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公开(公告)号：US12067486B2
公开(公告)日：2024-08-20
申请号：US16728093
申请日：2019-12-27
发明人： Xinran Liang , Xiang Sean Zhou
摘要： A system for fault diagnosis is provided. The system may acquire a vibration signal of a target device, and determine one or more feature values of the vibration signal. The system may further determine a fault condition of the target device by applying a fault diagnosis model to the feature values. The fault diagnosis model may include a trained first component including a plurality of stacked trained RBMs, and a trained second component connected to the trained first component. The trained second component may include a trained fully connected layer and a trained output layer connected to the trained fully connected layer.
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公开(公告)号：US12040076B2
公开(公告)日：2024-07-16
申请号：US17550594
申请日：2021-12-14
发明人： Xiao Chen , Shanhui Sun , Terrence Chen
CPC分类号： G16H30/20 , A61B5/0044 , A61B5/055 , G06T7/0014 , G06T7/60 , A61B2576/023 , G06T2207/10088 , G06T2207/20081 , G06T2207/20084 , G06T2207/30048
摘要： Described herein are systems, methods and instrumentalities associated with automatic assessment of aneurysms. An automatic aneurysm assessment system or apparatus may be configured to obtain, e.g., using a pre-trained artificial neural network, strain values associated one or more locations of a human heart and one or more cardiac phases of the human heart and derive a representation (e.g., a 2D matrix) of the strain values across time and/or space. The system or apparatus may determine, based on the derived representation of the strain values, respective strain patterns associated with the one or more locations of the human heart and further determine whether the one or more locations are aneurysm locations by comparing the automatically determined strain patterns with predetermined normal strain patterns of the heart and determining the presence or risk of aneurysms based on the comparison.
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公开(公告)号：US12033327B2
公开(公告)日：2024-07-09
申请号：US17221160
申请日：2021-04-02
发明人： Jie-Zhi Cheng , Qitian Chen
CPC分类号： G06T7/11 , G06T7/0012 , G16H30/40 , G16H50/20 , G06T2207/10116 , G06T2207/20081 , G06T2207/20084 , G06T2207/30008 , G06T2207/30061 , G06T2207/30168
摘要： Described herein are systems, methods, and instrumentalities associated with processing medical chest images such as chest X-ray (CXR) images. Segmentation models derived via a deep learning process are used to segment the chest images and obtain a rib segmentation result and a lung segmentation result for each image. The rib segmentation result may include a rib sequence identified in the image while the lung segmentation result may include one or more lung fields identified in the image. The quality of each chest image (e.g., whether the image reflects a breath-holding state of the patient) may then be determined based on whether a sufficient number of ribs in the rib segmentation result overlap with the lung fields in the lung segmentation result. The segmentation results may be obtained in a coarse-to-fine manner, e.g., by first determining a large rib area and then further segmenting the large rib area to identify each individual rib.
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公开(公告)号：US20240177420A1
公开(公告)日：2024-05-30
申请号：US17994675
申请日：2022-11-28
发明人： Srikrishna Karanam , Runze Li , Meng Zheng , Ziyan Wu
CPC分类号： G06T17/20 , A61B34/10 , G06T7/75 , A61B2034/105 , G06T2207/30196
摘要： A video sequence depicting a person in a medical environment may be obtained and used for determining one or more human models of the person. A first human model representing a first pose or a first body shape of the person may be determined based on a first subset of images from the video sequence, while a second human model representing a second pose or a second body shape of the person may be determined based on a second subset of images from the video sequence. The second 2D or 3D representation of the person may include an adjustment to the first 2D or 3D representation of the person based on the observation of the person provided by the second subset of images.
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5.发明授权公开(公告)号：US11992289B2
公开(公告)日：2024-05-28
申请号：US17060988
申请日：2020-10-01
发明人： Zhang Chen , Xiao Chen , Shanhui Sun , Terrence Chen
IPC分类号： A61B5/00 , G01R33/36 , G01R33/561 , G01R33/563 , G06N3/08 , G06N3/084
CPC分类号： A61B5/0044 , A61B5/7264 , G01R33/3642 , G01R33/5612 , G01R33/5615 , G01R33/56325 , G06N3/08 , G06N3/084
摘要： A method includes using fully sampled retro cine data to train an algorithm, and applying the trained algorithm to real time MR cine data to yield reconstructed MR images.
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公开(公告)号：US20240161440A1
公开(公告)日：2024-05-16
申请号：US17988328
申请日：2022-11-16
发明人： Meng Zheng , Yuchun Liu , Fan Yang , Srikrishna Karanam , Ziyan Wu , Terrence Chen
CPC分类号： G06V10/24 , G06T7/80 , G06V10/751 , G06V10/82 , G06T2207/10024 , G06T2207/20081
摘要： Images captured by different image capturing devices may have different fields of views and/or resolutions. One or more of these images may be aligned based on an image template, and additional details for the adapted images may be predicted using a machine-learned data recovery model and added to the adapted images such that the images may have the same field of view or the same resolution.
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公开(公告)号：US20240144469A1
公开(公告)日：2024-05-02
申请号：US17973982
申请日：2022-10-26
发明人： Xiao Chen , Shanhui Sun , Terrence Chen , Arun Innanje
IPC分类号： G06T7/00 , G06T7/11 , G06T7/30 , G06V10/764
CPC分类号： G06T7/0012 , G06T7/11 , G06T7/30 , G06V10/764 , G06T2207/10088 , G06T2207/30048
摘要： Cardiac images such as cardiac magnetic resonance (CMR) images and tissue characterization maps (e.g., T1/T2 maps) may be analyzed automatically using machine learning (ML) techniques, and reports may be generated to summarize the analysis. The ML techniques may include training one or more of an image classification model, a heart segmentation model, or a cardiac pathology detection model to automate the image analysis and/or reporting process. The image classification model may be capable of grouping the cardiac images into different categories, the heart segmentation model may be capable of delineating different anatomical regions of the heart, and the pathology detection model may be capable of detecting a medical abnormality in one or more of the anatomical regions based on tissue patterns or parameters automatically recognized by the detection model. Image registration that compensates for the impact of motions or movements may also be conducted automatically using ML techniques.
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公开(公告)号：US11967102B2
公开(公告)日：2024-04-23
申请号：US17378495
申请日：2021-07-16
发明人： Abhishek Sharma , Arun Innanje , Ziyan Wu
CPC分类号： G06T7/73 , G06N3/045 , G06T7/0012 , G06V40/103 , G06T2207/10024 , G06T2207/10028 , G06T2207/10048 , G06T2207/20081 , G06T2207/20084 , G06T2207/30196
摘要： Image-based key points detection using a convolutional neural network (CNN) may be impacted if the key points are occluded in the image. Images obtained from additional imaging modalities such as depth and/or thermal images may be used in conjunction with RGB images to reduce or minimize the impact of the occlusion. The additional images may be used to determine adjustment values that are then applied to the weights of the CNN so that the convolution operations may be performed in a modality aware manner to increase the robustness, accuracy, and efficiency of key point detection.
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公开(公告)号：US11966454B2
公开(公告)日：2024-04-23
申请号：US17513493
申请日：2021-10-28
发明人： Zhang Chen , Xiao Chen , Yikang Liu , Terrence Chen , Shanhui Sun
IPC分类号： G06K9/00 , G01R33/56 , G06F18/214 , G06N3/08 , G06T3/40 , G06T5/70 , G06T7/00 , G06T11/00 , G06V10/94 , G16H30/20
CPC分类号： G06F18/2148 , G01R33/5608 , G06N3/08 , G06T3/40 , G06T5/70 , G06T7/0014 , G06T11/008 , G06V10/95 , G16H30/20 , G06T2207/10088 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004
摘要： A neural network system implements a model for generating an output image based on a received input image. The model is learned through a training process during which parameters associated with the model are adjusted so as to maximize a difference between a first image predicted using first parameter values of the model and a second image predicted using second parameter values of the model, and to minimize a difference between the second image and a ground truth image. During a first iteration of the training process the first image is predicted and during a second iteration the second image is predicted. The first parameter values are obtained during the first iteration by minimizing a difference between the first image and the ground truth image, and the second parameter values are obtained during the second iteration by maximizing the difference between the first image and the second image.
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公开(公告)号：US11965947B2
公开(公告)日：2024-04-23
申请号：US17533276
申请日：2021-11-23
发明人： Zhang Chen , Shanhui Sun , Xiao Chen , Terrence Chen
CPC分类号： G01R33/5608 , G06N3/045 , G06N3/08
摘要： In Multiplex MRI image reconstruction, a hardware processor acquires sub-sampled Multiplex MRI data and reconstructs parametric images from the sub-sampled Multiplex MRI data. A machine learning model or deep learning model uses the subsampled Multiplex MRI data as the input and parametric maps calculated from the fully sampled data, or reconstructed fully sample data, as the ground truth. The model learns to reconstruct the parametric maps directly from the subsampled Multiplex MRI data.
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