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1. 发明申请

WO2009042034A1 USER INTERFACE FOR POLYP ANNOTATION, SEGMENTATION, AND MEASUREMENT IN 3D COMPUTED TOMOGRAPHY COLONOGRAPHY 审中-公开
标题翻译： 3D计算机图像聚合的聚合物聚合，分割和测量的用户界面
公开(公告)号：WO2009042034A1
公开(公告)日：2009-04-02
申请号：PCT/US2008/010629
申请日：2008-09-10
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , LU, Le , BARBU, Adrian , WOLF, Matthias , LAKARE, Sarang , BOGONI, Luca , SALGANICOFF, Marcos , COMANICIU, Dorin
发明人： LU, Le , BARBU, Adrian , WOLF, Matthias , LAKARE, Sarang , BOGONI, Luca , SALGANICOFF, Marcos , COMANICIU, Dorin
IPC分类号： G06F3/048
CPC分类号： G16H40/63 , G06F19/00 , G06T7/0012 , G06T7/12 , G06T2200/24 , G06T2207/10072 , G06T2207/20092 , G06T2207/30032
摘要： A method and system for providing a user interface for polyp annotation, segmentation, and measurement in computer tomography colonography (CTC) volumes is disclosed. The interface receives an initial polyp position in a CTC volume, and automatically segments the polyp based on the initial polyp position. In order to segment the polyp, a polyp tip is detected in the CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary. The segmented polyp is displayed in the user interface, and a user can modify the segmented polyp boundary using the interface. The interface can measure the size of the segmented polyp in three dimensions. The user can also use the interface for polyp annotation in CTC volumes.
摘要翻译：公开了一种用于在计算机断层造影（CTC）体积中提供用于息肉注释，分割和测量的用户界面的方法和系统。界面在CTC体积中接收初始息肉位置，并根据初始息肉位置自动分段息肉。为了分割息肉，使用训练有素的3D点检测器在CTC体积中检测息肉末端。然后将局部极坐标系拟合到CTC体积中的结肠表面，其中原点在检测到的息肉末端。使用训练有素的3D框，在局部极坐标系的每个轴上检测Polyp内部体素和息肉外部体素。基于检测到的息肉内部体素和息肉外部体素，通过使用训练有素的分类器进行升压1D曲线解析，在局部极坐标系的每个轴上检测边界体素。这导致分段息肉边界。分段息肉显示在用户界面中，用户可以使用界面修改分段息肉边界。界面可以在三维中测量分段息肉的大小。用户还可以在CTC卷中使用界面进行息肉注释。

2. 发明申请

WO2009038647A1 METHOD AND SYSTEM FOR POLYP SEGMENTATION FOR 3D COMPUTED TOMOGRAPHY COLONOGRAPHY 审中-公开
标题翻译：用于3D计算机图像聚合的聚合分割的方法和系统
公开(公告)号：WO2009038647A1
公开(公告)日：2009-03-26
申请号：PCT/US2008/010469
申请日：2008-09-08
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , LU, Le , BARBU, Adrian , WOLF, Matthias , LAKARE, Sarang , BOGONI, Luca , SALGANICOFF, Marcos , COMANICIU, Dorin
发明人： LU, Le , BARBU, Adrian , WOLF, Matthias , LAKARE, Sarang , BOGONI, Luca , SALGANICOFF, Marcos , COMANICIU, Dorin
IPC分类号： G06T5/00 , G06T7/00
CPC分类号： G06T7/0012 , G06T7/11 , G06T7/143 , G06T2207/10081 , G06T2207/20076 , G06T2207/30032
摘要： A method and system for polyp segmentation in computed tomography colonogrphy (CTC) volumes is disclosed. The polyp segmentation method utilizes a three-staged probabilistic binary classification approach for automatically segmenting polyp voxels from surrounding tissue in CTC volumes. Based on an input initial polyp position, a polyp tip is detected in a CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary
摘要翻译：公开了一种计算机断层扫描（CTC）体积中息肉分割的方法和系统。息肉分割方法采用三阶段概率二分类方法，自动分割CTC体积周围组织的息肉体素。基于输入的初始息肉位置，使用经过训练的3D点检测器在CTC体积中检测息肉末端。然后将局部极坐标系拟合到CTC体积中的结肠表面，其中原点在检测到的息肉末端。使用训练有素的3D框，在局部极坐标系的每个轴上检测Polyp内部体素和息肉外部体素。基于检测到的息肉内部体素和息肉外部体素，通过使用训练有素的分类器进行升压1D曲线解析，在局部极坐标系的每个轴上检测边界体素。这导致分段息肉边界

3. 发明申请

WO2009009092A1 SYSTEM AND METHOD FOR DETECTING SPHERICAL AND ELLIPSOIDAL OBJECTS USING CUTTING PLANES 审中-公开
标题翻译：使用切割平面检测球形和椭圆形物体的系统和方法
公开(公告)号：WO2009009092A1
公开(公告)日：2009-01-15
申请号：PCT/US2008/008465
申请日：2008-07-10
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , WOLF, Matthias , SALGANICOFF, Marcos , LAKARE, Sarang
发明人： WOLF, Matthias , SALGANICOFF, Marcos , LAKARE, Sarang
IPC分类号： G06T7/00
CPC分类号： G06T7/0012
摘要： A method for detecting spherical and ellipsoidal objects is digitized medical images includes providing (81) a 2-dimensional (2D) slice I(x, y) extracted from a medical image volume of a colon, said image volume comprising a plurality of intensities associated with a 3 grid of points, generating (82) a plurality of templates of different sizes whose shape matches a target structure being sought in said slice, calculating (83) a normalized gradient from said slice, calculating (84) a diverging field gradient response (DFGR) for each of the plurality of masks with the normalized gradient, and selecting (85) a strongest response as being indicative of the position and size of the target structure.
摘要翻译：用于检测球形和椭球体的方法是数字化医学图像，包括提供（81）从冒号的医学图像体积提取的二维（2D）切片I（x，y），所述图像体积包括多个相关联的强度产生（82）多个不同尺寸的模板，其形状与在所述切片中寻找的目标结构匹配，从所述切片计算（83）归一化梯度，计算（84）发散场梯度响应（DFGR），并且选择（85）最强响应作为目标结构的位置和大小的指示。

4. 发明申请

WO2009009099A2 COMPUTER-ASSISTED DETECTION OF COLONIC POLYPS USING CONVEX HULL 审中-公开
标题翻译：使用凸耳的计算机辅助检测殖民地聚合物
公开(公告)号：WO2009009099A2
公开(公告)日：2009-01-15
申请号：PCT/US2008/008476
申请日：2008-07-10
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , WOLF, Matthias , SALGANICOFF, Marcos
发明人： WOLF, Matthias , SALGANICOFF, Marcos
IPC分类号： G06T7/00
CPC分类号： G06T7/0012 , G06T7/64 , G06T2207/30032
摘要： A method for performing computer-assisted diagnosis includes receiving a plurality of two-dimensional views of an internal structure (S51), defining a search space around one or more areas of analysis within each view of the internal structure (S54), calculating a convex hull for each area of analysis within each search space of each view of the internal structure (S55), determining a set of foreground pixels that are located within the convex hull for each area of analysis within each search space within each view of the internal structure (S56), and for each area of analysis, merging the set of foreground pixels that are located within the convex hull from each view (S57).
摘要翻译：一种用于执行计算机辅助诊断的方法，包括接收内部结构的多个二维视图（S51），在内部结构的每个视图内围绕一个或多个分析区域定义搜索空间（S54），计算凸对于内部结构的每个视图的每个搜索空间内的每个搜索空间中的每个分析区域的船体（S55），确定在内部结构的每个视图内的每个搜索空间内的每个搜索空间内的每个分析区域位于凸包内的一组前景像素（S56），并且对于每个分析区域，从每个视图合并位于凸包中的前景像素集合（S57）。

5. 发明申请

WO2007033228A2 REDUCING FALSE POSITIVES FOR AUTOMATIC COMPUTERIZED DETECTION OF OBJECTS 审中-公开
标题翻译：减少对物体自动计算机检测的虚假信息
公开(公告)号：WO2007033228A2
公开(公告)日：2007-03-22
申请号：PCT/US2006/035601
申请日：2006-09-11
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , LIANG, Jianming , XUE, Zhiyun , SALGANICOFF, Marcos , WOLF, Matthias
发明人： LIANG, Jianming , XUE, Zhiyun , SALGANICOFF, Marcos , WOLF, Matthias
CPC分类号： G06T7/0012
摘要： A computer-implemented method for identifying an object of interest includes providing input data including an image and a candidate for the object of interest in the image (201), extracting a boundary of the candidate, and extracting a segment of a region of interest containing the candidate (202). The method further includes determining a plurality of features of an extracted segment of the region of interest containing the candidate (203), and outputting the object of interest (204), wherein the object of interest is characterized by the plurality of features (205), wherein the object of interest and the plurality of features are stored as computer-readable code.
摘要翻译：用于识别感兴趣对象的计算机实现的方法包括：提供包括图像的图像和候选对象的输入数据（201），提取候选的边界，以及提取包含候选人（202）。该方法还包括确定包含候选（203）的感兴趣区域的提取段的多个特征，并输出感兴趣对象（204），其中感兴趣对象由多个特征（205）表征，，其中感兴趣对象和多个特征被存储为计算机可读代码。

6. 发明申请

WO2006042322A1 A TOBOGGAN-BASED METHOD FOR AUTOMATIC DETECTION AND SEGMENTATION OF OBJECTS IN IMAGE DATA 审中-公开
标题翻译：一种基于TOBOGGAN的方法，用于自动检测和分割图像数据中的对象
公开(公告)号：WO2006042322A1
公开(公告)日：2006-04-20
申请号：PCT/US2005/037051
申请日：2005-10-12
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , LIANG, Jianming , WOLF, Matthias , SALGANICOFF, Marcos
发明人： LIANG, Jianming , WOLF, Matthias , SALGANICOFF, Marcos
IPC分类号： G06T5/30
CPC分类号： G06K9/342 , G06K2209/05 , G06T7/11 , G06T7/155 , G06T2207/10081 , G06T2207/20152 , G06T2207/30032 , G06T2207/30064 , G06T2207/30101
摘要： An exemplary method of detecting one or more objects in image data is provided. The image data includes a plurality of pixels/voxels. The method includes sliding (at 810) pixels/voxels that meet sliding criteria; and collecting (at 815) the slid pixels/voxels that satisfy collecting criteria. An exemplary method of segmenting an object in image data is also provided. The method includes receiving an initial pixel/voxel in the image data; and forming a segmentation of the object based on the initial pixel/voxel.
摘要翻译：提供了检测图像数据中的一个或多个对象的示例性方法。图像数据包括多个像素/体素。该方法包括滑动（在810处）符合滑动标准的像素/体素; 并收集（815）满足收集标准的滑动像素/体素。还提供了分割图像数据中的对象的示例性方法。该方法包括在图像数据中接收初始像素/体元; 并基于初始像素/体元形成对象的分割。

7. 发明申请

WO2009045460A1 SYSTEM AND METHOD FOR LESION DETECTION USING LOCALLY ADJUSTABLE PRIORS 审中-公开
标题翻译：使用本地可调优先级进行检测的系统和方法
公开(公告)号：WO2009045460A1
公开(公告)日：2009-04-09
申请号：PCT/US2008/011398
申请日：2008-10-02
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , JEREBKO, Anna , SALGANICOFF, Marcos
发明人： JEREBKO, Anna , SALGANICOFF, Marcos
IPC分类号： G06K9/62
CPC分类号： G06K9/6278 , G06K2209/05 , G06K2209/053 , G06T7/0012 , G06T7/11 , G06T7/143 , G06T2207/30028 , G06T2207/30061
摘要： According to an aspect of the invention, a method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing (81) a training set of annotated images, each image including one or more candidate regions that have been identified as suspicious, deriving (82) a set of descriptive feature vectors, where each candidate region is associated with a feature vector. A subset of the features are conditionally dependent, and the remaining features are conditionally independent. The conditionally independent features are used to train (83) a naive Bayes classifier that classifies the candidate regions as lesion or non-lesion. A joint probability distribution that models the conditionally dependent features, and a prior-odds probability ratio of a candidate region being associated with a lesion are determined (84, 85) from the training images. A new classifier is formed (86) from the naive Bayes classifier, the joint probability distribution, and the prior-odds probability ratio.
摘要翻译：根据本发明的一个方面，一种用于训练分类器的方法，用于在数字医学图像的计算机辅助诊断中对候选区域进行分类，包括提供（81）注释图像的训练集，每个图像包括一个或多个已被识别的候选区域作为可疑的，导出（82）一组描述性特征向量，其中每个候选区域与特征向量相关联。特征的子集有条件依赖，其余的特征是有条件的独立的。条件独立的特征用于训练（83）一个幼稚贝叶斯分类器，将候选区域分类为病变或非病变。从训练图像中确定与条件相关特征建模的联合概率分布以及与病变相关联的候选区域的先前概率概率比（84,85）。从朴素贝叶斯分类器，联合概率分布和先验概率概率比，形成一个新的分类器（86）。

8. 发明申请

WO2006107801A3 SYSTEM AND METHOD FOR REDUCING ARTIFACTS IN MOTION CORRECTED DYNAMIC IMAGE SEQUENCES 审中-公开
公开(公告)号：WO2006107801A3
公开(公告)日：2006-10-12
申请号：PCT/US2006/012180
申请日：2006-04-03
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , VALADEZ, Gerardo, Hermosillo , SALGANICOFF, Marcos
发明人： VALADEZ, Gerardo, Hermosillo , SALGANICOFF, Marcos
IPC分类号： G06T7/00
摘要： A system and method for reducing artifacts in a motion corrected image sequence are provided. A method for reducing an artifact in a motion corrected image sequence comprises: applying a deformation to a reference image of a plurality of post-contrast enhanced images to obtain an interpolated version of the reference image (340); and performing a registration between the interpolated version of the reference image and a pre-contrast enhanced image and the plurality of post-contrast enhanced images to obtain a plurality of motion corrected images (350).

9. 发明申请

WO2007056495A1 SYSTEM AND METHOD FOR COMPUTER AIDED DETECTION VIA ASYMMETRIC CASCADE OF SPARSE LINEAR CLASSIFIERS 审中-公开
标题翻译：用于计算机辅助检测的系统和方法通过非线性分类器的不对称分类
公开(公告)号：WO2007056495A1
公开(公告)日：2007-05-18
申请号：PCT/US2006/043585
申请日：2006-11-08
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , BI, Jinbo , PERIASWAMY, Senthil , OKADA, Kazunori , KUBOTA, Toshiro , FUNG, Glenn , SALGANICOFF, Marcos , RAO, R. Bharat
发明人： BI, Jinbo , PERIASWAMY, Senthil , OKADA, Kazunori , KUBOTA, Toshiro , FUNG, Glenn , SALGANICOFF, Marcos , RAO, R. Bharat
IPC分类号： G06K9/68 , G06K9/62
CPC分类号： G06K9/6256 , G06K9/6282
摘要： A method for computer aided detection of anatomical abnormalities in medical images includes providing a plurality of abnormality candidates (10) and features (11, 13, 15) of said abnormality candidates, and classifying said abnormality candidates as true positives or false positives using a hierarchical cascade of linear classifiers (12, 14, 16) of the form sign (w T x+ b ), wherein x is a feature vector, w is a weighting vector and b is a model parameter, wherein different weights are used to penalize false negatives and false positives, and wherein more complex features are used for each successive stage of said cascade of classifiers.
摘要翻译：一种用于计算机辅助检测医学图像中的解剖异常的方法，包括提供所述异常候选的多个异常候选（10）和特征（11,13,15），并且使用分层次分析将所述异常候选分类为真阳性或假阳性（x，x，x + b）的线性分类器（12,14,16）的级联，其中x是特征向量，w是加权向量，b是模型参数，其中使用不同的权重来惩罚假阴性和假阳性，并且其中对于分级器级联的每个连续阶段使用更复杂的特征。

10. 发明申请

WO2009045470A1 SYSTEM AND METHOD FOR ROBUST SEGMENTATION OF PULMONARY NODULES OF VARIOUS DENSITIES 审中-公开
标题翻译：不同密度脉冲分布的系统和方法
公开(公告)号：WO2009045470A1
公开(公告)日：2009-04-09
申请号：PCT/US2008/011410
申请日：2008-10-02
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , KUBOTA, Toshiro , JEREBKO, Anna , SALGANICOFF, Marcos
发明人： KUBOTA, Toshiro , JEREBKO, Anna , SALGANICOFF, Marcos
IPC分类号： G06T7/00
CPC分类号： G06T7/0012 , G06T7/11 , G06T7/187 , G06T2207/30064
摘要： A method for differentiating pulmonary nodules in digitized medical images includes identifying an object of interest from a digital image of the lungs, computing (51) a first distance map of each point of the object of interest, determining (53) a seed point from the first distance map, starting from the seed point, growing (54) a first region by adding successive adjacent layers of points until a background point is reached, and partitioning (57, 58) the first region into a nodule region and a non-nodule region.
摘要翻译：用于区分数字化医学图像中的肺结节的方法包括从肺的数字图像识别感兴趣对象，计算（51）感兴趣对象的每个点的第一距离图，确定（53）来自第一距离图，从种子点开始，通过添加连续相邻的点直到达到背景点来生长（54）第一区域，并且将第一区域分割成结节区域和非结节地区。

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