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1. 发明授权

US08391579B2 Method and system for automatic detection and segmentation of axillary lymph nodes 有权
标题翻译：腋窝淋巴结自动检测和分割的方法和系统
公开(公告)号：US08391579B2
公开(公告)日：2013-03-05
申请号：US13041569
申请日：2011-03-07
申请人： Adrian Barbu , Michael Suehling , Xun (Jason) Xu , David Liu , Shaohua Kevin Zhou , Dorin Comaniciu
发明人： Adrian Barbu , Michael Suehling , Xun (Jason) Xu , David Liu , Shaohua Kevin Zhou , Dorin Comaniciu
IPC分类号： G06K9/00 , A61B5/05
CPC分类号： G06T7/11 , G06K2209/051 , G06T7/143 , G06T2207/10081 , G06T2207/30004
摘要： A method and system for automatically detecting and segmenting lymph nodes in a 3D medical image, such as a CT image, is disclosed. A plurality of lymph node center point candidates are detected in the 3D medical image. A lymph node candidate is segmented for each of the detected lymph node center point candidates. Lymph nodes are detected from the segmented lymph node candidates by verifying the segmented lymph node candidates using a trained lymph node classifier.
摘要翻译：公开了一种用于在诸如CT图像的3D医学图像中自动检测和分割淋巴结的方法和系统。在3D医学图像中检测到多个淋巴结中心点候选。为每个检测到的淋巴结中心点候选物分割淋巴结候选物。通过使用训练有素的淋巴结分类器验证分割的淋巴结候选物，从分割的淋巴结候选物中检测淋巴结。

2. 发明申请

US20110222751A1 Method and System for Automatic Detection and Segmentation of Axillary Lymph Nodes 有权
标题翻译：腋窝淋巴结自动检测和分割方法与系统
公开(公告)号：US20110222751A1
公开(公告)日：2011-09-15
申请号：US13041569
申请日：2011-03-07
申请人： Adrian Barbu , Michael Suehling , Xun (Jason) Xu , David Liu , Shaohua Kevin Zhou , Dorin Comaniciu
发明人： Adrian Barbu , Michael Suehling , Xun (Jason) Xu , David Liu , Shaohua Kevin Zhou , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06T7/11 , G06K2209/051 , G06T7/143 , G06T2207/10081 , G06T2207/30004
摘要： A method and system for automatically detecting and segmenting lymph nodes in a 3D medical image, such as a CT image, is disclosed. A plurality of lymph node center point candidates are detected in the 3D medical image. A lymph node candidate is segmented for each of the detected lymph node center point candidates. Lymph nodes are detected from the segmented lymph node candidates by verifying the segmented lymph node candidates using a trained lymph node classifier.
摘要翻译：公开了一种用于在诸如CT图像的3D医学图像中自动检测和分割淋巴结的方法和系统。在3D医学图像中检测到多个淋巴结中心点候选。为每个检测到的淋巴结中心点候选物分割淋巴结候选物。通过使用训练有素的淋巴结分类器验证分割的淋巴结候选物，从分割的淋巴结候选物中检测淋巴结。

3. 发明授权

US07940971B2 System and method for coronary digital subtraction angiography 有权
标题翻译：冠状动脉数字减影血管造影的系统和方法
公开(公告)号：US07940971B2
公开(公告)日：2011-05-10
申请号：US11779405
申请日：2007-07-18
申请人： Wei Zhang , Adrian Barbu , Simone Prummer , Martin Ostermeier , Chandan K. Reddy , Dorin Comaniciu
发明人： Wei Zhang , Adrian Barbu , Simone Prummer , Martin Ostermeier , Chandan K. Reddy , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06K9/34 , G06K2209/05 , Y10S128/922
摘要： A method and system for extracting motion-based layers from fluoroscopic image sequences are disclosed. Portions of multiple objects, such as anatomical structures, are detected in the fluoroscopic images. Motion of the objects is estimated between the images is the sequence of fluoroscopic images. The images in the fluoroscopic image sequence are then divided into layers based on the estimated motion. In a particular implementation, the coronary vessel tree and the diaphragm can be extracted in separate motion layers from coronary angiograph fluoroscopic image sequence.
摘要翻译：公开了一种从透视图像序列中提取基于运动的层的方法和系统。在透视图像中检测到多个对象的部分，例如解剖结构。在图像之间估计物体的运动是荧光图像的序列。然后，基于估计的运动将透视图像序列中的图像分成多个层。在特定的实施方案中，冠状动脉血管和隔膜可以与冠状动脉血管造影术透视图像序列分开提取。

4. 发明授权

US07702596B2 Probabilistic boosting tree framework for learning discriminative models 有权
标题翻译：用于学习歧视模型的概率增强树框架
公开(公告)号：US07702596B2
公开(公告)日：2010-04-20
申请号：US12180696
申请日：2008-07-28
申请人： Zhuowen Tu , Adrian Barbu
发明人： Zhuowen Tu , Adrian Barbu
IPC分类号： G06F15/18
CPC分类号： G06N7/005 , G06K9/6256 , G06K2209/05 , G06N99/005 , G06T7/74 , G06T7/77 , G06T2207/10072 , G06T2207/10132 , G06T2207/20132 , G06T2207/30044 , G06T2207/30048 , G06T2207/30201
摘要： A probabilistic boosting tree framework for computing two-class and multi-class discriminative models is disclosed. In the learning stage, the probabilistic boosting tree (PBT) automatically constructs a tree in which each node combines a number of weak classifiers (e.g., evidence, knowledge) into a strong classifier or conditional posterior probability. The PBT approaches the target posterior distribution by data augmentation (e.g., tree expansion) through a divide-and-conquer strategy. In the testing stage, the conditional probability is computed at each tree node based on the learned classifier which guides the probability propagation in its sub-trees. The top node of the tree therefore outputs the overall posterior probability by integrating the probabilities gathered from its sub-trees. In the training stage, a tree is recursively constructed in which each tree node is a strong classifier. The input training set is divided into two new sets, left and right ones, according to the learned classifier. Each set is then used to train the left and right sub-trees recursively.
摘要翻译：公开了一种用于计算两类和多类判别模型的概率提升树框架。在学习阶段，概率增强树（PBT）自动构建一棵树，其中每个节点将多个弱分类器（例如，证据，知识）组合成强分类器或条件后验概率。 PBT通过划分和征服策略通过数据增加（例如，树扩展）来接近目标后验分布。在测试阶段，基于学习的分类器在每个树节点计算条件概率，该分类器引导其子树中的概率传播。因此，树的顶级节点通过整合从其子树收集的概率输出总后验概率。在训练阶段，递归地构造树，其中每个树节点是强分类器。根据学习分类器，输入训练集分为左右两组。然后每组用于递归地训练左右子树。

5. 发明申请

US20090080729A1 Method and system for evaluating image segmentation based on visibility 有权
标题翻译：基于可见度评估图像分割的方法和系统
公开(公告)号：US20090080729A1
公开(公告)日：2009-03-26
申请号：US12231634
申请日：2008-09-04
申请人： Wei Zhang , Adrian Barbu , Simone Prummer , Martin Ostermeier , Dorin Comaniciu
发明人： Wei Zhang , Adrian Barbu , Simone Prummer , Martin Ostermeier , Dorin Comaniciu
IPC分类号： A61B5/00
CPC分类号： G06T7/60 , G06T7/11 , G06T7/174 , G06T7/246 , G06T2207/10121 , G06T2207/30048 , G06T2207/30101
摘要： A method and system for evaluating image segmentation is disclosed. In order to quantitatively evaluate an image segmentation technique, synthetic image data is generated and the synthetic image data is segmented to extract an object using the segmentation technique. This segmentation results in a foreground containing the extracted object and a background. The visibility of the extracted object is quantitatively measured based on the intensity distributions of the segmented foreground and background. The visibility is quantitatively measured by calculating the Jeffries-Matusita distance between the foreground and background intensity distributions. This method can be used to evaluate segmentation of vessels in fluoroscopic image sequences by coronary digital subtraction angiography (DSA).
摘要翻译：公开了一种用于评估图像分割的方法和系统。为了定量评估图像分割技术，生成合成图像数据，并且使用分割技术对合成图像数据进行分割以提取对象。该分割导致包含提取的对象和背景的前景。基于分割的前景和背景的强度分布，定量地测量提取的对象的可视性。通过计算前景和背景强度分布之间的Jeffries-Matusita距离来定量测量可见度。该方法可用于通过冠状动脉数字减影血管造影（DSA）评估荧光镜图像序列中血管的分割。

6. 发明申请

US20090062641A1 Method and system for catheter detection and tracking in a fluoroscopic image sequence 失效
标题翻译：荧光镜图像序列中导管检测和跟踪的方法和系统
公开(公告)号：US20090062641A1
公开(公告)日：2009-03-05
申请号：US12221732
申请日：2008-08-06
申请人： Adrian Barbu , Wei Zhang , Norbert Strobel , Adam K. Galant , Ulrich Bill , Dorin Comaniciu
发明人： Adrian Barbu , Wei Zhang , Norbert Strobel , Adam K. Galant , Ulrich Bill , Dorin Comaniciu
IPC分类号： A61B5/05
CPC分类号： A61B34/20 , A61B2090/376
摘要： A method and system for detecting and tracking an ablation catheter tip in a fluoroscopic image sequence is disclosed. Catheter tip candidates are detected in each frame of the fluoroscopic image sequence using marginal space learning. The detected catheter tip candidates are then tracked over all the frames of the fluoroscopic image sequence in order to determine an ablation catheter tip location in each frame.
摘要翻译：公开了一种用于在透视图像序列中检测和跟踪消融导管尖端的方法和系统。使用边缘空间学习在荧光镜像图像序列的每个帧中检测导管尖端候选。然后在荧光镜像图像序列的所有帧上跟踪检测到的导管尖端候选，以便确定每个帧中的消融导管尖端位置。

7. 发明申请

US20080262814A1 Method and system for generating a four-chamber heart model 有权
标题翻译：用于产生四室心脏模型的方法和系统
公开(公告)号：US20080262814A1
公开(公告)日：2008-10-23
申请号：US12082143
申请日：2008-04-09
申请人： Yefeng Zheng , Adrian Barbu , Bogdan Georgescu , Michael Lynch , Michael Scheuering , Dorin Comaniciu
发明人： Yefeng Zheng , Adrian Barbu , Bogdan Georgescu , Michael Lynch , Michael Scheuering , Dorin Comaniciu
IPC分类号： G06G7/60
CPC分类号： G06F19/3437 , G06F19/00 , G06F19/34 , G06T2207/30048 , G16H50/50
摘要： A method and system for building a statistical four-chamber heart model from 3D volumes is disclosed. In order to generate the four-chamber heart model, each chamber is modeled using an open mesh, with holes at the valves. Based on the image data in one or more 3D volumes, meshes are generated and edited for the left ventricle (LV), left atrium (LA), right ventricle (RV), and right atrium (RA). Resampling to enforce point correspondence is performed during mesh editing. Important anatomic landmarks in the heart are explicitly represented in the four-chamber heart model of the present invention.
摘要翻译：公开了一种用于从3D体积构建统计四室心脏模型的方法和系统。为了产生四腔心脏模型，每个腔室都使用开放的网格进行建模，在阀门处有孔。基于一个或多个3D体积中的图像数据，为左心室（LV），左心房（LA），右心室（RV）和右心房（RA）生成并编辑网格。在网格编辑期间执行重新采样以强制点对应。在本发明的四腔心脏模型中明确地表示心脏中的重要解剖学标志。

8. 发明申请

US20080080754A1 System and Method For Online Optimization of Guidewire Visibility In Fluoroscopic Systems 有权
标题翻译：在荧光镜系统中在线优化导丝可视性的系统和方法
公开(公告)号：US20080080754A1
公开(公告)日：2008-04-03
申请号：US11860588
申请日：2007-09-25
申请人： Adrian Barbu , Vassilis Athitsos , Bogdan Georgescu , Peter Durlak , Dorin Comaniciu
发明人： Adrian Barbu , Vassilis Athitsos , Bogdan Georgescu , Peter Durlak , Dorin Comaniciu
IPC分类号： G06K9/60 , A61M29/02
CPC分类号： A61B6/12 , G06T5/002 , G06T5/003 , G06T5/20 , G06T2207/10121 , G06T2207/30021 , G06T2207/30048 , G06T2207/30101
摘要： A method for online optimization of guidewire visibility in fluoroscopic images includes providing an digitized image acquired from a fluoroscopic imaging system, the image comprising an array of intensities corresponding to a 2-dimensional grid of pixels, detecting a guidewire in the fluoroscopic image, enhancing the visibility of the guidewire in the fluoroscopic image, calculating a visibility measure of the guidewire in the fluoroscopic image, and readjusting acquisition parameters of the fluoroscopic imaging system wherein the guidewire visibility is improved.
摘要翻译：一种用于在荧光镜图像中在线优化导丝可视性的方法包括提供从荧光成像系统获取的数字化图像，所述图像包括对应于像素的二维网格的强度阵列，检测透视图像中的导丝，增强在透视图像中的导丝的可视性，计算透视图像中的导丝的可见度测量，以及重新调整荧光镜成像系统的获取参数，其中导丝可视性得到改善。

9. 发明申请

US20060079761A1 Method for detecting polyps in a three dimensional image volume 有权
公开(公告)号：US20060079761A1
公开(公告)日：2006-04-13
申请号：US11244798
申请日：2005-10-06
申请人： Zhuowen Tu , Xiang Zhou , Adrian Barbu , Luca Bogoni , Dorin Comaniciu
发明人： Zhuowen Tu , Xiang Zhou , Adrian Barbu , Luca Bogoni , Dorin Comaniciu
IPC分类号： A61B5/05 , G06K9/00
CPC分类号： G06K9/4614 , G06K9/6206 , G06K9/6214 , G06K9/6257 , G06K2209/053 , G06T7/0012 , G06T2207/10072 , G06T2207/30032
摘要： A method for detecting target objects in a three dimensional (3D) image volume of an anatomical structure is disclosed. A set of candidate locations in the image volume are obtained. For each candidate location, sub-volumes of at least two different scales are cropped out. Each sub-volume comprises a plurality of voxels. For each of the sub-volumes, each sub-volume is rotated in at least two different orientations. A shape classifier is applied to each sub-volume. If the voxels in the sub-volume pass the shape classifier, a gradient direction is computed for the voxels. If the gradient direction for the voxels is one of a predefined orientation, a probability classifier is applied to the voxels. A probability measure computed by the probability classifier as a confidence measure is used for the sub-volume. If the confidence measure is above a predetermined threshold value, the sub-volume is determined to contain the target object.

10. 发明授权

US08396533B2 Method and system for catheter detection and tracking in a fluoroscopic image sequence 失效
公开(公告)号：US08396533B2
公开(公告)日：2013-03-12
申请号：US12221732
申请日：2008-08-06
申请人： Adrian Barbu , Wei Zhang , Norbert Strobel , Adam K. Galant , Ulrich Bill , Dorin Comaniciu
发明人： Adrian Barbu , Wei Zhang , Norbert Strobel , Adam K. Galant , Ulrich Bill , Dorin Comaniciu
IPC分类号： A61B5/05
CPC分类号： A61B34/20 , A61B2090/376
摘要： A method and system for detecting and tracking an ablation catheter tip in a fluoroscopic image sequence is disclosed. Catheter tip candidates are detected in each frame of the fluoroscopic image sequence using marginal space learning. The detected catheter tip candidates are then tracked over all the frames of the fluoroscopic image sequence in order to determine an ablation catheter tip location in each frame.

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