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1.发明申请Method and System for Multi-Organ Segmentation Using Learning-Based Segmentation and Level Set Optimization 有权使用基于学习的分割和级别集优化的多器官分割的方法和系统公开(公告)号：US20120230572A1
公开(公告)日：2012-09-13
申请号：US13416508
申请日：2012-03-09
申请人： Timo Kohlberger , Michal Sofka , Jens Wetzl , Jingdan Zhang , Shaohua Kevin Zhou , Neil Birkbeck , Jerome Declerck , Jens Kaftan
发明人： Timo Kohlberger , Michal Sofka , Jens Wetzl , Jingdan Zhang , Shaohua Kevin Zhou , Neil Birkbeck , Jerome Declerck , Jens Kaftan
IPC分类号： G06K9/34
CPC分类号： G06T7/0083 , G06K9/6209 , G06T7/12 , G06T2207/10081 , G06T2207/20081 , G06T2207/20161 , G06T2207/30048 , G06T2207/30084
摘要： A method and system for automatic multi-organ segmentation in a 3D image, such as a 3D computed tomography (CT) volume using learning-base segmentation and level set optimization is disclosed. A plurality of meshes are segmented in a 3D medical image, each mesh corresponding to one of a plurality of organs. A level set in initialized by converting each of the plurality of meshes to a respective signed distance map. The level set optimized by refining the signed distance map corresponding to each one of the plurality of organs to minimize an energy function.
摘要翻译： 公开了一种3D图像自动多器官分割的方法和系统，例如使用学习型基础分割和级别集优化的3D计算机断层摄影（CT）体积。 在3D医学图像中分割多个网格，每个网格对应于多个器官中的一个。 通过将多个网格中的每一个转换为相应的带符号距离图来初始化的级别。 通过精化对应于多个器官中的每一个的有符号距离图来最优化能量函数来优化的级别集合。
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2.发明授权Method and system for multi-organ segmentation using learning-based segmentation and level set optimization 有权使用基于学习的分割和水平集优化的多器官分割的方法和系统公开(公告)号：US09042620B2
公开(公告)日：2015-05-26
申请号：US13416508
申请日：2012-03-09
申请人： Timo Kohlberger , Michal Sofka , Jens Wetzl , Jingdan Zhang , Shaohua Kevin Zhou , Neil Birkbeck , Jerome Declerck , Jens Kaftan
发明人： Timo Kohlberger , Michal Sofka , Jens Wetzl , Jingdan Zhang , Shaohua Kevin Zhou , Neil Birkbeck , Jerome Declerck , Jens Kaftan
CPC分类号： G06T7/0083 , G06K9/6209 , G06T7/12 , G06T2207/10081 , G06T2207/20081 , G06T2207/20161 , G06T2207/30048 , G06T2207/30084
摘要： A method and system for automatic multi-organ segmentation in a 3D image, such as a 3D computed tomography (CT) volume using learning-base segmentation and level set optimization is disclosed. A plurality of meshes are segmented in a 3D medical image, each mesh corresponding to one of a plurality of organs. A level set in initialized by converting each of the plurality of meshes to a respective signed distance map. The level set optimized by refining the signed distance map corresponding to each one of the plurality of organs to minimize an energy function.
摘要翻译： 公开了一种3D图像自动多器官分割的方法和系统，例如使用学习型基础分割和级别集优化的3D计算机断层摄影（CT）体积。 在3D医学图像中分割多个网格，每个网格对应于多个器官中的一个。 通过将多个网格中的每一个转换为相应的带符号距离图来初始化的级别。 通过精化对应于多个器官中的每一个的有符号距离图来最优化能量函数来优化的级别集合。
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公开(公告)号：US08860715B2
公开(公告)日：2014-10-14
申请号：US13228505
申请日：2011-09-09
申请人： Neil Birkbeck , Michal Sofka , Shaohua Kevin Zhou
发明人： Neil Birkbeck , Michal Sofka , Shaohua Kevin Zhou
CPC分类号： G06K9/6256 , G06K9/00986 , G06K9/4614 , G06K9/6282
摘要： A method and system for evaluating probabilistic boosting trees is disclosed. In an embodiment, input data is received at a graphics processing unit. A weighted empirical distribution associated with each node of the probabilistic boosting tree is determined using a stack implementation. The weighted empirical distribution associated with each node is added to a total posterior distribution value.
摘要翻译： 公开了一种用于评估概率提升树的方法和系统。 在一个实施例中，在图形处理单元处接收输入数据。 使用堆栈实现来确定与概率增强树的每个节点相关联的加权经验分布。 将与每个节点相关联的加权经验分布加到总后验分布值中。
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公开(公告)号：US20120069003A1
公开(公告)日：2012-03-22
申请号：US13228505
申请日：2011-09-09
申请人： Neil Birkbeck , Michal Sofka , Shaohua Kevin Zhou
发明人： Neil Birkbeck , Michal Sofka , Shaohua Kevin Zhou
CPC分类号： G06K9/6256 , G06K9/00986 , G06K9/4614 , G06K9/6282
摘要： A method and system for evaluating probabilistic boosting trees is disclosed. In an embodiment, input data is received at a graphics processing unit. A weighted empirical distribution associated with each node of the probabilistic boosting tree is determined using a stack implementation. The weighted empirical distribution associated with each node is added to a total posterior distribution value.
摘要翻译： 公开了一种用于评估概率提升树的方法和系统。 在一个实施例中，在图形处理单元处接收输入数据。 使用堆栈实现来确定与概率增强树的每个节点相关联的加权经验分布。 将与每个节点相关联的加权经验分布加到总后验分布值中。
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公开(公告)号：US09495752B2
公开(公告)日：2016-11-15
申请号：US13953825
申请日：2013-07-30
申请人： Dijia Wu , Neil Birkbeck , Michal Sofka , Meizhu Liu , Shaohua Kevin Zhou
发明人： Dijia Wu , Neil Birkbeck , Michal Sofka , Meizhu Liu , Shaohua Kevin Zhou
IPC分类号： G06T7/00
CPC分类号： G06T7/0079 , G06T7/0012 , G06T7/11 , G06T7/143 , G06T2207/10072 , G06T2207/20041 , G06T2207/20076
摘要： Multiple object segmentation is performed for three-dimensional computed tomography. The adjacent objects are individually segmented. Overlapping regions or locations designated as belonging to both objects may be identified. Confidence maps for the individual segmentations are used to label the locations of the overlap as belonging to one or the other object, not both. This re-segmentation is applied for the overlapping local, and not other locations. Confidence maps in re-segmentation and application just to overlap locations may be used independently of each other or in combination.
摘要翻译： 对三维计算机断层摄影进行多目标分割。 相邻的对象被单独分段。 可以识别指定为属于两个对象的重叠区域或位置。 用于各个分段的置信图用于将重叠的位置标记为属于一个或另一个对象，而不是两者。 这种重新分割被应用于重叠的本地，而不是其他位置。 重新分割和应用恰好重叠位置的置信度图可以彼此独立地或组合地使用。
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公开(公告)号：US20140086465A1
公开(公告)日：2014-03-27
申请号：US13953825
申请日：2013-07-30
申请人： Dijia Wu , Neil Birkbeck , Michal Sofka , Meizhu Liu , Shaohua Kevin Zhou
发明人： Dijia Wu , Neil Birkbeck , Michal Sofka , Meizhu Liu , Shaohua Kevin Zhou
IPC分类号： G06T7/00
CPC分类号： G06T7/0079 , G06T7/0012 , G06T7/11 , G06T7/143 , G06T2207/10072 , G06T2207/20041 , G06T2207/20076
摘要： Multiple object segmentation is performed for three-dimensional computed tomography. The adjacent objects are individually segmented. Overlapping regions or locations designated as belonging to both objects may be identified. Confidence maps for the individual segmentations are used to label the locations of the overlap as belonging to one or the other object, not both. This re-segmentation is applied for the overlapping local, and not other locations. Confidence maps in re-segmentation and application just to overlap locations may be used independently of each other or in combination.
摘要翻译： 对三维计算机断层摄影进行多目标分割。 相邻的对象被单独分段。 可以识别指定为属于两个对象的重叠区域或位置。 用于各个分段的置信图用于将重叠的位置标记为属于一个或另一个对象，而不是两者。 这种重新分割被应用于重叠的本地，而不是其他位置。 重新分割和应用恰好重叠位置的置信度图可以彼此独立地或组合地使用。
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7.发明授权Method and system for multiple object detection by sequential Monte Carlo and hierarchical detection network 有权通过连续蒙特卡洛和分层检测网络进行多目标检测的方法和系统公开(公告)号：US08605969B2
公开(公告)日：2013-12-10
申请号：US13080964
申请日：2011-04-06
IPC分类号： G06K9/00
CPC分类号： G06K9/6297 , A61B8/0816 , G06K9/6278 , G06K2209/051 , G06T7/70 , G06T2207/10132 , G06T2207/20016 , G06T2207/20076 , G06T2207/30016 , G06T2207/30048 , G06T2207/30056
摘要： A method and system for detecting multiple objects in an image is disclosed. A plurality of objects in an image is sequentially detected in an order specified by a trained hierarchical detection network. In the training of the hierarchical detection network, the order for object detection is automatically determined. The detection of each object in the image is performed by obtaining a plurality of sample poses for the object from a proposal distribution, weighting each of the plurality of sample poses based on an importance ratio, and estimating a posterior distribution for the object based on the weighted sample poses.
摘要翻译： 公开了一种用于检测图像中的多个对象的方法和系统。 图像中的多个对象以训练有序的分层检测网络指定的顺序被顺序地检测。 在分层检测网络的训练中，自动确定对象检测的顺序。 通过从提案分布中获得用于对象的多个样本姿势，基于重要性比例对多个样本姿势进行加权，并基于该对象的后验分布来估计图像中的每个对象的检测 加权样本姿势。
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公开(公告)号：US20110243407A1
公开(公告)日：2011-10-06
申请号：US13080891
申请日：2011-04-06
申请人： Michal Sofka , Kristof Ralovich , Jingdan Zhang , Shaohua Kevin Zhou , Gianluca Paladini , Dorin Comaniciu
发明人： Michal Sofka , Kristof Ralovich , Jingdan Zhang , Shaohua Kevin Zhou , Gianluca Paladini , Dorin Comaniciu
CPC分类号： G06K9/6256 , G06F19/00 , G06F19/321 , G06T7/0012 , G06T2200/16 , G06T2207/20016
摘要： For cloud-based computer assisted detection, hierarchal detection is used, allowing detection on data at progressively greater resolutions. Detected locations at coarser resolutions are used to limit the data transmitted at greater resolutions. Data is only transmitted for neighborhoods around the previously detected locations. Subsequent detection using higher resolution data refines the locations, but only for regions associated with previous detection. By limiting the number and/or size of regions provided at greater resolutions based on the previous detection, the progressive transmission avoids transmission of some data. Additionally, or alternatively, lossy compression may be used without or with minimal reduction in detection sensitivity.
摘要翻译： 对于基于云的计算机辅助检测，使用层次检测，允许以逐渐更大的分辨率检测数据。 较粗分辨率下的检测位置用于限制以更高分辨率传输的数据。 仅针对先前检测到的位置周围的邻域发送数据。 使用较高分辨率数据的后续检测会优化位置，但仅适用于与先前检测相关的区域。 通过基于先前的检测限制以更大分辨率提供的区域的数量和/或尺寸，逐行传输避免了一些数据的传输。 另外或者可选地，可以使用有损压缩，而不用或以最小的检测灵敏度降低。
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公开(公告)号：US08811697B2
公开(公告)日：2014-08-19
申请号：US13080891
申请日：2011-04-06
申请人： Michal Sofka , Kristof Ralovich , Jingdan Zhang , Shaohua Kevin Zhou , Gianluca Paladini , Dorin Comaniciu
发明人： Michal Sofka , Kristof Ralovich , Jingdan Zhang , Shaohua Kevin Zhou , Gianluca Paladini , Dorin Comaniciu
CPC分类号： G06K9/6256 , G06F19/00 , G06F19/321 , G06T7/0012 , G06T2200/16 , G06T2207/20016
摘要： For cloud-based computer assisted detection, hierarchal detection is used, allowing detection on data at progressively greater resolutions. Detected locations at coarser resolutions are used to limit the data transmitted at greater resolutions. Data is only transmitted for neighborhoods around the previously detected locations. Subsequent detection using higher resolution data refines the locations, but only for regions associated with previous detection. By limiting the number and/or size of regions provided at greater resolutions based on the previous detection, the progressive transmission avoids transmission of some data. Additionally, or alternatively, lossy compression may be used without or with minimal reduction in detection sensitivity.
摘要翻译： 对于基于云的计算机辅助检测，使用层次检测，允许以逐渐更大的分辨率检测数据。 较粗分辨率下的检测位置用于限制以更高分辨率传输的数据。 仅针对先前检测到的位置周围的邻域发送数据。 使用较高分辨率数据的后续检测会优化位置，但仅适用于与先前检测相关的区域。 通过基于先前的检测限制以更大分辨率提供的区域的数量和/或尺寸，逐行传输避免了一些数据的传输。 另外或者可选地，可以使用有损压缩，而不用或以最小的检测灵敏度降低。
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10.发明申请Method and System for Multiple Object Detection by Sequential Monte Carlo and Hierarchical Detection Network 有权通过连续蒙特卡罗和分层检测网络进行多目标检测的方法与系统公开(公告)号：US20110243386A1
公开(公告)日：2011-10-06
申请号：US13080964
申请日：2011-04-06
IPC分类号： G06K9/00
CPC分类号： G06K9/6297 , A61B8/0816 , G06K9/6278 , G06K2209/051 , G06T7/70 , G06T2207/10132 , G06T2207/20016 , G06T2207/20076 , G06T2207/30016 , G06T2207/30048 , G06T2207/30056
摘要： A method and system for detecting multiple objects in an image is disclosed. A plurality of objects in an image is sequentially detected in an order specified by a trained hierarchical detection network. In the training of the hierarchical detection network, the order for object detection is automatically determined. The detection of each object in the image is performed by obtaining a plurality of sample poses for the object from a proposal distribution, weighting each of the plurality of sample poses based on an importance ratio, and estimating a posterior distribution for the object based on the weighted sample poses.
摘要翻译： 公开了一种用于检测图像中的多个对象的方法和系统。 图像中的多个对象以训练有序的分层检测网络指定的顺序被顺序地检测。 在分层检测网络的训练中，自动确定对象检测的顺序。 通过从提案分布中获得用于对象的多个样本姿势，基于重要性比例对多个样本姿势进行加权，并基于该对象的后验分布来估计图像中的每个对象的检测 加权样本姿势。
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