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公开(公告)号：US10438350B2
公开(公告)日：2019-10-08
申请号：US15634657
申请日：2017-06-27
Applicant: General Electric Company
Inventor： Bhushan Dayaram Patil , Peter Lamb , Roshni Rustom Bhagalia , Bipul Das
Abstract: The present approach relates, in some aspects, to a multi-level and a multi-channel frame work for segmentation using model-based or “shallow” classification (i.e. learning processes such as linear regression, clustering, support vector machines, and so forth) followed by deep learning. This framework starts with a very low resolution version of the multi-channel data and constructs an shallow classifier with simple features to generate a coarser level tissue mask that in turn is used to crop patches from the high-resolution volume. The cropped volume is then processed using the trained convolution network to perform a deep learning based segmentation within the slices.
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公开(公告)号：US20180374209A1
公开(公告)日：2018-12-27
申请号：US15634657
申请日：2017-06-27
Applicant: General Electric Company
Inventor： Bhushan Dayaram Patil , Peter Lamb , Roshni Rustom Bhagalia , Bipul Das
CPC classification number: G06T7/0012 , G06N3/0445 , G06N3/0454 , G06N3/082 , G06N3/084 , G06N3/088 , G06N7/005 , G06N20/10 , G06T7/11 , G06T2207/10081 , G06T2207/20081 , G06T2207/20084 , G06T2207/30008
Abstract: The present approach relates, in some aspects, to a multi-level and a multi-channel frame work for segmentation using model-based or “shallow” classification (i.e. learning processes such as linear regression, clustering, support vector machines, and so forth) followed by deep learning. This framework starts with a very low resolution version of the multi-channel data and constructs an shallow classifier with simple features to generate a coarser level tissue mask that in turn is used to crop patches from the high-resolution volume. The cropped volume is then processed using the trained convolution network to perform a deep learning based segmentation within the slices.
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公开(公告)号：US20140294276A1
公开(公告)日：2014-10-02
申请号：US13852781
申请日：2013-03-28
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Qi Song , Srikrishnan V , Roshni Rustom Bhagalia , Biqul Das
IPC: G06T7/00
CPC classification number: G06T7/0046 , G06K9/621 , G06K2009/366 , G06K2209/051 , G06T7/0012 , G06T7/75 , G06T2207/10072 , G06T2207/10136 , G06T2207/20081 , G06T2207/30004
Abstract: A hierarchical multi-object active appearance model (AAM) framework is disclosed for processing image data, such as localizer or scout image data. In accordance with this approach, a hierarchical arrangement of models (e.g., a model pyramid) maybe employed where a global or parent model that encodes relationships across multiple co-located structures is used to obtain an initial, coarse fit. Subsequent processing by child sub-models add more detail and flexibility to the overall fit.
Abstract translation: 公开了一种用于处理诸如定位器或侦察图像数据的图像数据的分级多对象主动外观模型（AAM）框架。 根据这种方法，可以使用模型（例如，模型金字塔）的分层布置，其中使用编码跨多个同位置结构的关系的全局或父模型来获得初始粗匹配。 儿童子模型的后续处理增加了整体配合的更多细节和灵活性。
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公开(公告)号：US09355454B2
公开(公告)日：2016-05-31
申请号：US13852781
申请日：2013-03-28
Applicant: General Electric Company
Inventor： Qi Song , Srikrishnan V , Roshni Rustom Bhagalia , Bipul Das
CPC classification number: G06T7/0046 , G06K9/621 , G06K2009/366 , G06K2209/051 , G06T7/0012 , G06T7/75 , G06T2207/10072 , G06T2207/10136 , G06T2207/20081 , G06T2207/30004
Abstract: A hierarchical multi-object active appearance model (AAM) framework is disclosed for processing image data, such as localizer or scout image data. In accordance with this approach, a hierarchical arrangement of models (e.g., a model pyramid) maybe employed where a global or parent model that encodes relationships across multiple co-located structures is used to obtain an initial, coarse fit. Subsequent processing by child sub-models add more detail and flexibility to the overall fit.
Abstract translation: 公开了一种用于处理诸如定位器或侦察图像数据的图像数据的分级多对象主动外观模型（AAM）框架。 根据这种方法，可以使用模型（例如，模型金字塔）的分层布置，其中使用编码跨多个同位置结构的关系的全局或父模型来获得初始粗匹配。 儿童子模型的后续处理增加了整体配合的更多细节和灵活性。
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