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1. 发明申请

WO2007035285A1 SYSTEM AND METHOD FOR AUTOMATIC EXTRACTION OF SPINAL CORD FROM 3D VOLUMETRIC IMAGES 审中-公开
标题翻译：用于从3D体积图像自动提取脊髓的系统和方法
公开(公告)号：WO2007035285A1
公开(公告)日：2007-03-29
申请号：PCT/US2006/035032
申请日：2006-09-11
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , PERIASWAMY, Senthil , OU, Yangming , JEREBKO, Anna
发明人： PERIASWAMY, Senthil , OU, Yangming , JEREBKO, Anna
IPC分类号： G06T5/00 , G06T7/00
CPC分类号： G06T7/0012 , G06T7/12 , G06T7/143 , G06T2207/10088 , G06T2207/30012
摘要： A method of extracting a spinal cord from a digitized medical image includes providing a digitized medical image, selecting (31) a set of points from said image as candidates for belonging to the spine, initializing (33) a probability for each candidate point to belong to said spine, minimizing (34) a weighted sum of square differences of image intensities of said candidate points and intensities determined by a mathematical model of said spine to estimate parameter values for said model, calculating (36) a residual error for each point from the differences at each point between an estimated image intensity calculated (35) from said estimated model parameters and an actual image intensity, updating (37) the candidate point probabilities from said residual errors, and eliminating candidate points whose probability falls below a predetermined value.
摘要翻译：从数字化医学图像提取脊髓的方法包括提供数字化医学图像，从所述图像中选择（31）一组点作为属于脊柱的候选，初始化（33）每个候选点属于的概率对所述脊柱进行最小化（34）所述候选点的图像强度的平方差的加权和和由所述脊柱的数学模型确定的强度，以估计所述模型的参数值，计算（36）每个点的残差从所述估计模型参数计算出的估计图像强度（35）与实际图像强度之间的每个点处的差异，从所述残余误差更新（37）候选点概率，以及消除概率低于预定值的候选点。

2. 发明申请

WO2009045461A1 SYSTEM AND METHOD FOR JOINT CLASSIFICATION USING FEATURE SPACE CLUSTER LABELS 审中-公开
标题翻译：使用特征空间聚类标签进行联合分类的系统和方法
公开(公告)号：WO2009045461A1
公开(公告)日：2009-04-09
申请号：PCT/US2008/011399
申请日：2008-10-02
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , JEREBKO, Anna
发明人： JEREBKO, Anna
IPC分类号： G06K9/62
CPC分类号： G06K9/622 , G06K9/6262 , G06K2209/053
摘要： A method for training a classifier for use in a computer aided detection system includes providing (41) a training set of images acquired from a plurality of patients, each said image including one or more candidate regions that have been identified (42) as suspicious by a candidate generation step of a computer aided detection system, and wherein each said image has been manually annotated to identify lesions, using said training set to train (44) a classifier adapted for identifying a candidate region as a lesion or non-lesion, clustering (46) candidate regions having similar features for each patient individually, and modifying (47) said trained classifier decision boundary with an additional classification step incorporating said individual candidate region clustering
摘要翻译：一种用于训练用于计算机辅助检测系统的分类器的方法包括提供（41）从多个患者获取的图像的训练集合，每个所述图像包括已被识别（42）的一个或多个候选区域可疑由计算机辅助检测系统的候选生成步骤，并且其中每个所述图像已被手动注释以识别病变，使用所述训练集来训练（44）适于将候选区域识别为病变或非病变的分类器，聚类（46）个别地具有每个患者具有相似特征的候选区域，以及用包括所述单个候选区域聚类的附加分类步骤修改（47）所述经训练的分类器决策边界

3. 发明申请

WO2008002633A2 SYSTEM AND METHOD FOR DETECTION OF BREAST MASSES AND CALCIFICATIONS USING THE TOMOSYNTHESIS PROJECTION AND RECONSTRUCTED IMAGES 审中-公开
标题翻译：用于检测乳房质量的系统和方法以及使用酪氨酸激酶预测和重构图像的计算
公开(公告)号：WO2008002633A2
公开(公告)日：2008-01-03
申请号：PCT/US2007/014983
申请日：2007-06-26
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , JEREBKO, Anna , KRISHNAN, Arun , ZHOU, Xiang
发明人： JEREBKO, Anna , KRISHNAN, Arun , ZHOU, Xiang
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 (10) a plurality of 2-dimensional (2D) digital X-ray projectional breast images acquired from different viewing angles, extracting (12) candidate lesions and 2D features from said 2D projectional images, computing spicularity characteristics of said candidate lesions, including location, periodicity, and amplitude, applying (13) 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 (14) a synthetic 2D slice for each X-ray image wherein malignant regions are indicated by ellipses on a non-malignant background, and constructing (15) 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质量和钙化的方法，包括提供（10）从不同视角获取的多个二维（2D）数字X射线投射乳房图像，从所述的多个视点中提取（12）候选病变和2D特征 2D投影图像，计算所述候选病变的细胞特征，包括位置，周期和幅度，将（13）学习算法应用于所述候选病变以预测所述病变的恶性肿瘤的概率，从所述学习算法接收检测的概率图对于每个乳房图像，所述检测包括将像素与与恶性肿瘤相关联的概率相关联，为每个X射线图像创建（14）合成2D切片，其中恶性区域由非恶性背景上的椭圆表示，并构建（15）来自所述2D合成切片的合成的三维（3D）图像体积

4. 发明申请

WO2008027393A3 COMPUTER AIDED DETECTION OF BONE METASTASIS 审中-公开
公开(公告)号：WO2008027393A3
公开(公告)日：2008-03-06
申请号：PCT/US2007/018938
申请日：2007-08-28
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , JEREBKO, Anna , ZHOU, Xiang
发明人： JEREBKO, Anna , ZHOU, Xiang
IPC分类号： G06T7/00
摘要： A method for automatic detection of regions of suspicion within a region of interest includes acquiring image data sets (31) from an image scanner (32) or an image database (30). The region of interest is segmented within the image data sets. Each of the image data sets are co-registered to a common coordinate system. Each image data set is individually examined to identify the location of regions of suspicion with reference to the common coordinate system within the image data sets. Each of the image data sets are individually inspected at the locations of the identified regions of suspicion on the common coordinate system to obtain information pertaining to the regions of suspicion. It is determined, based on the obtained information pertaining to the regions of suspicion, whether the regions of suspicion are abnormalities.

5. 发明申请

WO2007044508A2 SYSTEM AND METHOD FOR WHOLE BODY LANDMARK DETECTION, SEGMENTATION AND CHANGE QUANTIFICATION IN DIGITAL IMAGES 审中-公开
标题翻译：用于数字图像中整体地标检测，分割和改变量化的系统和方法
公开(公告)号：WO2007044508A2
公开(公告)日：2007-04-19
申请号：PCT/US2006/039109
申请日：2006-10-05
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , HUANG, Xiaolei , ZHOU, Xiang , JEREBKO, Anna , KRISHNAN, Arun , GUAN, Haiying , KUBOTA, Toshiro , POTESIL, Vaclav
发明人： HUANG, Xiaolei , ZHOU, Xiang , JEREBKO, Anna , KRISHNAN, Arun , GUAN, Haiying , KUBOTA, Toshiro , POTESIL, Vaclav
IPC分类号： G06T7/00
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 (11) a training set comprising a plurality of digitized whole-body images, providing labels (12) on anatomical landmarks in each image of said training set, aligning (13) each said training set image, generating (14) 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 (15) 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（11）包括多个数字化全身图像的训练集，在所述训练集的每个图像中的解剖标志上提供标签（12），对齐（ 13）每个所述训练集图像，通过将所述对齐的训练体裁剪成不同空间尺度的一个或多个裁剪窗口来生成（14）每个地标的正面和负面训练示例，并且使用所述正面和反面示例来训练（15）检测器，用于从粗分辨率到高分辨率的一个或多个空间尺度范围内的每个界标，其中记录粗分辨率检测器的裁剪窗口和精细分辨率检测器之间的空间关系。

6. 发明申请

WO2004081874A2 SYSTEMS AND METHODS FOR PROVIDING AUTOMATIC 3D LESION SEGMENTATION AND MEASUREMENTS 审中-公开
标题翻译：提供自动三维分割和测量的系统和方法
公开(公告)号：WO2004081874A2
公开(公告)日：2004-09-23
申请号：PCT/US2004/007302
申请日：2004-03-11
申请人： SIEMENS MEDICAL SOLUTIONS USA INC. , JEREBKO, Anna , KRISHNAN, Arun , BOGONI, Luca
发明人： JEREBKO, Anna , KRISHNAN, Arun , BOGONI, Luca
IPC分类号： G06T7/00
CPC分类号： G06T7/62 , G06T7/12 , G06T2200/04 , G06T2207/10072 , G06T2207/20104 , G06T2207/30032 , G06T2207/30064
摘要： Systems and methods are provided for automatic 3D segmentation of abnormal anatomical structures such as colonic polyps, aneurisms or lung nodules, etc., in 3D medical imaging applications. For example, systems and methods for 3D lesion segmentation implement a centroid-based coordinate transformation (e.g., spherical transformation, ellipsoidal transformation, etc.) to transform a 3D surface of the lesion from an original volume space into, e.g., a spherical or ellipsoidal coordinate space, followed by interpolation of the transformed lesion surface to enable accurate determination of a boundary between a lesion and surrounding normal structures.
摘要翻译：提供了用于3D医学成像应用中的异常解剖结构如结肠息肉，动脉瘤或肺结节等的自动3D分割的系统和方法。例如，用于3D损伤分割的系统和方法实现基于质心的坐标变换（例如，球形变换，椭圆形变换等），以将损伤的3D表面从原始体积空间转换成例如球形或椭圆形坐标空间，然后插入转化的病变表面，以便能够准确确定病变和周围正常结构之间的边界。

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

WO2006028965A3 SYSTEM AND METHOD FOR SEGMENTING A STRUCTURE OF INTEREST USING AN INTERPOLATION OF A SEPARATING SURFACE IN AN AREA OF ATTACHMENT TO A STRUCTURE HAVING SIMILAR PROPERTIES 审中-公开
公开(公告)号：WO2006028965A3
公开(公告)日：2006-03-16
申请号：PCT/US2005/031321
申请日：2005-09-02
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , JEREBKO, Anna , BOGONI, Luca , LAKARE, Sarang
发明人： JEREBKO, Anna , BOGONI, Luca , LAKARE, Sarang
IPC分类号： G06T5/00 , A61B6/00
摘要： A system and method for segmenting a structure of interest are provided. The method comprises: segmenting a surface of the structure of interest (320); and interpolating a border of the structure of interest, wherein the border separates the structure of interest from other structures having similar properties (330).

9. 发明申请

WO2005091217A1 SEGMENTATION OF STRUCTURES BASED ON CURVATURE SLOPE 审中-公开
标题翻译：基于曲率坡度的结构分段
公开(公告)号：WO2005091217A1
公开(公告)日：2005-09-29
申请号：PCT/US2005/006901
申请日：2005-03-03
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , JEREBKO, Anna , BOGONI, Luca , LAKARE, Sarang
发明人： JEREBKO, Anna , BOGONI, Luca , LAKARE, Sarang
IPC分类号： G06T5/00
CPC分类号： G06K9/4609 , G06T7/10
摘要： A system and method for segmentation of structures in an image based on curvature slope are provided, the system including a processor, a curvature slope unit in signal communication with the processor for computing curvature slopes in the image and discarding voxels having a slope not satisfying specifications, and a segmentation unit in signal communication with the processor for locating connected clusters of retained voxels; and the method including computing curvature slopes in the image, discarding voxels having a slope not satisfying specifications, and locating connected clusters of retained voxels.
摘要翻译：提供了一种基于曲率斜率来分割图像中的结构的系统和方法，该系统包括处理器，与处理器进行信号通信的曲率斜率单元，用于计算图像中的曲率斜率，并且丢弃具有不满足规格的斜率的体素以及与处理器进行信号通信的分割单元，用于定位保持的体素的连接簇; 并且该方法包括计算图像中的曲率斜率，丢弃具有不满足规格的斜率的体元，以及定位保留体素的连接簇。

10. 发明申请

WO2009020630A2 SYSTEM AND METHOD FOR ROBUST SEGMENTATION OF TUBULAR STRUCTURES IN 2D AND 3D IMAGES 审中-公开
标题翻译：用于2D和3D图像中管状结构的鲁棒分段的系统和方法
公开(公告)号：WO2009020630A2
公开(公告)日：2009-02-12
申请号：PCT/US2008/009470
申请日：2008-08-07
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , FAHMI, Rachid , WOLF, Matthias , JEREBKO, Anna
发明人： FAHMI, Rachid , WOLF, Matthias , JEREBKO, Anna
IPC分类号： G06T7/00 , A61B5/00
CPC分类号： G06K9/6207 , G06K9/34 , G06T7/0012 , G06T7/149 , G06T2207/20116 , G06T2207/30101
摘要： A method for segmenting tubular structures in medical images includes providing (62) at least a start point and an end point in a digital image volume, minimizing (63) an action surface U 0 ( p ) which, at each image point p , corresponds to a minimal energy integrated along a path that starts at start point p0 and ends at p, sliding back (64) on the minimal action surface from an end point to the start point to find a minimal path connecting the terminal points, initializing(65) a level set function with points on the minimal path, and evolving (66) the level set function to find a surface of a structure about the minimal path, wherein the level set function is constrained to be close to a signed distance function and wherein the level set function is prevented from growing wider than a predetermined diameter R , wherein the surface about the minimal path defines a tubular structure.
摘要翻译：用于在医学图像中分割管状结构的方法包括在数字图像体积中提供（62）至少起始点和终点，使（63）最小化（63）在每个图像点处对应于沿起始点p0开始并以p结束的路径积分的最小能量从终点到起始点在最小动作表面上滑回（64），以找到连接终点的最小路径，初始化（65）a 在最小路径上具有点的级别集函数，并且演化（66）级集合函数以找到关于最小路径的结构的表面，其中级集合函数被约束为接近带符号距离函数，并且其中级防止设定功能比预定直径R更宽，其中围绕最小路径的表面限定管状结构。

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