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1. 发明授权

US09159128B2 Enhanced multi-protocol analysis via intelligent supervised embedding (empravise) for multimodal data fusion 有权
标题翻译：通过智能监控嵌入（empravise）进行多模态数据融合的增强型多协议分析
公开(公告)号：US09159128B2
公开(公告)日：2015-10-13
申请号：US13978812
申请日：2012-01-13
申请人： Anant Madabhushi , Satish Viswanath
发明人： Anant Madabhushi , Satish Viswanath
IPC分类号： G06K9/00 , G06T7/00 , G06K9/62
CPC分类号： G06T7/0012 , G06K9/6232 , G06K9/6251 , G06K9/6256 , G06K2209/051
摘要： The present invention provides a system and method for analysis of multimodal imaging and non-imaging biomedical data, using a multi-parametric data representation and integration framework. The present invention makes use of (1) dimensionality reduction to account for differing dimensionalities and scale in multimodal biomedical data, and (2) a supervised ensemble of embeddings to accurately capture maximum available class information from the data.
摘要翻译：本发明提供了一种使用多参数数据表示和集成框架来分析多模式成像和非成像生物医学数据的系统和方法。本发明利用（1）降维来解决多模式生物医学数据中的不同维度和尺度，以及（2）监督的嵌入集合，以从数据中准确地捕获最大可用类信息。

2. 发明授权

US09002092B2 Image-based risk score—A prognostic predictor of survival and outcome from digital histopathology 有权
标题翻译：基于图像的风险评分 - 从数字组织病理学的生存和结果的预后预测
公开(公告)号：US09002092B2
公开(公告)日：2015-04-07
申请号：US13147537
申请日：2010-02-02
申请人： Anant Madabhushi , Ajay Basavanhally , Shridar Ganesan
发明人： Anant Madabhushi , Ajay Basavanhally , Shridar Ganesan
IPC分类号： G06K9/00 , G06T7/00
CPC分类号： G06T7/0012 , G06K9/00147 , G06K9/6277 , G06T2207/30024 , G06T2207/30096
摘要： The present invention relates to an image-based computer-aided prognosis (CAP) system and method that seeks to replicate the prognostic power of molecular assays in histopathology and pathological processes, including but not limited to cancer. Using only a tissue slide samples, a mechanism for digital slide scanning, and a computer, the present invention relates to an image-based CAP system and method which aims to overcome many of the drawbacks associated with prognostic molecular assays (e.g. Oncotype DX) including the high cost associated with the assay, limited laboratory facilities with specialized equipment, and length of time between biopsy and prognostic prediction.
摘要翻译：本发明涉及一种基于图像的计算机辅助预后（CAP）系统和方法，其寻求在组织病理学和病理过程（包括但不限于癌症）中复制分子测定的预后能力。本发明涉及一种基于图像的CAP系统和方法，其目的在于克服与预后分子测定（例如Oncotype DX）相关的许多缺点，包括与测定相关的高成本，具有专门设备的实验室设施有限，以及活检和预后预测之间的时间长度。

3. 发明授权

US08442285B2 Combined feature ensemble mutual information image registration 有权
标题翻译：组合特征集合互信息图像注册
公开(公告)号：US08442285B2
公开(公告)日：2013-05-14
申请号：US12593651
申请日：2008-04-01
申请人： Anant Madabhushi , Jonathan Chappelow , Michael Feldman , John E. Tomaszewski
发明人： Anant Madabhushi , Jonathan Chappelow , Michael Feldman , John E. Tomaszewski
IPC分类号： G06K9/00
CPC分类号： G06K9/6231 , G06T7/32 , G06T7/33 , G06T7/35 , G06T2207/10088 , G06T2207/30004 , G06T2207/30081
摘要： This invention relates to medical image registration. Specifically, the invention relates to a combined feature ensemble mutual information (COFEMI) for robust inter-modal, inter-protocol image registration.
摘要翻译：本发明涉及医学图像配准。具体地说，本发明涉及用于鲁棒模式间协议间映像配准的组合特征集合互信息（COFEMI）。

4. 发明申请

US20110026798A1 SYSTEM AND METHOD FOR AUTOMATED SEGMENTATION, CHARACTERIZATION, AND CLASSIFICATION OF POSSIBLY MALIGNANT LESIONS AND STRATIFICATION OF MALIGNANT TUMORS 有权
标题翻译：用于自动分割，识别和分类可能的恶性淋巴结和恶性肿瘤分离的系统和方法
公开(公告)号：US20110026798A1
公开(公告)日：2011-02-03
申请号：US12867349
申请日：2009-02-19
申请人： Anant Madabhushi , Shannon Agner , Mark Rosen
发明人： Anant Madabhushi , Shannon Agner , Mark Rosen
IPC分类号： G06K9/00
CPC分类号： G01R33/56 , G01R33/5608 , G01R33/56308 , G06T7/0012 , G06T7/11 , G06T2207/10096 , G06T2207/20076 , G06T2207/20108 , G06T2207/20116 , G06T2207/30068 , G06T2207/30096
摘要： A method and apparatus for classifying possibly malignant lesions from sets of DCE-MRI images includes receiving a set of MRI slice images obtained at respectively different times, where each slice image includes voxels representative of at least one region of interest (ROI). The images are processed to determine the boundaries of the ROIs and the voxels within the identified boundaries in corresponding regions of the images from each time period are processed to extract kinetic texture features. The kinetic texture features are then used in a classification process which classifies the ROIs as malignant or benign. The malignant lesions are further classified to separate TN lesions from non-TN lesions.
摘要翻译：从DCE-MRI图像集合分类可能的恶性病变的方法和装置包括接收在不同时间获得的一组MRI切片图像，其中每个切片图像包括代表至少一个感兴趣区域（ROI）的体素。处理图像以确定ROI的边界，并且处理来自每个时间段的图像的相应区域中所识别的边界内的体素以提取动力学纹理特征。然后将动力学纹理特征用于将ROI分类为恶性或良性的分类过程。恶性病变进一步分类为TN病变与非TN病变。

5. 发明申请

US20100098306A1 MALIGNANCY DIAGNOSIS USING CONTENT - BASED IMAGE RETREIVAL OF TISSUE HISTOPATHOLOGY 有权
标题翻译：使用内容的组织病理学图像退化的恶性肿瘤诊断
公开(公告)号：US20100098306A1
公开(公告)日：2010-04-22
申请号：US12375981
申请日：2007-08-01
申请人： Anant Madabhushi , Scott Doyle , Michael D. Feldman , John E. Tomaszewski
发明人： Anant Madabhushi , Scott Doyle , Michael D. Feldman , John E. Tomaszewski
IPC分类号： G06K9/00
CPC分类号： G06K9/0014 , G06T7/0012 , G06T7/11 , G06T2207/30096
摘要： This invention relates to computer-aided diagnostics using content-based retrieval of histopathological image features. Specifically, the invention relates to the extraction of image features from a histopathological image based on predetermined criteria and their analysis for malignancy determination.
摘要翻译：本发明涉及使用基于内容的组织病理学图像特征检索的计算机辅助诊断。具体地，本发明涉及基于预定标准从组织病理学图像中提取图像特征及其对于恶性肿瘤确定的分析。

6. 发明申请

US20050027188A1 Method and apparatus for automatically detecting breast lesions and tumors in images 失效
标题翻译：用于自动检测图像中乳腺病变和肿瘤的方法和装置
公开(公告)号：US20050027188A1
公开(公告)日：2005-02-03
申请号：US10736455
申请日：2003-12-15
申请人： Dimitris Metaxas , Anant Madabhushi
发明人： Dimitris Metaxas , Anant Madabhushi
IPC分类号： A61B5/055 , A61B8/08 , G06T5/00 , G06T7/00 , A61B5/05
CPC分类号： G06T7/0012 , A61B5/055 , A61B5/7267 , A61B8/0825 , G06T7/11 , G06T7/12 , G06T7/143 , G06T7/149 , G06T2207/10088 , G06T2207/10132 , G06T2207/20156 , G06T2207/30068 , G06T2207/30096
摘要： A method and apparatus for automatically detecting breast tumors and lesions in images, including ultrasound, digital and analog mammograms, and MRI images, is provided. An image of a breast is acquired. The image is filtered and contrast of the image is enhanced. Intensity and texture classifiers are applied to each pixel in the image, the classifiers indicative of the probability of the pixel corresponding to a tumor. A seed point is identified within the image, and a region of interest is grown around the seed point. Directional gradients are calculated for each pixel of the image. Boundary points of the region of interest are identified. The boundary points are passed as inputs to a deformable model. The deformable model processes the boundary points to indicate the presence or absence of a tumor.
摘要翻译：提供了一种用于自动检测图像中的乳腺肿瘤和病变的方法和装置，包括超声，数字和模拟乳房X线照片以及MRI图像。获取乳房的图像。图像被过滤，图像的对比度增强。强度和纹理分类器被应用于图像中的每个像素，分类器指示对应于肿瘤的像素的概率。在图像内识别种子点，并且在种子点周围生长感兴趣区域。为图像的每个像素计算定向梯度。确定感兴趣区域的边界点。边界点作为输入传递给可变形模型。可变形模型处理边界点以指示肿瘤的存在或不存在。

7. 发明授权

US09235891B2 Boosted consensus classifier for large images using fields of view of various sizes 有权
标题翻译：使用各种尺寸视野的大图像的增强共识分类器
公开(公告)号：US09235891B2
公开(公告)日：2016-01-12
申请号：US13978927
申请日：2012-01-10
申请人： Anant Madabhushi , Ajay Basavanhally
发明人： Anant Madabhushi , Ajay Basavanhally
IPC分类号： G06K9/00 , G06T7/00 , G06K9/62
CPC分类号： G06T7/0012 , G06K9/00147 , G06K9/469 , G06K9/6256 , G06K2209/05 , G06T7/12 , G06T7/149 , G06T2207/20072 , G06T2207/30024 , G06T2207/30068
摘要： A system and method for predicting disease outcome by analyzing a large, heterogeneous image by a boosted, multi-field-of-view (FOV) framework, based on image-based features from multi-parametric heterogeneous images, comprises (a) inputting the heterogeneous image; (b) generating a plurality of FOVs at a plurality of fixed FOV sizes, the method for generating the plurality of FOVs at a plurality of fixed FOV sizes comprising dividing simultaneously, via the computing device, the large, heterogeneous image into (i) a plurality of FOVs at a first fixed FOV size from among the plurality of fixed FOV sizes; and (ii) a plurality of FOVs at a second fixed FOV size from among the plurality of fixed FOV sizes; (c) producing simultaneously for the heterogeneous image a combined class decision for: (i) the plurality of FOVs at the first fixed FOV size, and (ii) the plurality of FOV s at the second fixed FOV size.
摘要翻译：通过基于来自多参数异构图像的基于图像的特征，通过增强的多视场（FOV）框架分析大的异质图像来预测疾病结果的系统和方法包括（a）输入异质图像（b）以多个固定FOV大小生成多个FOV，所述用于以多个固定FOV大小生成多个FOV的方法包括经由计算装置同时将大的异构图像划分为（i）多个固定FOV尺寸中的第一固定FOV尺寸的多个FOV; 和（ii）多个固定FOV尺寸中的第二固定FOV尺寸的多个FOV; （c）同时产生用于异质图像的组合类决定：（i）以第一固定FOV大小的多个FOV，以及（ii）第二固定FOV大小的多个FOV。

8. 发明申请

US20140064581A1 BOOSTED CONSENSUS CLASSIFIER FOR LARGE IMAGES USING FIELDS OF VIEW OF VARIOUS SIZES 有权
标题翻译：使用各种不同尺寸视图的大图像增强共享分类器
公开(公告)号：US20140064581A1
公开(公告)日：2014-03-06
申请号：US13978927
申请日：2012-01-10
申请人： Anant Madabhushi , Ajay Basavanhally
发明人： Anant Madabhushi , Ajay Basavanhally
IPC分类号： G06T7/00
CPC分类号： G06T7/0012 , G06K9/00147 , G06K9/469 , G06K9/6256 , G06K2209/05 , G06T7/12 , G06T7/149 , G06T2207/20072 , G06T2207/30024 , G06T2207/30068
摘要： A system and method for predicting disease outcome by analyzing a large, heterogeneous image by a boosted, multi-field-of-view (FOV) framework, based on image-based features from multi-parametric heterogeneous images, comprises (a) inputting the heterogeneous image; (b) generating a plurality of FOVs at a plurality of fixed FOV sizes, the method for generating the plurality of FOVs at a plurality of fixed FOV sizes comprising dividing simultaneously, via the computing device, the large, heterogeneous image into (i) a plurality of FOVs at a first fixed FOV size from among the plurality of fixed FOV sizes; and (ii) a plurality of FOVs at a second fixed FOV size from among the plurality of fixed FOV sizes; (c) producing simultaneously for the heterogeneous image a combined class decision for: (i) the plurality of FOVs at the first fixed FOV size, and (ii) the plurality of FOV s at the second fixed FOV size.
摘要翻译：通过基于来自多参数异构图像的基于图像的特征，通过增强的多视场（FOV）框架分析大的异质图像来预测疾病结果的系统和方法包括（a）输入异质图像（b）以多个固定的FOV大小生成多个FOV，所述用于以多个固定的FOV大小生成多个FOV的方法包括经由计算装置将大的异构图像同时划分为（i）多个固定FOV尺寸中的第一固定FOV尺寸的多个FOV; 和（ii）多个固定FOV尺寸中的第二固定FOV尺寸的多个FOV; （c）同时产生用于异质图像的组合类决定：（i）以第一固定FOV大小的多个FOV，以及（ii）第二固定FOV大小的多个FOV。

9. 发明申请

US20140037172A1 ENHANCED MULTI-PROTOCOL ANALYSIS VIA INTELLIGENT SUPERVISED EMBEDDING (EMPRAVISE) FOR MULTIMODAL DATA FUSION 有权
标题翻译：通过用于多模式数据融合的智能监控嵌入（EMPRAVISE）来增强多协议分析
公开(公告)号：US20140037172A1
公开(公告)日：2014-02-06
申请号：US13978812
申请日：2012-01-13
申请人： Anant Madabhushi , Satish Viswanath
发明人： Anant Madabhushi , Satish Viswanath
IPC分类号： G06T7/00
CPC分类号： G06T7/0012 , G06K9/6232 , G06K9/6251 , G06K9/6256 , G06K2209/051
摘要： The present invention provides a system and method for analysis of multimodal imaging and non-imaging biomedical data, using a multi-parametric data representation and integration framework. The present invention makes use of (1) dimensionality reduction to account for differing dimensionalities and scale in multimodal biomedical data, and (2) a supervised ensemble of embeddings to accurately capture maximum available class information from the data.
摘要翻译：本发明提供了一种使用多参数数据表示和集成框架来分析多模式成像和非成像生物医学数据的系统和方法。本发明利用（1）降维来解决多模式生物医学数据中的不同维度和尺度，以及（2）监督的嵌入集合，以从数据中准确地捕获最大可用类信息。

10. 发明申请

US20120106821A1 IMAGE-BASED RISK SCORE-A PROGNOSTIC PREDICTOR OF SURVIVAL AND OUTCOME FROM DIGITAL HISTOPATHOLOGY 有权
标题翻译：基于图像的风险评分 - 来自数字病理学的生存和预测的预后因素
公开(公告)号：US20120106821A1
公开(公告)日：2012-05-03
申请号：US13147537
申请日：2010-02-02
申请人： Anant Madabhushi , Ajay Basavanhally , Shridar Ganesan
发明人： Anant Madabhushi , Ajay Basavanhally , Shridar Ganesan
IPC分类号： G06K9/46
CPC分类号： G06T7/0012 , G06K9/00147 , G06K9/6277 , G06T2207/30024 , G06T2207/30096
摘要： The present invention relates to an image-based computer-aided prognosis (CAP) system and method that seeks to replicate the prognostic power of molecular assays in histopathology and pathological processes, including but not limited to cancer. Using only a tissue slide samples, a mechanism for digital slide scanning, and a computer, the present invention relates to an image-based CAP system and method which aims to overcome many of the drawbacks associated with prognostic molecular assays (e.g. Oncotype DX) including the high cost associated with the assay, limited laboratory facilities with specialized equipment, and length of time between biopsy and prognostic prediction.
摘要翻译：本发明涉及一种基于图像的计算机辅助预后（CAP）系统和方法，其寻求在组织病理学和病理过程（包括但不限于癌症）中复制分子测定的预后能力。本发明涉及一种基于图像的CAP系统和方法，其目的在于克服与预后分子测定（例如Oncotype DX）相关的许多缺点，包括与测定相关的高成本，具有专门设备的实验室设施有限，以及活检和预后预测之间的时间长度。

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