 ,  G06T7/62 ,  G06T7/68 ,  G06T2207/30101

摘要： A method for detecting lymph nodes in a medical image includes receiving image data. One or more regions of interest are detected from within the received image data. One or more lymph node candidates are identified using a set of predefined parameters that is particular to the detected region of interest where each lymph node candidate is located. The identifying unit may identify the one or more lymph node candidates by performing DGFR processing. The method may also include receiving user-provided adjustments to the predefined parameters that are particular to the detected regions of interest and identifying the lymph node candidates based on the adjusted parameters. The lymph node candidates identified based on the adjusted parameters may be displayed along with the image data in real-time as the adjustments are provided.

摘要翻译： 用于检测医学图像中的淋巴结的方法包括接收图像数据。 从所接收的图像数据中检测一个或多个感兴趣的区域。 使用一组预定义的参数来识别一个或多个淋巴结候选物，该组预定参数对于检测到的每个淋巴结候选者所在的感兴趣区域是特别的。 识别单元可以通过执行DGFR处理来识别一个或多个淋巴结候选。 该方法还可以包括接收用户提供的对所检测到的感兴趣区域特有的预定义参数的调整，并且基于经调整的参数来识别淋巴结候选。 随着提供调整，可以实时地显示基于调整后的参数识别的淋巴结候选以及图像数据。

公开(公告)号：US20120303687A1

公开(公告)日：2012-11-29

申请号：US13571257

申请日：2012-08-09

申请人： Alain Dazzi ,  Arun Krishnan

发明人： Alain Dazzi ,  Arun Krishnan

IPC分类号： G06F17/30

CPC分类号： G06F16/00

摘要： A method allowing for fast mapping between a content identifier (ID) for a media content file and the file path of said media content file includes: dividing a plurality of characters making up the contend ID into a plurality of ordered subsections separated by a separator character, correlating at least one of the subsections with a directory in an electronic file system, and correlating the last of the subsections with a name of the media content file, the media content file being located within a directory correlated with a preceding subsection.

摘要翻译： 允许在媒体内容文件的内容标识符（ID）和所述媒体内容文件的文件路径之间进行快速映射的方法包括：将构成竞争ID的多个字符分成由分隔符分隔的多个有序子部分 将至少一个子部分与电子文件系统中的目录相关联，并且将最后的子部分与媒体内容文件的名称相关联，所述媒体内容文件位于与前一小节相关的目录内。

公开(公告)号：US07995809B2

公开(公告)日：2011-08-09

申请号：US11396854

申请日：2006-04-03

申请人： Kazunori Okada ,  Visvanathan Ramesh ,  Arun Krishnan ,  Maneesh K. Singh ,  Umut Akdemir

发明人： Kazunori Okada ,  Visvanathan Ramesh ,  Arun Krishnan ,  Maneesh K. Singh ,  Umut Akdemir

IPC分类号： G06K9/00

CPC分类号： G06T7/155 ,  G06T7/12 ,  G06T7/149 ,  G06T2207/10081 ,  G06T2207/20008 ,  G06T2207/20116 ,  G06T2207/30064

摘要： By testing for nodule segmentation errors based on the scan data, juxtapleural cases are identified. Once identified, the scan data or subsequent estimation may be altered to account for adjacent rib, tissue, vessel or other structure effecting segmentation. One alteration is to shape a filter as a function of the scan data. For example, an originally estimated ellipsoid for the nodule segmentation defines the filter. The filter is used to identify the undesired information, and masking removes the undesired information for subsequent estimation of the nodule segmentation. Another possible alteration biases the subsequent estimation away from the incorrect information, such as the rib, tissue or vessel information influencing the original estimation. For example, a negative prior or probability is assigned to data corresponding to the originally estimated segmentation for the subsequent estimation.

摘要翻译： 通过基于扫描数据测试结节分割错误，确定并列案例。 一旦被识别，可以改变扫描数据或随后的估计，以考虑影响分割的相邻肋，组织，血管或其他结构。 一个改变是根据扫描数据来形成一个过滤器。 例如，用于结节分段的原始估计的椭圆体定义了滤波器。 滤波器用于识别不期望的信息，并且掩蔽去除了不期望的信息以用于随后估计结节分割。 另一种可能的改变偏离了不正确信息的后续估计，例如影响原始估计的肋骨，组织或血管信息。 例如，将负的先前或概率分配给对应于用于后续估计的最初估计的分割的数据。

公开(公告)号：US20110142320A1

公开(公告)日：2011-06-16

申请号：US12968492

申请日：2010-12-15

申请人： Alok Gupta ,  Arun Krishnan ,  Xiang Sean Zhou

发明人： Alok Gupta ,  Arun Krishnan ,  Xiang Sean Zhou

IPC分类号： G06K9/00

CPC分类号： G06T7/0012 ,  G06F19/00 ,  G06F19/321 ,  G06T7/41 ,  G06T2207/10081 ,  G06T2207/10104 ,  G06T2207/20101 ,  G06T2207/30004 ,  G16H50/20

摘要： A system for providing automatic diagnosis and decision support includes: a medical image database; generative learning and modeling modules that build distributional appearance models and spatial relational models of organs or structures using images from the medical image database; a statistical whole-body atlas that includes one or more distributional appearance models and spatial relational models of organs or structure, in one or more whole-body imaging modalities, built by the generative learning and modeling modules; and discriminative learning and modeling modules that build two-class or multi-class classifiers for performing at least one of organ, structure or disease detection or segmentation.

摘要翻译： 一种用于提供自动诊断和决策支持的系统包括：医学图像数据库; 生成学习和建模模块，使用医学图像数据库中的图像构建器官或结构的分布外观模型和空间关系模型; 统计全身图集，包括一个或多个分布式外观模型和器官或结构的空间关系模型，在一个或多个全身成像模式中，由生成学习和建模模块构建; 以及构建用于执行器官，结构或疾病检测或分割中的至少一种的两类或多类分类器的辨别性学习和建模模块。

公开(公告)号：US07876938B2

公开(公告)日：2011-01-25

申请号：US11542477

申请日：2006-10-03

申请人： Xiaolei Huang ,  Xiang Zhou ,  Anna Jerebko ,  Arun Krishnan ,  Haiying Guan ,  Toshiro Kubota ,  Vaclav Potesil

发明人： Xiaolei Huang ,  Xiang Zhou ,  Anna Jerebko ,  Arun Krishnan ,  Haiying Guan ,  Toshiro Kubota ,  Vaclav Potesil

IPC分类号： G06K9/00 ,  G06K9/62

CPC分类号： G06T7/174 ,  G06T7/155 ,  G06T7/33 ,  G06T7/38 ,  G06T2207/10072 ,  G06T2207/20016 ,  G06T2207/20081 ,  G06T2207/20164 ,  G06T2207/30004 ,  G06T2207/30204

摘要： A method for segmenting digitized images includes providing a training set comprising a plurality of digitized whole-body images, providing labels on anatomical landmarks in each image of said training set, aligning each said training set image, generating positive and negative training examples for each landmark by cropping the aligned training volumes into one or more cropping windows of different spatial scales, and using said positive and negative examples to train a detector for each landmark at one or more spatial scales ranging from a coarse resolution to a fine resolution, wherein the spatial relationship between a cropping windows of a coarse resolution detector and a fine resolution detector is recorded.

摘要翻译： 用于分割数字化图像的方法包括提供包括多个数字化的全身图像的训练集合，在所述训练集合的每个图像中的解剖学标志上提供标签，对准每个所述训练集合图像，为每个界标生成正和负训练样本 通过将对准的训练量裁剪成不同空间尺度的一个或多个裁剪窗口，并且使用所述正和负的示例来在从粗分辨率到精细分辨率的一个或多个空间尺度上训练每个地标的检测器，其中空间 记录粗分辨率检测器的裁切窗与精细分辨率检测器之间的关系。

公开(公告)号：US07840046B2

公开(公告)日：2010-11-23

申请号：US11767707

申请日：2007-06-25

申请人： Anna Jerebko ,  Arun Krishnan ,  Xiang Zhou

发明人： Anna Jerebko ,  Arun Krishnan ,  Xiang Zhou

IPC分类号： G06K9/00 ,  A61B6/04 ,  A61B5/05

CPC分类号： A61B6/466 ,  A61B6/03 ,  A61B6/502 ,  G06T7/0012 ,  G06T7/97 ,  G06T11/005 ,  G06T2207/10112 ,  G06T2207/10116 ,  G06T2207/30068 ,  G06T2211/436

摘要： A method of detecting breast masses and calcifications in digitized images, includes providing a plurality of 2-dimensional (2D) digital X-ray projectional breast images acquired from different viewing angles, extracting candidate lesions and 2D features from said 2D projectional images, computing spicularity characteristics of said candidate lesions, including location, periodicity, and amplitude, applying learning algorithms to said candidate lesions to predict a probability of malignancy of said lesion, receiving from said learning algorithm a probability map of detections for each breast image, said detections comprising associating pixels with a probability of being associated with a malignancy, creating a synthetic 2D slice for each X-ray image wherein malignant regions are indicated by ellipses on a non-malignant background, and constructing a synthetic 3-dimensional (3D) image volume from said 2D synthetic slices.

摘要翻译： 一种在数字化图像中检测乳房肿块和钙化的方法，包括提供从不同视角获取的多个二维（2D）数字X射线投射乳房图像，从所述2D投影图像中提取候选病变和2D特征，计算孢子 所述候选病变的特征，包括位置，周期和幅度，将学习算法应用于所述候选病变以预测所述病变的恶性的概率，从所述学习算法接收每个乳腺图像的检测的概率图，所述检测包括关联 具有与恶性肿瘤相关联的概率的像素，为每个X射线图像创建合成2D切片，其中恶性区域由非恶性背景上的椭圆表示，以及从所述非恶性背景构建合成的三维（3D）图像体积 2D合成切片。

公开(公告)号：US07792778B2

公开(公告)日：2010-09-07

申请号：US11782689

申请日：2007-07-25

申请人： Xiang Zhou ,  Alok Gupta ,  Arun Krishnan

发明人： Xiang Zhou ,  Alok Gupta ,  Arun Krishnan

IPC分类号： G06N5/00 ,  G06K9/32 ,  G06Q50/00 ,  G06Q19/00 ,  G06F15/16

CPC分类号： G06Q50/24 ,  G06F19/00 ,  G06F19/321 ,  G06Q50/22 ,  G16H50/20

摘要： A system for knowledge-based image computer aided detection includes a text interpretation system receiving an electronic patient record and outputting an assertion relevant for the electronic patient record, an annotation/detection system detects anatomical and functional structures in input images and interacts with the text interpretation system to receive the assertion and outputting annotated images based on the input images, and an imaging decision support system taking the annotated images and outputting classifications of annotated structures in the annotated images.

摘要翻译： 用于基于知识的图像计算机辅助检测的系统包括接收电子病人记录并输出与电子病人记录相关的断言的文本解释系统，注释/检测系统检测输入图像中的解剖学和功能结构并与文本解释相互作用 系统，用于基于输入图像接收断言并输出注释图像，以及成像决策支持系统，其采用注释图像并输出注释图像中注释结构的分类。

公开(公告)号：US07724930B2

公开(公告)日：2010-05-25

申请号：US11590451

申请日：2006-10-31

申请人： Xiang Zhou ,  Arun Krishnan ,  Alok Gupta ,  Vaclav Potesil

发明人： Xiang Zhou ,  Arun Krishnan ,  Alok Gupta ,  Vaclav Potesil

IPC分类号： G06K9/00 ,  A61B6/00

CPC分类号： G06T7/0012 ,  G06T7/38 ,  G06T2207/30004

摘要： A method of automatic change quantification for medical decision support includes: automatically detecting a structure in a set of medical images; characterizing the detected structure including modeling of deformation characteristics of the detected structure; matching images based on the characterization of the detected structure, wherein a size measure of the detected structure is constrained according to the deformation characteristics; and quantifying a change in the detected structure.

摘要翻译： 用于医疗决策支持的自动变化量化的方法包括：自动检测一组医学图像中的结构; 表征检测到的结构，包括检测结构的变形特性的建模; 基于检测结构的表征匹配图像，其中检测结构的尺寸测量根据变形特性被约束; 并量化检测结构的变化。

公开(公告)号：US07616799B2

公开(公告)日：2009-11-10

申请号：US11146788

申请日：2005-06-07

申请人： Venkat Raghavan Ramamurthy ,  Arun Krishnan ,  Christian Beldinger ,  Juergen Soldner ,  Maxim Mamin ,  Axel Barth ,  Stefan Käpplinger ,  Michael Gluth ,  Peggy Hawman ,  Darrell Burckhardt ,  Axel Platz

发明人： Venkat Raghavan Ramamurthy ,  Arun Krishnan ,  Christian Beldinger ,  Juergen Soldner ,  Maxim Mamin ,  Axel Barth ,  Stefan Käpplinger ,  Michael Gluth ,  Peggy Hawman ,  Darrell Burckhardt ,  Axel Platz

IPC分类号： G06K9/36

CPC分类号： G06T7/0012 ,  G06T7/38 ,  G06T2207/30004 ,  G06T2219/2012

摘要： A system and method for monitoring disease progression or response to therapy using multi-modal visualization are provided. The method comprises: selecting a first image dataset of a first timepoint; loading the first image dataset of the first timepoint; selecting a second image dataset of a second timepoint; loading the second image dataset of the second timepoint; registering the first image dataset of the first timepoint and the second image dataset of the second timepoint; and displaying the first image dataset of the first timepoint and the second image dataset of the second timepoint.

摘要翻译： 提供了使用多模式可视化来监测疾病进展或对治疗的反应的系统和方法。 该方法包括：选择第一时间点的第一图像数据集; 加载第一个时间点的第一个图像数据集; 选择第二时间点的第二图像数据集; 加载第二个时间点的第二个图像数据集; 注册第一时间点的第一图像数据集和第二时间点的第二图像数据集; 以及显示第二时间点的第一时间点和第二图像数据集的第一图像数据集。