-
公开(公告)号：US20230206428A1
公开(公告)日：2023-06-29
申请号：US17564304
申请日：2021-12-29
Inventor： Yikang Liu , Shanhui Sun , Terrence Chen , Zhang Chen , Xiao Chen
CPC classification number: G06T7/0012 , G06N3/02 , G06T7/11 , G06T7/194 , G06T2207/10121 , G06T2207/20081 , G06T2207/20084 , G06T2207/30101
Abstract: Described herein are systems, methods, and instrumentalities associated with image segmentation such as tubular structure segmentation. An artificial neural network is trained to segment tubular structures of interest in a medical scan image based on annotated images of a different type of tubular structures that may have a different contrast and/or appearance from the tubular structures of interest. The training may be conducted in multiple stages during which a segmentation model learned from the annotated images during a first stage may be modified to fit the tubular structures of interest in a second stage. In examples, the tubular structures of interest may include coronary arteries, catheters, guide wires, etc., and the annotated images used for training the artificial neural network may include blood vessels such as retina blood vessels.
-
公开(公告)号：US20230200767A1
公开(公告)日：2023-06-29
申请号：US17560492
申请日：2021-12-23
Inventor： Meng Zheng , Elena Zhao , Srikrishna Karanam , Ziyan Wu , Terrence Chen
CPC classification number: A61B6/468 , A61B6/469 , G06T7/149 , G06T2207/30204 , G06T2207/10081 , G06T2207/10088 , G06T2207/30096
Abstract: An automated process for data annotation of medical images includes obtaining image data from an imaging sensor, partitioning the image data, identifying an object of interest in the partitioned image data, generating an initial contour with one or more control points with respect to the object of interest, identifying a manual adjustment of one of the control points, automatically adjust a position of at least one other control point within a predetermined range of the manually adjusted control point to a new position, the new position of the at least one other control point and manually adjusted control point defining a new contour, and generating an updated image with the new contour and corresponding control points.
-
公开(公告)号：US20230187052A1
公开(公告)日：2023-06-15
申请号：US17550594
申请日：2021-12-14
Inventor： Xiao Chen , Shanhui Sun , Terrence Chen
CPC classification number: G16H30/20 , G06T7/0014 , G06T7/60 , A61B5/0044 , A61B5/055 , G06T2207/10088 , G06T2207/30048 , G06T2207/20081 , G06T2207/20084 , A61B2576/023
Abstract: 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.
-
公开(公告)号：US20230019733A1
公开(公告)日：2023-01-19
申请号：US17378448
申请日：2021-07-16
Inventor： Xiao Chen , Shuo Han , Zhang Chen , Shanhui Sun , Terrence Chen
IPC: G06T5/00 , G06T7/00 , G01R33/48 , G01R33/565
Abstract: Neural network based systems, methods, and instrumentalities may be used to remove motion artifacts from magnetic resonance (MR) images. Such a neural network based system may be trained to perform the motion artifact removal tasks without reference (e.g., without using paired motion-contaminated and motion-free MR images). Various training techniques are described herein including one that feeds the neural network with pairs of MR images with different levels of motion contamination and forces the neural network learn to correct the motion contamination by transforming a first image of a contaminated pair into a second image of the contaminated pair. Other neural network training techniques are also described with an aim to reduce the reliance on training data that is difficult to obtain.
-
公开(公告)号：US20230014745A1
公开(公告)日：2023-01-19
申请号：US17378465
申请日：2021-07-16
Inventor： Zhang Chen , Shanhui Sun , Xiao Chen , Terrence Chen
Abstract: Disclosed herein are systems, methods, and instrumentalities associated with reconstructing magnetic resonance (MR) images based on under-sampled MR data. The MR data include 2D or 3D information, and may encompass multiple contrasts and multiple coils. The MR images are reconstructed using deep learning (DL) methods, which may accelerate the scan and/or image generation process. Challenges imposed by the large quantity of the MR data and hardware limitations are overcome by separately reconstructing MR images based on respective subsets of contrasts, coils, and/or readout segments, and then combining the reconstructed MR images to obtain desired multi-contrast results.
-
Patent Agency Ranking
公开(公告)号：US11379727B2
公开(公告)日：2022-07-05
申请号：US16694298
申请日：2019-11-25
Inventor： Abhishek Sharma , Arun Innanje , Ziyan Wu , Shanhui Sun , Terrence Chen
Abstract: Methods and systems for enhancing a distributed medical network. For example, a computer-implemented method includes inputting training data corresponding to each local computer into their corresponding machine learning model; generating a plurality of local losses including generating a local loss for each machine learning model based at least in part on the corresponding training data; generating a plurality of local parameter gradients including generating a local parameter gradient for each machine learning model based at least in part on the corresponding local loss; generating a global parameter update based at least in part on the plurality of local parameter gradients; and updating each machine learning model hosted at each local computer of the plurality of local computers by at least updating their corresponding active parameter set based at least in part on the global parameter update.
-
公开(公告)号：US11348291B2
公开(公告)日：2022-05-31
申请号：US16699540
申请日：2019-11-29
Inventor： Puyang Wang , Zhang Chen , Shanhui Sun , Terrence Chen
Abstract: A system for reconstructing magnetic resonance images includes a processor that is configured to obtain, from a magnetic resonance scanner, sub-sampled k-space data; apply an inverse fast fourier transform to the sub-sampled k-space data to generate a preliminary image; and process the preliminary image via a trained cascaded recurrent neural network to reconstruct a magnetic resonance image.
-
公开(公告)号：US20220101537A1
公开(公告)日：2022-03-31
申请号：US17039279
申请日：2020-09-30
Inventor： Shanhui Sun , Hanchao Yu , Xiao Chen , Terrence Chen
Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with estimating the motion of an anatomical structure. The motion estimation may be performed using a feature pyramid and/or a motion pyramid that correspond to multiple image scales. The motion estimation may be performed using neural networks and parameters that are learned via a training process involving a student network and a teacher network pre-pretrained with abilities to apply progressive motion compensation.
-
公开(公告)号：US20220022817A1
公开(公告)日：2022-01-27
申请号：US16934623
申请日：2020-07-21
Inventor： Zhang Chen , Shanhui Sun , Xiao Chen , Terrence Chen
IPC: A61B5/00 , A61B5/055 , A61B5/0452 , G01R33/48
Abstract: A method includes acquiring MRI data, using an algorithm to predict cardiac cycles from the acquired MRI data, and operating on sections of the acquired MRI data corresponding to selected portions of the predicted cardiac cycles.
-
公开(公告)号：US11120585B2
公开(公告)日：2021-09-14
申请号：US16699092
申请日：2019-11-28
Inventor： Zhang Chen , Shanhui Sun , Terrence Chen
Abstract: The present disclosure relates to a system. The system may obtain a k-space dataset according to magnetic resonance (MR) signals acquired by a magnetic resonance imaging (MRI) scanner. The system may also generate, based on the k-space dataset using an image reconstruction model that includes a sequence sub-model and a domain translation sub-model, a reconstructed image by: inputting at least a part of the k-space dataset into the sequence sub-model; outputting, from the sequence sub-model, a feature representation of the k-space dataset; inputting the feature representation of the k-space dataset into the domain translation sub-model; and outputting, from the domain translation sub-model, the reconstructed image.
-
-
-
-
-
-
-
-
-
Search Results
125
records
(took 0.032 seconds)
