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1. 发明申请

US20090310836A1 Automatic Learning of Image Features to Predict Disease 有权
标题翻译：自动学习图像特征预测疾病
公开(公告)号：US20090310836A1
公开(公告)日：2009-12-17
申请号：US12427974
申请日：2009-04-22
申请人： Arun Krishnan , Xiang Zhou , Martin Huber , Michael Kelm , Joerg Freund
发明人： Arun Krishnan , Xiang Zhou , Martin Huber , Michael Kelm , Joerg Freund
IPC分类号： G06K9/62 , G06K9/00
CPC分类号： G06T7/11 , G06F19/00 , G06T7/0012 , G06T2207/20081 , G16H50/70
摘要： A method for training a computer system for automatic detection of regions of interest includes receiving patient records. For each of the received patient records a text field and a medical image are identified from within the patient record and the medical image is automatically segmented to identify a structure of interest. The text field is searched for one or more keywords indicative of a particular abnormality associated with the structure of interest. The medical image is added to a grouping representing the particular abnormality when the text field indicates that the patient has the particular abnormality and the medical image is added to a grouping representing the absence of the particular abnormality when the text field does not indicate that the patient has the particular abnormality. The groupings of medical images are used to automatically train a computer system for the subsequent detection of the particular abnormality.
摘要翻译：用于训练用于感兴趣区域的自动检测的计算机系统的方法包括接收患者记录。对于每个接收到的患者记录，从患者记录中识别文本字段和医学图像，并且医疗图像被自动分段以识别感兴趣的结构。搜索文本字段以查找指示与感兴趣结构相关联的特定异常的一个或多个关键字。当文本字段指示患者具有特定异常并将医学图像添加到代表不存在特定异常的分组时，医疗图像被添加到表示特定异常的分组中，当文本字段没有指示患者有特殊的异常。医学图像的分组用于自动训练计算机系统以便随后检测特定的异常。

2. 发明授权

US07949167B2 Automatic learning of image features to predict disease 有权
标题翻译：自动学习图像特征预测疾病
公开(公告)号：US07949167B2
公开(公告)日：2011-05-24
申请号：US12427974
申请日：2009-04-22
申请人： Arun Krishnan , Xiang Zhou , Martin Huber , Michael Kelm , Joerg Freund
发明人： Arun Krishnan , Xiang Zhou , Martin Huber , Michael Kelm , Joerg Freund
IPC分类号： G06K9/00 , A61B6/00 , A61B5/00
CPC分类号： G06T7/11 , G06F19/00 , G06T7/0012 , G06T2207/20081 , G16H50/70
摘要： A method for training a computer system for automatic detection of regions of interest includes receiving patient records. For each of the received patient records a text field and a medical image are identified from within the patient record and the medical image is automatically segmented to identify a structure of interest. The text field is searched for one or more keywords indicative of a particular abnormality associated with the structure of interest. The medical image is added to a grouping representing the particular abnormality when the text field indicates that the patient has the particular abnormality and the medical image is added to a grouping representing the absence of the particular abnormality when the text field does not indicate that the patient has the particular abnormality. The groupings of medical images are used to automatically train a computer system for the subsequent detection of the particular abnormality.
摘要翻译：用于训练用于感兴趣区域的自动检测的计算机系统的方法包括接收患者记录。对于每个接收到的患者记录，从患者记录中识别文本字段和医学图像，并且医疗图像被自动分段以识别感兴趣的结构。搜索文本字段以查找指示与感兴趣结构相关联的特定异常的一个或多个关键字。当文本字段指示患者具有特定异常并将医学图像添加到代表不存在特定异常的分组时，医疗图像被添加到表示特定异常的分组中，当文本字段没有指示患者有特殊的异常。医学图像的分组用于自动训练计算机系统以便随后检测特定的异常。

3. 发明授权

US08494235B2 Automatic detection of lymph nodes 有权
标题翻译：自动检测淋巴结
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。

4. 发明授权

US07876938B2 System and method for whole body landmark detection, segmentation and change quantification in digital images 有权
标题翻译：数字图像全身地标检测，分割和变化量化的系统和方法
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。

5. 发明授权

US07840046B2 System and method for detection of breast masses and calcifications using the tomosynthesis projection and reconstructed images 有权
标题翻译：使用断层合成投影和重建图像检测乳房肿块和钙化的系统和方法
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。

6. 发明授权

US07792778B2 Knowledge-based imaging CAD system 有权
标题翻译：基于知识的成像CAD系统
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。

7. 发明授权

US07724930B2 Systems and methods for automatic change quantification for medical decision support 有权
标题翻译：用于医疗决策支持的自动变更量化的系统和方法
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。

8. 发明申请

US20080267483A1 Registration of Medical Images Using Learned-Based Matching Functions 有权
标题翻译：使用基于学习的匹配功能注册医学图像
公开(公告)号：US20080267483A1
公开(公告)日：2008-10-30
申请号：US12110643
申请日：2008-04-28
申请人： Yiqiang Zhan , Xiang Zhou , Arun Krishnan
发明人： Yiqiang Zhan , Xiang Zhou , Arun Krishnan
IPC分类号： G06K9/00
CPC分类号： G06K9/32 , G06K2209/05 , G06T7/33
摘要： A method for registering a medical image includes acquiring a first medical image of a subject. One or more simulated medical images are synthesized based on the acquired first medical image. One or more matching functions are trained using the first medical image and the simulated medical images. A second medical image of the subject is acquired. The first medical image and the second medical image are registered using the one or more trained matching functions.
摘要翻译：用于登记医学图像的方法包括获取对象的第一医学图像。基于获取的第一医学图像合成一个或多个模拟医学图像。使用第一医学图像和模拟医学图像训练一个或多个匹配函数。获取受试者的第二个医学图像。第一医用图像和第二医用图像使用一个或多个经过训练的匹配功能进行登记。

9. 发明申请

US20080025592A1 System and Method for Detection of Breast Masses and Calcifications Using the Tomosynthesis Projection and Reconstructed Images 有权
标题翻译：使用Tomosynthesis Projection和重建图像检测乳腺肿块和钙化的系统和方法
公开(公告)号：US20080025592A1
公开(公告)日：2008-01-31
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
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。

10. 发明授权

US07244230B2 Computer aided diagnostic assistance for medical imaging 有权
标题翻译：计算机辅助诊断辅助医学成像
公开(公告)号：US07244230B2
公开(公告)日：2007-07-17
申请号：US10702721
申请日：2003-11-06
申请人： Bhavani Duggirala , Diane S. Paine , Dorin Comaniciu , Arun Krishnan , Xiang Zhou
发明人： Bhavani Duggirala , Diane S. Paine , Dorin Comaniciu , Arun Krishnan , Xiang Zhou
IPC分类号： A61B5/00 , A61B8/00
CPC分类号： A61B8/56 , A61B8/00 , A61B8/4427 , A61B8/485 , A61B8/565 , G06F19/00 , G06T7/0012 , G06T2207/30004 , G16H50/20 , Y02A90/22 , Y02A90/26 , Y10S128/925
摘要： Various attributes for medical CAD analysis used alone or in combination are disclosed, including: (1) using medical sensor data and context information associated with the configuration of the medical system to provide recommendations for further acts to be performed for more accurate or different diagnosis, (2) recommending further acts to be performed and providing an indication of the importance of the further act for further diagnosis, (3) distributing the computer-assisted diagnosis software and/or database over multiple systems, and (4) transmitting patient data to a remote system for CAD diagnosis and using the results of the CAD diagnosis at the remote facility.
摘要翻译：公开了用于单独或组合使用的医疗CAD分析的各种属性，包括：（1）使用与医疗系统的配置相关联的医疗传感器数据和上下文信息来提供用于执行更准确或不同诊断的进一步行为的建议，（2）推荐进一步的行为，并提供进一步诊断的进一步行动的重要性的指示，（3）通过多个系统分发计算机辅助诊断软件和/或数据库，以及（4）将患者数据发送到用于CAD诊断的远程系统，并在远程设施中使用CAD诊断的结果。

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