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1.发明授权Method and system for segmentation of the prostate in 3D magnetic resonance images 有权3D磁共振图像前列腺分割方法与系统公开(公告)号：US09025841B2
公开(公告)日：2015-05-05
申请号：US12946909
申请日：2010-11-16
申请人： Michael Wels , Michael Suehling , Michael Kelm , Sascha Seifert , Maria Jimena Costa , Alexander Cavallaro , Martin Huber , Dorin Comaniciu
发明人： Michael Wels , Michael Suehling , Michael Kelm , Sascha Seifert , Maria Jimena Costa , Alexander Cavallaro , Martin Huber , Dorin Comaniciu
CPC分类号： G06K9/6207 , G06T7/0012 , G06T7/12 , G06T7/149 , G06T2207/10081 , G06T2207/10088 , G06T2207/30081
摘要： A method and system for fully automatic segmentation the prostate in multi-spectral 3D magnetic resonance (MR) image data having one or more scalar intensity values per voxel is disclosed. After intensity standardization of multi-spectral 3D MR image data, a prostate boundary is detected in the multi-spectral 3D MR image data using marginal space learning (MSL). The detected prostate boundary is refined using one or more trained boundary detectors. The detected prostate boundary can be split into patches corresponding to anatomical regions of the prostate and the detected prostate boundary can be refined using trained boundary detectors corresponding to the patches.
摘要翻译： 公开了一种用于在具有每个体素具有一个或多个标量强度值的多光谱3D磁共振（MR）图像数据中全息自动分割前列腺的方法和系统。 在多光谱3D MR图像数据的强度标准化之后，使用边缘空间学习（MSL）在多光谱3D MR图像数据中检测前列腺边界。 使用一个或多个训练有素的边界检测器来精确检测前列腺边界。 检测到的前列腺边界可以分裂成对应于前列腺的解剖区域的斑块，并且可以使用对应于斑块的训练边界检测器来精细检测检测到的前列腺边界。
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2.发明申请Method and System for Segmentation of the Prostate in 3D Magnetic Resonance Images 有权三维磁共振图像前列腺分割方法与系统公开(公告)号：US20110116698A1
公开(公告)日：2011-05-19
申请号：US12946909
申请日：2010-11-16
申请人： Michael Weis , Michael Suehling , Michael Kelm , Sascha Seifert , Maria Jimena Costa , Alexander Cavallaro , Martin Huber , Dorin Comaniciu
发明人： Michael Weis , Michael Suehling , Michael Kelm , Sascha Seifert , Maria Jimena Costa , Alexander Cavallaro , Martin Huber , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06K9/6207 , G06T7/0012 , G06T7/12 , G06T7/149 , G06T2207/10081 , G06T2207/10088 , G06T2207/30081
摘要： A method and system for fully automatic segmentation the prostate in multi-spectral 3D magnetic resonance (MR) image data having one or more scalar intensity values per voxel is disclosed. After intensity standardization of multi-spectral 3D MR image data, a prostate boundary is detected in the multi-spectral 3D MR image data using marginal space learning (MSL). The detected prostate boundary is refined using one or more trained boundary detectors. The detected prostate boundary can be split into patches corresponding to anatomical regions of the prostate and the detected prostate boundary can be refined using trained boundary detectors corresponding to the patches.
摘要翻译： 公开了一种用于在具有每个体素具有一个或多个标量强度值的多光谱3D磁共振（MR）图像数据中全息自动分割前列腺的方法和系统。 在多光谱3D MR图像数据的强度标准化之后，使用边缘空间学习（MSL）在多光谱3D MR图像数据中检测前列腺边界。 使用一个或多个训练有素的边界检测器来精确检测前列腺边界。 检测到的前列腺边界可以分裂成对应于前列腺的解剖区域的斑块，并且可以使用对应于斑块的训练边界检测器来精细检测检测到的前列腺边界。
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公开(公告)号：US09545238B2
公开(公告)日：2017-01-17
申请号：US13053263
申请日：2011-03-22
申请人： Rüdiger Bertsch , Roland Brill , Alexander Cavallaro , Maria Jimena Costa , Martin Huber , Michael Kelm , Helmut König , Sascha Seifert , Michael Wels
发明人： Rüdiger Bertsch , Roland Brill , Alexander Cavallaro , Maria Jimena Costa , Martin Huber , Michael Kelm , Helmut König , Sascha Seifert , Michael Wels
CPC分类号： A61B6/5217 , A61B5/055 , A61B5/415 , A61B5/418 , A61B5/4842 , G06F19/00 , G06F19/321 , G16H50/20
摘要： A method and system for the diagnosis of 3D images are disclosed, which significantly cuts the time required for the diagnosis. The 3D images are for example an image volume dataset of a magnetic resonance tomography system which is saved in an RIS or PACS system. In at least one embodiment, the diagnostic finding are partially automatically generated, and details of the position, size and change in pathological structures are compared to previous diagnostic findings are generated automatically. As a result of this automation the diagnostic work of radiologists is significantly reduced.
摘要翻译： 公开了用于诊断3D图像的方法和系统，其显着地缩短了诊断所需的时间。 3D图像例如是保存在RIS或PACS系统中的磁共振断层摄影系统的图像体积数据集。 在至少一个实施例中，部分地自动生成诊断发现，并且自动地生成病理结构的位置，大小和变化的细节与先前的诊断结果进行比较。 由于这种自动化，放射科医师的诊断工作显着减少。
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公开(公告)号：US20110235887A1
公开(公告)日：2011-09-29
申请号：US13053263
申请日：2011-03-22
申请人： Rüdiger Bertsch , Roland Brill , Alexander Cavallaro , Maria Jimena Costa , Martin Huber , Michael Kelm , Helmut König , Sascha Seifert , Michael Wels
发明人： Rüdiger Bertsch , Roland Brill , Alexander Cavallaro , Maria Jimena Costa , Martin Huber , Michael Kelm , Helmut König , Sascha Seifert , Michael Wels
IPC分类号： G06K9/00
CPC分类号： A61B6/5217 , A61B5/055 , A61B5/415 , A61B5/418 , A61B5/4842 , G06F19/00 , G06F19/321 , G16H50/20
摘要： A method and system for the diagnosis of 3D images are disclosed, which significantly cuts the time required for the diagnosis. The 3D images are for example an image volume dataset of a magnetic resonance tomography system which is saved in an RIS or PACS system. In at least one embodiment, the diagnostic finding are partially automatically generated, and details of the position, size and change in pathological structures are compared to previous diagnostic findings are generated automatically. As a result of this automation the diagnostic work of radiologists is significantly reduced.
摘要翻译： 公开了用于诊断3D图像的方法和系统，其显着地缩短了诊断所需的时间。 3D图像例如是保存在RIS或PACS系统中的磁共振断层摄影系统的图像体积数据集。 在至少一个实施例中，部分地自动生成诊断发现，并且自动地生成病理结构的位置，大小和变化的细节与先前的诊断结果进行比较。 由于这种自动化，放射科医师的诊断工作显着减少。
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5.发明授权Method and system for automatic detection of spinal bone lesions in 3D medical image data 有权3D医学图像数据中脊髓损伤自动检测方法及系统公开(公告)号：US08693750B2
公开(公告)日：2014-04-08
申请号：US13342248
申请日：2012-01-03
申请人： Michael Wels , Michael Suehling , Shaohua Kevin Zhou , David Liu , Dijia Wu , Christopher V. Alvino , Michael Kelm , Grzegorz Soza , Dorin Comaniciu
发明人： Michael Wels , Michael Suehling , Shaohua Kevin Zhou , David Liu , Dijia Wu , Christopher V. Alvino , Michael Kelm , Grzegorz Soza , Dorin Comaniciu
CPC分类号： G06K9/4614 , G06K9/6256 , G06K2209/055 , G06K2209/40 , G06T7/0012 , G06T2207/10072 , G06T2207/20016 , G06T2207/20081 , G06T2207/30012 , G06T2207/30096
摘要： A method and system for automatic detection and volumetric quantification of bone lesions in 3D medical images, such as 3D computed tomography (CT) volumes, is disclosed. Regions of interest corresponding to bone regions are detected in a 3D medical image. Bone lesions are detected in the regions of interest using a cascade of trained detectors. The cascade of trained detectors automatically detects lesion centers and then estimates lesion size in all three spatial axes. A hierarchical multi-scale approach is used to detect bone lesions using a cascade of detectors on multiple levels of a resolution pyramid of the 3D medical image.
摘要翻译： 公开了一种用于3D医学图像中的骨损伤的自动检测和体积定量的方法和系统，例如3D计算机断层摄影（CT）体积。 在3D医学图像中检测与骨骼区域相对应的兴趣区域。 使用级联的经过训练的检测器在感兴趣的区域中检测到骨损伤。 训练有素的检测器的级联自动检测损伤中心，然后估计所有三个空间轴的病变大小。 使用分级多尺度方法来使用3D医学图像的分辨率金字塔的多个级别上的级联检测器来检测骨损伤。
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6.发明申请Method and System for Automatic Detection of Spinal Bone Lesions in 3D Medical Image Data 有权自动检测3D医学图像数据中脊髓损伤的方法和系统公开(公告)号：US20120183193A1
公开(公告)日：2012-07-19
申请号：US13342248
申请日：2012-01-03
申请人： Michael Wels , Michael Suehling , Shaohua Kevin Zhou , David Liu , Dijia Wu , Christopher V. Alvino , Michael Kelm , Grzegorz Soza , Dorin Comaniciu
发明人： Michael Wels , Michael Suehling , Shaohua Kevin Zhou , David Liu , Dijia Wu , Christopher V. Alvino , Michael Kelm , Grzegorz Soza , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06K9/4614 , G06K9/6256 , G06K2209/055 , G06K2209/40 , G06T7/0012 , G06T2207/10072 , G06T2207/20016 , G06T2207/20081 , G06T2207/30012 , G06T2207/30096
摘要： A method and system for automatic detection and volumetric quantification of bone lesions in 3D medical images, such as 3D computed tomography (CT) volumes, is disclosed. Regions of interest corresponding to bone regions are detected in a 3D medical image. Bone lesions are detected in the regions of interest using a cascade of trained detectors. The cascade of trained detectors automatically detects lesion centers and then estimates lesion size in all three spatial axes. A hierarchical multi-scale approach is used to detect bone lesions using a cascade of detectors on multiple levels of a resolution pyramid of the 3D medical image.
摘要翻译： 公开了一种用于3D医学图像中的骨损伤的自动检测和体积定量的方法和系统，例如3D计算机断层摄影（CT）体积。 在3D医学图像中检测与骨骼区域相对应的兴趣区域。 使用级联的经过训练的检测器在感兴趣的区域中检测到骨损伤。 训练有素的检测器的级联自动检测损伤中心，然后估计所有三个空间轴的病变大小。 使用分级多尺度方法来使用3D医学图像的分辨率金字塔的多个级别上的级联检测器来检测骨损伤。
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公开(公告)号：US20120321174A1
公开(公告)日：2012-12-20
申请号：US13160744
申请日：2011-06-15
申请人： Alexey Tsymbal , Michael Kelm , Maria Jimena Costa , Shaohua Kevin Zhou , Dorin Comaniciu , Yefeng Zheng , Alexander Schwing
发明人： Alexey Tsymbal , Michael Kelm , Maria Jimena Costa , Shaohua Kevin Zhou , Dorin Comaniciu , Yefeng Zheng , Alexander Schwing
IPC分类号： G06K9/62
CPC分类号： G06K9/6219 , G06F17/30247 , G06F17/30268 , G06K9/6263 , G06K9/6282
摘要： A method of performing image retrieval includes training a random forest RF classifier based on low-level features of training images and a high-level feature, using similarity values generated by the RF classifier to determine a subset of the training images that are most similar to one another, and classifying input images for the high-level feature using the RF classifier and the determined subset of images.
摘要翻译： 执行图像检索的方法包括基于训练图像的低级特征和高级特征来训练随机森林RF分类器，使用由RF分类器生成的相似性值来确定最相似的训练图像的子集 并且使用RF分类器和确定的图像子集对高级特征的输入图像进行分类。
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公开(公告)号：US08744172B2
公开(公告)日：2014-06-03
申请号：US13160744
申请日：2011-06-15
申请人： Alexey Tsymbal , Michael Kelm , Maria Jimena Costa , Shaohua Kevin Zhou , Dorin Comaniciu , Yefeng Zheng , Alexander Schwing
发明人： Alexey Tsymbal , Michael Kelm , Maria Jimena Costa , Shaohua Kevin Zhou , Dorin Comaniciu , Yefeng Zheng , Alexander Schwing
IPC分类号： G06K9/00
CPC分类号： G06K9/6219 , G06F17/30247 , G06F17/30268 , G06K9/6263 , G06K9/6282
摘要： A method of performing image retrieval includes training a random forest RF classifier based on low-level features of training images and a high-level feature, using similarity values generated by the RF classifier to determine a subset of the training images that are most similar to one another, and classifying input images for the high-level feature using the RF classifier and the determined subset of images.
摘要翻译： 执行图像检索的方法包括基于训练图像的低级特征和高级特征来训练随机森林RF分类器，使用由RF分类器生成的相似性值来确定最相似的训练图像的子集 并且使用RF分类器和确定的图像子集对高级特征的输入图像进行分类。
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9.发明授权Method and system for on-site learning of landmark detection models for end user-specific diagnostic medical image reading 有权用于最终用户特定诊断医学图像阅读的地标检测模型的现场学习方法和系统公开(公告)号：US09113781B2
公开(公告)日：2015-08-25
申请号：US13761263
申请日：2013-02-07
CPC分类号： A61B5/0033 , A61B6/00 , A61B6/025 , A61B6/502 , G06F19/00 , G06F19/321 , G06K9/00 , G06K9/6255 , G06K2209/05
摘要： A method and system for on-line learning of landmark detection models for end-user specific diagnostic image reading is disclosed. A selection of a landmark to be detected in a 3D medical image is received. A current landmark detection result for the selected landmark in the 3D medical image is determined by automatically detecting the selected landmark in the 3D medical image using a stored landmark detection model corresponding to the selected landmark or by receiving a manual annotation of the selected landmark in the 3D medical image. The stored landmark detection model corresponding to the selected landmark is then updated based on the current landmark detection result for the selected landmark in the 3D medical image. The landmark selected in the 3D medical image can be a set of landmarks defining a custom view of the 3D medical image.
摘要翻译： 公开了一种用于在线学习用于最终用户特定诊断图像读取的地标检测模型的方法和系统。 接收在3D医疗图像中检测的地标的选择。 通过使用与所选择的地标相对应的存储的地标检测模型自动检测3D医学图像中的所选地标来确定3D医学图像中所选择的地标的当前地标检测结果，或者通过接收所选地标的手动注释 3D医学图像。 然后基于3D医学图像中所选地标的当前地标检测结果来更新对应于所选地标的所存储的地标检测模型。 在3D医学图像中选择的地标可以是定义3D医学图像的定制视图的一组地标。
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10.发明申请Method and System for On-Site Learning of Landmark Detection Models for End User-Specific Diagnostic Medical Image Reading 有权用于特定于用户的诊断医学图像阅读的地标检测模型的现场学习方法和系统公开(公告)号：US20140219548A1
公开(公告)日：2014-08-07
申请号：US13761263
申请日：2013-02-07
IPC分类号： A61B5/00
CPC分类号： A61B5/0033 , A61B6/00 , A61B6/025 , A61B6/502 , G06F19/00 , G06F19/321 , G06K9/00 , G06K9/6255 , G06K2209/05
摘要： A method and system for on-line learning of landmark detection models for end-user specific diagnostic image reading is disclosed. A selection of a landmark to be detected in a 3D medical image is received. A current landmark detection result for the selected landmark in the 3D medical image is determined by automatically detecting the selected landmark in the 3D medical image using a stored landmark detection model corresponding to the selected landmark or by receiving a manual annotation of the selected landmark in the 3D medical image. The stored landmark detection model corresponding to the selected landmark is then updated based on the current landmark detection result for the selected landmark in the 3D medical image. The landmark selected in the 3D medical image can be a set of landmarks defining a custom view of the 3D medical image.
摘要翻译： 公开了一种用于在线学习用于最终用户特定诊断图像读取的地标检测模型的方法和系统。 接收在3D医疗图像中检测的地标的选择。 通过使用与所选择的地标相对应的存储的地标检测模型自动检测3D医学图像中的所选地标来确定3D医学图像中所选择的地标的当前地标检测结果，或者通过接收所选地标的手动注释 3D医学图像。 然后基于3D医学图像中所选地标的当前地标检测结果来更新对应于所选地标的所存储的地标检测模型。 在3D医学图像中选择的地标可以是定义3D医学图像的定制视图的一组地标。
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