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公开(公告)号：US20190311228A1
公开(公告)日：2019-10-10
申请号：US16258751
申请日：2019-01-28
发明人： Yu Zhao , Yimo Gao , Shu Liao , Liang Zhao , Zhennan Yan , Yiqiang Zhan , Xiang Sean Zhou
摘要： A framework for cross-modality image synthesis. A first and second model may be trained using respective first and second pairs of complementary images and corresponding first and second ground truth images that represent first and second views of a region of interest. The first and second pairs of complementary images may be acquired by a first modality and the first and second ground truth images may be acquired by a second modality. A combinational network may further be trained to combine features from the first and second models. At least one synthetic second modality image may then be generated by passing current images through the trained first or second model and the combinational network, wherein the current images are acquired by the first modality and represent the first or second view of the region of interest.
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公开(公告)号：US09799120B1
公开(公告)日：2017-10-24
申请号：US15149474
申请日：2016-05-09
发明人： Matthias Fenchel , Yiqiang Zhan
CPC分类号： G06T7/0014 , G06K9/6262 , G06K9/627 , G06T7/11 , G06T2207/10088 , G06T2207/20128
摘要： In a magnetic resonance (MR) apparatus and segmentation method, a region in an MR image, acquired from a scan of a patient with an MR scanner of the apparatus, is provided to a computer for segmentation of the region from the overall image dataset. The segmentation takes place based on a classification of image elements of the image dataset, and the classification is iteratively re-trained in a weakly supervised learning algorithm based on examination-specific information provided to the computer.
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公开(公告)号：US11176188B2
公开(公告)日：2021-11-16
申请号：US15865539
申请日：2018-01-09
IPC分类号： G06F16/00 , G06F16/35 , G16H50/70 , G06K9/00 , G06N20/00 , G06F16/93 , G06K9/62 , G06F16/36 , G16H15/00 , G16H30/40 , G06N3/04 , G06N3/08 , G06N7/00 , G16H10/60 , G06F40/30 , G06N20/10 , G06N5/00
摘要： A visualization framework based on document representation learning is described herein. The framework may first convert a free text document into word vectors using learning word embeddings. Document representations may then be determined in a fixed-dimensional semantic representation space by passing the word vectors through a trained machine learning model, wherein more related documents lie closer than less related documents in the representation space. A clustering algorithm may be applied to the document representations for a given patient to generate clusters. The framework then generates a visualization based on these clusters.
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公开(公告)号：US20210158531A1
公开(公告)日：2021-05-27
申请号：US17163893
申请日：2021-02-01
发明人： Luca Bogoni , Marcos Salganicoff , Matthias Wolf , Shu Liao , Yiqiang Zhan , Gerardo Hermosillo Valadez , Xiang Sean Zhou , Zhigang Peng
IPC分类号： G06T7/11 , G06T7/00 , G16H15/00 , A61B5/00 , G16H50/20 , G16H10/60 , A61B5/055 , A61B5/107 , G16H30/40
摘要： A framework for patient management based on anatomic measurements is described herein. In accordance with one aspect, patient records are clustered into a set of sub-populations based on first anatomic measurements and characteristics extracted from first patient data associated with a population of patients. A representative sub-population similar to a patient may be determined from the set of sub-populations based on the patient data of the patient. A report that presents the second anatomic measurements associated with the patient in relation to corresponding first anatomic measurements associated with the representative sub-population may then be generated.
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公开(公告)号：US20190019287A1
公开(公告)日：2019-01-17
申请号：US16016776
申请日：2018-06-25
发明人： Fitsum Aklilu Reda , Yiqiang Zhan , Parmeet Singh Bhatia , Yoshihisa Shinagawa , Luca Bogoni , Xiang Sean Zhou
CPC分类号： G06T7/0012 , G01B21/20 , G06K9/4628 , G06K9/627 , G06K2209/051 , G06N20/00 , G06T7/12 , G06T7/60 , G06T7/62 , G06T7/66 , G06T2200/24 , G06T2207/10081 , G06T2207/20081 , G06T2207/20084 , G06T2207/30048 , G06T2207/30101 , G06T2207/30172
摘要： A framework for automated measurement. In accordance with one aspect, the framework detects a centerline point of a structure of interest in an image. A centerline of the structure of interest may be traced based on the detected centerline point. A trained segmentation learning structure may be used to generate one or more contours of the structure of interest along the centerline. One or more measurements may then be extracted from the one or more contours.
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公开(公告)号：US20180293727A1
公开(公告)日：2018-10-11
申请号：US15483322
申请日：2017-04-10
发明人： Matthias Fenchel , Yiqiang Zhan
摘要： In a method and an apparatus for rib unfolding in an MR image, a computer is provided with an input data file formed of volumetric MR data that represent a 3D image of the rib cage and the lungs of a subject. A view is selected wherein the ribs in the rib cage are in approximated as curves, such as a transverse slice through the 3D image, or an oblique view of the 3D image. The lungs in the selected view are used in order to define a first smooth curved surface representation that is inside of the rib cage. Further ellipses are selectively defined starting from the first ellipse and moving outwardly from the first smooth curved surface representation that respectively proceed through rib pairs in the rib cage in the selected image. These further smooth curved surface representations are then used to unfold the 3D image, by cutting and straightening these further smooth curved surface representations, thereby obtaining an unfolded 3D image, which is then displayed at a display.
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