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1.发明授权公开(公告)号：US10806372B2
公开(公告)日：2020-10-20
申请号：US14840357
申请日：2015-08-31
摘要： A method of evaluating airway wall density and inflammation including: segmenting a bronchial tree to create an airway wall map; for each branch, taking a set of locations that form the wall of each branch from the map and sampling the value in a virtual non-contrast image of the bronchial tree and, given a set of samples of pre-contrast densities, computing a value to yield a bronchial wall density for each branch to yield density measures; for each branch, taking the set of locations that form the wall of each branch from the map and sampling the value in a contrast agent map of the bronchial tree and, given the set of samples of contrast agent intake, computing a value to yield a bronchial wall uptake for each branch to yield inflammation measures; and using the density and inflammation measures to determine treatment or predict outcome for a patient.
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公开(公告)号：US20190220701A1
公开(公告)日：2019-07-18
申请号：US15872304
申请日：2018-01-16
CPC分类号： G06K9/6256 , A61B6/032 , A61B6/5211 , G06K9/623 , G06K9/66 , G06T5/002 , G06T7/11 , G06T7/187 , G06T11/003 , G06T2207/10081 , G06T2207/20081 , G06T2207/20084 , G06T2207/20116 , G06T2207/20152 , G06T2207/30061 , G06T2207/30101
摘要： A generative network is used for lung lobe segmentation or lung fissure localization, or for training a machine network for lobar segmentation or localization. For segmentation, deep learning is used to better deal with a sparse sampling of training data. To increase the amount of training data available, an image-to-image or generative network localizes fissures in at least some of the samples. The deep-learnt network, fissure localization, or other segmentation may benefit from generative localization of fissures.
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3.发明申请公开(公告)号：US20190128989A1
公开(公告)日：2019-05-02
申请号：US16008086
申请日：2018-06-14
发明人： Sandro Braun , Boris Mailhe , Xiao Chen , Benjamin L. Odry , Pascal Ceccaldi , Mariappan S. Nadar
IPC分类号： G01R33/565 , G06T5/00 , G06N3/08 , G06N5/04 , G06T7/20
摘要： Systems and methods are provided for correcting motion artifacts in magnetic resonance images. An image-to-image neural network is used to generate motion corrected magnetic resonance data given motion corrupted magnetic resonance data. The image-to-image neural network is coupled within an adversarial network to help refine the generated magnetic resonance data. The adversarial network includes a generator network (the image-to-image neural network) and a discriminator network. The generator network is trained to minimize a loss function based on a Wasserstein distance when generating MR data. The discriminator network is trained to differentiate the motion corrected MR data from motion artifact free MR data.
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公开(公告)号：US20190046068A1
公开(公告)日：2019-02-14
申请号：US16055546
申请日：2018-08-06
摘要： Systems and methods are provided for generating a protocol independent image. A deep learning generative framework learns to recognize the boundaries and classification of tissues in an MRI image. The deep learning generative framework includes an encoder, a decoder, and a discriminator network. The encoder is trained using the discriminator network to generate a latent space that is invariant to protocol and the decoder is trained to generate the best output possible for brain and/or tissue extraction.
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公开(公告)号：US10043088B2
公开(公告)日：2018-08-07
申请号：US15606069
申请日：2017-05-26
摘要： For image quality scoring of an image from a medical scanner, a generative model of an expected good quality image may be created using deep machine-learning. The deviation of an input image from the generative model is used as an input feature vector for a discriminative model. The discriminative model may also operate on another input feature vector derived from the input image. Based on these input feature vectors, the discriminative model outputs an image quality score.
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公开(公告)号：US20170372155A1
公开(公告)日：2017-12-28
申请号：US15606069
申请日：2017-05-26
CPC分类号： G06K9/03 , G06K9/6255 , G06K9/627 , G06K9/6274 , G06K9/6277 , G06K9/66 , G06K2209/05 , G16H30/40 , G16H40/60
摘要： For image quality scoring of an image from a medical scanner, a generative model of an expected good quality image may be created using deep machine-learning. The deviation of an input image from the generative model is used as an input feature vector for a discriminative model. The discriminative model may also operate on another input feature vector derived from the input image. Based on these input feature vectors, the discriminative model outputs an image quality score.
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7.发明授权公开(公告)号：US11255943B2
公开(公告)日：2022-02-22
申请号：US16162559
申请日：2018-10-17
摘要： For determination of motion artifact in MR imaging, motion of the patient in three dimensions is used with a measurement k-space line order based on one or more actual imaging sequences to generate training data. The MR scan of the ground truth three-dimensional (3D) representation subjected to 3D motion is simulated using the realistic line order. The difference between the resulting reconstructed 3D representation and the ground truth 3D representation is used in machine-based deep learning to train a network to predict motion artifact or level given an input 3D representation from a scan of a patient. The architecture of the network may be defined to deal with anisotropic data from the MR scan.
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公开(公告)号：US10713785B2
公开(公告)日：2020-07-14
申请号：US15892746
申请日：2018-02-09
发明人： Sandro Braun , Xiaoguang Lu , Boris Mailhe , Benjamin L. Odry , Xiao Chen , Mariappan S. Nadar
摘要： A system and method includes generation of one or more motion-corrupted images based on each of a plurality of reference images, and training of a regression network to determine a motion score, where training of the regression network includes input of a generated motion-corrupted image to the regression network, reception of a first motion score output by the regression network in response to the input image, and determination of a loss by comparison of the first motion score to a target motion score, the target motion score calculated based on the input motion-corrupted image and a reference image based on which the motion-corrupted image was generated.
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公开(公告)号：US20190172207A1
公开(公告)日：2019-06-06
申请号：US15831731
申请日：2017-12-05
发明人： Benjamin L. Odry , Hasan Ertan Cetingul , Mariappan S. Nadar , Puneet Sharma , Shaohua Kevin Zhou , Dorin Comaniciu
CPC分类号： G06T7/0016 , A61B6/032 , A61B6/481 , A61B6/501 , A61B6/506 , A61B6/5247 , G06K9/4628 , G06K2209/05 , G06T7/0012 , G06T7/11 , G06T2207/10081 , G06T2207/20081 , G06T2207/20084 , G06T2207/30016 , G06T2207/30096 , G16H30/40 , G16H50/20 , G16H50/70
摘要： A method of reviewing neural scans includes receiving at least one landmark corresponding to an anatomical region. A plurality of images of tissue including the anatomical region is received and a neural network configured to differentiate between healthy tissue and unhealthy tissue within the anatomical region is generated. The neural network is generated by a machine learning process configured to receive the plurality of images of tissue and generate a plurality of weighting factors configured to differentiate between healthy tissue and unhealthy tissue. At least one patient image of tissue including the anatomical region is received and a determination is made by the neural network whether the at least one patient image of tissue includes healthy or unhealthy tissue.
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公开(公告)号：US10258304B1
公开(公告)日：2019-04-16
申请号：US15825304
申请日：2017-11-29
IPC分类号： G06K9/00 , A61B6/00 , A61B6/03 , A61B5/08 , A61B5/00 , G06T7/13 , G06T7/11 , G06T7/00 , G06K9/62 , G06N3/04 , G06N3/08
摘要： A method and apparatus for automated boundary delineation of a tubular structure in a 3D medical image of a patient using an infinitely recurrent neural network (IRNN) is disclosed. An unraveled cross-section image corresponding to a portion of a tubular structure is extracted from 3D medical image. The unraveled cross-section image is divided into a plurality of image chunks. A boundary of the portion of the tubular structure is detected based on the plurality of image chunks using a trained IRNN. The trained IRNN repeatedly inputs a sequential data stream, including the plurality of image chunks of the unraveled cross-section image, for a plurality of iterations while preserving a memory state between iterations, and detects, for each image chunk of the unraveled cross-section image input to the trained IRNN in the sequential data stream, a corresponding section of the boundary of the portion of the tubular structure.
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