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公开(公告)号：US20230196742A1
公开(公告)日：2023-06-22
申请号：US17557984
申请日：2021-12-21
Inventor： Shanhui Sun , Yikang Liu , Xiao Chen , Zhang Chen , Terrence Chen
IPC: G06V10/774 , G06V10/82 , G06T7/00
CPC classification number: G06V10/7747 , G06V10/82 , G06T7/0012 , G06T2207/30004
Abstract: Described herein are systems, methods, and instrumentalities associated with landmark detection. The detection may be accomplished by determining a graph representation of a plurality of hypothetical landmarks detected in one or more medical images. The graph representation may include nodes that represent the hypothetical landmarks and edges that represent the relationships between paired hypothetical landmarks. The graph representation may be processed using a graph neural network such a message passing graph neural network, by which the landmark detection problem may be converted and solved as a graph node labeling problem.
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公开(公告)号：US20230135995A1
公开(公告)日：2023-05-04
申请号：US17513320
申请日：2021-10-28
Inventor： Xiao Chen , Zhang Chen , Shanhui Sun , Terrence Chen
Abstract: Disclosed herein are systems, methods, and instrumentalities associated with reconstructing magnetic resonance (MR) images based on multi-slice, under-sampled MRI data (e.g., k-space data). The multi-slice MRI data may be acquired using a simultaneous multi-slice (SMS) technique and MRI information associated with multiple MRI slices may be entangled in the multi-slice MRI data. A neural network may be trained and used to disentangle the MRI information and reconstruct MRI images for the different slices. A data consistency component may be used to estimate k-space data based on estimates made by the neural network, from which respective MRI images associated with multiple MRI slices may be obtained by applying a Fourier transform to the k-space data.
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13.发明授权公开(公告)号：US11545255B2
公开(公告)日：2023-01-03
申请号：US16722429
申请日：2019-12-20
Inventor： Shanhui Sun , Zhang Chen , Terrence Chen
Abstract: Methods and systems for classifying an image. For example, a method includes: inputting a medical image into a recognition model, the recognition model configured to: generate one or more attribute distributions that are substantially Gaussian when inputted with a normal image; and generate one or more attribute distributions that are substantially non-Gaussian when inputted with an abnormal image; generating, by the recognition model, one or more attribute distributions corresponding to medical image; generating a marginal likelihood corresponding to the likelihood of a sample image substantially matching the medical image, the sample image generated by sampling, by a generative model, the one or more attribute distributions; and generating a classification by at least: if the marginal likelihood is greater than or equal to a predetermined likelihood threshold, determining the image to be normal; and if the marginal likelihood is less than the predetermined likelihood threshold, determining the image to be abnormal.
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公开(公告)号：US11521323B2
公开(公告)日：2022-12-06
申请号：US17076641
申请日：2020-10-21
Inventor： Yimo Guo , Xiao Chen , Shanhui Sun , Terrence Chen
Abstract: A bullseyes plot may be generated based on cardiac magnetic resonance imaging (CMRI) to facilitate the diagnosis and treatment of heart diseases. Described herein are systems, methods, and instrumentalities associated with bullseyes plot generation. A plurality of myocardial segments may be obtained for constructing the bullseye plot based on landmark points detected in short-axis and long-axis magnetic resonance (MR) slices of the heart and by arranging the short-axis MR slices sequentially in accordance with the order in which the slices are generated during the CMRI. The sequential order of the short-axis MR slices may be determined utilizing projected locations of the short-axis MR slices on a long-axis MR slice and respective distances of the projected locations to a landmark point of the long-axis MR slice. The myocardium and/or landmark points may be identified in the short-axis and/or long-axis MR slices using artificial neural networks.
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公开(公告)号：US11514573B2
公开(公告)日：2022-11-29
申请号：US17014573
申请日：2020-09-08
Inventor： Qiaoying Huang , Shanhui Sun , Zhang Chen , Terrence Chen
IPC: G06T7/00 , G06T7/11 , G06T7/55 , G06K9/62 , G06N3/04 , G16H50/50 , G16H50/30 , G16H30/40 , G06F3/0485 , G06T11/20 , G06T13/80 , G06T19/00 , G06T7/73 , G06T7/246 , A61B5/00 , A61B5/11 , G06T3/00 , G06N3/08
Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with estimating a thickness of an anatomical structure based on a visual representation of the anatomical structure and a machine-learned thickness prediction model. The visual representation may include an image or a segmentation mask of the anatomical structure. The thickness prediction model may be learned based on ground truth information derived by applying a partial differential equation such as Laplace's equation to the visual representation and solving the partial differential equation. When the visual representation includes an image of the anatomical structure, the systems, methods and instrumentalities described herein may also be capable of generating a segmentation mask of the anatomical structure based on the image.
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Patent Agency Ranking
公开(公告)号：US20210272297A1
公开(公告)日：2021-09-02
申请号：US17154450
申请日：2021-01-21
Inventor： Xiao Chen , Shanhui Sun , Terrence Chen
Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with cardiac assessment. An apparatus as described herein may obtain electrocardiographic imaging (ECGI) information associated with a human heart and magnetic resonance imaging (MRI) information associated with the human heart, and integrate the ECGI and MRI information using a machine-learned model. Using the integrated ECGI and MRI information, the apparatus may predict target ablation sites, estimate electrophysiology (EP) measurements, and/or simulate the electrical system of the human heart.
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公开(公告)号：US20210158543A1
公开(公告)日：2021-05-27
申请号：US17070705
申请日：2020-10-14
Inventor： Shanhui Sun , Hanchao Yu , Qiaoying Huang , Zhang Chen , Terrence Chen
Abstract: Described herein are systems, methods and instrumentalities associated with motion tracking and strain determination. A motion tracking apparatus as described herein may track the motion of an anatomical structure from a source image to a target image and determine corresponding points on one or more surfaces of the anatomical structure in both the source image and the target image. Using these surface points, the motion tracking apparatus may calculate one or more strain parameters associated with the anatomical structure and provide the strain parameters for medical diagnosis and/or treatment.
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公开(公告)号：US20210158511A1
公开(公告)日：2021-05-27
申请号：US17014594
申请日：2020-09-08
Inventor： Yimo Guo , Shanhui Sun , Terrence Chen
Abstract: Described herein are systems, methods and instrumentalities associated with image segmentation. The systems, methods and instrumentalities have a hierarchical structure for producing a coarse segmentation of an anatomical structure and then refining the coarse segmentation based on a shape prior of the anatomical structure. The coarse segmentation may be generated using a multi-task neural network and based on both a segmentation loss and a regression loss. The refined segmentation may be obtained by deforming the shape prior using one or more of a shape-based model or a learning-based model.
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公开(公告)号：US12285283B2
公开(公告)日：2025-04-29
申请号：US17948822
申请日：2022-09-20
Inventor： Yikang Liu , Zhang Chen , Xiao Chen , Shanhui Sun , Terrence Chen
Abstract: A 3D anatomical model of one or more blood vessels of a patient may be obtained using CT angiography, while a 2D image of the blood vessels may be obtained based on fluoroscopy. The 3D model may be registered with the 2D image based on a contrast injection site identified on the 3D model and/or in the 2D image. A fused image may then be created to depict the overlaid 3D model and 2D image, for example, on a monitor or through a virtual reality headset. The injection site may be determined automatically or based on a user input that may include a bounding box drawn around the injection site on the 3D model, a selection of an automatically segmented area in the 3D model, etc.
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公开(公告)号：US20250029720A1
公开(公告)日：2025-01-23
申请号：US18225009
申请日：2023-07-21
Inventor： Shanhui Sun , Zhang Chen , Xiao Chen , Yikang Liu , Lin Zhao , Terrence Chen , Arun Innanje , Abhishek Sharma , Wenzhe Cui , Xiao Fan
Abstract: Disclosed herein are deep-learning based systems, methods, and instrumentalities for medical decision-making. A system as described herein may implement an artificial neural network (ANN) that may include multiple encoder neural networks and a decoder neural network. The multiple encoder neural networks may be configured to receive multiple types of patient data (e.g., text and image based patient data) and generate respective encoded representations of the patient data. The decoder neural network (e.g., a transformer decoder) may be configured to receive the encoded representations and generate a medical decision, a medical summary, or a medical questionnaire based on the encoded representations. In examples, the decoder neural network may be configured to implement a large language model (LLM) that may be pre-trained for performing the aforementioned tasks.
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