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公开(公告)号：US09117259B2
公开(公告)日：2015-08-25
申请号：US13238370
申请日：2011-09-21
申请人： David Liu , Dijia Wu , Shaohua Kevin Zhou , Maria Jimena Costa , Michael Suehling , Christian Tietjen
发明人： David Liu , Dijia Wu , Shaohua Kevin Zhou , Maria Jimena Costa , Michael Suehling , Christian Tietjen
CPC分类号： G06T7/0016 , G06T7/41 , G06T2207/10072 , G06T2207/20012 , G06T2207/20041 , G06T2207/20081 , G06T2207/20104 , G06T2207/30056
摘要： A method and system for automatically detecting liver lesions in medical image data, such as 3D CT images, is disclosed. A liver region is segmented in a 3D image. Liver lesion center candidates are detected in the segmented liver region. Lesion candidates are segmented corresponding to the liver lesion center candidates, and lesions are detected from the segmented lesion candidates using learning based verification.
摘要翻译： 公开了一种用于自动检测医学图像数据中肝脏病变的方法和系统，如3D CT图像。 肝脏区域被分割成3D图像。 肝脏病变中心候选人在分段肝脏区域检测到。 病变候选者对应于肝脏病变中心候选物，并且使用基于学习的验证从分割的病变候选物检测病变。
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公开(公告)号：US20120070055A1
公开(公告)日：2012-03-22
申请号：US13238370
申请日：2011-09-21
申请人： David Liu , Dijia Wu , Shaohua Kevin Zhou , Maria Jimena Costa , Michael Suehling , Christian Tietjen
发明人： David Liu , Dijia Wu , Shaohua Kevin Zhou , Maria Jimena Costa , Michael Suehling , Christian Tietjen
IPC分类号： G06K9/00
CPC分类号： G06T7/0016 , G06T7/41 , G06T2207/10072 , G06T2207/20012 , G06T2207/20041 , G06T2207/20081 , G06T2207/20104 , G06T2207/30056
摘要： A method and system for automatically detecting liver lesions in medical image data, such as 3D CT images, is disclosed. A liver region is segmented in a 3D image. Liver lesion center candidates are detected in the segmented liver region. Lesion candidates are segmented corresponding to the liver lesion center candidates, and lesions are detected from the segmented lesion candidates using learning based verification.
摘要翻译： 公开了一种用于自动检测医学图像数据中肝脏病变的方法和系统，如3D CT图像。 肝脏区域被分割成3D图像。 肝脏病变中心候选人在分段肝脏区域检测到。 病变候选者对应于肝脏病变中心候选物，并且使用基于学习的验证从分割的病变候选物检测病变。
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3.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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4.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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5.发明授权Method and system for automatic detection and segmentation of axillary lymph nodes 有权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
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。
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6.发明申请Method and System for Automatic Detection and Segmentation of Axillary Lymph Nodes 有权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。
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公开(公告)号：US09785858B2
公开(公告)日：2017-10-10
申请号：US12567197
申请日：2009-09-25
申请人： Sascha Seifert , Adrian Barbu , Shaohua Kevin Zhou , David Liu , Johannes Feulner , Martin Huber , Michael Suehling , Alexander Cavallaro , Dorin Comaniciu
发明人： Sascha Seifert , Adrian Barbu , Shaohua Kevin Zhou , David Liu , Johannes Feulner , Martin Huber , Michael Suehling , Alexander Cavallaro , Dorin Comaniciu
CPC分类号： G06K9/4638 , G06K2209/05 , G06T7/11 , G06T7/143 , G06T2207/10081 , G06T2207/20081 , G06T2207/20128 , G06T2207/30048 , G06T2207/30056
摘要： A method and apparatus for hierarchical parsing and semantic navigation of a full or partial body computed tomography CT scan is disclosed. In particular, organs are segmented and anatomic landmarks are detected in a full or partial body CT volume. One or more predetermined slices of the CT volume are detected. A plurality of anatomic landmarks and organ centers are then detected in the CT volume using a discriminative anatomical network, each detected in a portion of the CT volume constrained by at least one of the detected slices. A plurality of organs, such as heart, liver, kidneys, spleen, bladder, and prostate, are detected in a sense of a bounding box and segmented in the CT volume, detection of each organ bounding box constrained by the detected organ centers and anatomic landmarks. Organ segmentation is via a database-guided segmentation method.
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8.发明申请Method and System for Hierarchical Parsing and Semantic Navigation of Full Body Computed Tomography Data 有权全身计算机断层扫描数据的分层解析和语义导航方法与系统公开(公告)号：US20100080434A1
公开(公告)日：2010-04-01
申请号：US12567197
申请日：2009-09-25
申请人： Sascha Seifert , Adrian Barbu , Shaohua Kevin Zhou , David Liu , Johannes Feulner , Martin Huber , Michael Suehling , Alexander Cavallaro , Dorin Comaniciu
发明人： Sascha Seifert , Adrian Barbu , Shaohua Kevin Zhou , David Liu , Johannes Feulner , Martin Huber , Michael Suehling , Alexander Cavallaro , Dorin Comaniciu
IPC分类号： G06K9/00
CPC分类号： G06K9/4638 , G06K2209/05 , G06T7/11 , G06T7/143 , G06T2207/10081 , G06T2207/20081 , G06T2207/20128 , G06T2207/30048 , G06T2207/30056
摘要： A method and apparatus for hierarchical parsing and semantic navigation of a full or partial body computed tomography CT scan is disclosed. In particular, organs are segmented and anatomic landmarks are detected in a full or partial body CT volume. One or more predetermined slices of the CT volume are detected. A plurality of anatomic landmarks and organ centers are then detected in the CT volume using a discriminative anatomical network, each detected in a portion of the CT volume constrained by at least one of the detected slices. A plurality of organs, such as heart, liver, kidneys, spleen, bladder, and prostate, are detected in a sense of a bounding box and segmented in the CT volume, detection of each organ bounding box constrained by the detected organ centers and anatomic landmarks. Organ segmentation is via a database-guided segmentation method.
摘要翻译： 公开了一种用于全身或部分身体计算机断层摄影CT扫描的分层解析和语义导航的方法和装置。 特别地，器官被分割，并且在全身或部分体CT体积中检测解剖标志物。 检测CT体积的一个或多个预定切片。 然后使用辨别性解剖网络在CT体积中检测多个解剖标志和器官中心，每个解剖学网络在CT体积的一部分中被至少一个检测到的切片所约束。 检测多个器官，如心脏，肝脏，肾脏，脾脏，膀胱和前列腺，并在CT体积中被分割，并在被检测到的器官中枢和解剖学上受到限制的每个器官界限盒的检测 地标 器官分割是通过数据库引导的分割方法。
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9.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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10.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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