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41.发明申请公开(公告)号：US20210193298A1
公开(公告)日：2021-06-24
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：US20210157464A1
公开(公告)日：2021-05-27
申请号：US17014609
申请日：2020-09-08
Inventor： Arun Innanje , Xiao Chen , Shanhui Sun , Terrence Chen
Abstract: Cardiac features captured via an MRI scan may be tracked and analyzed using a system described herein. The system may receive a plurality of MR slices derived via the MRI scan and present the MR slices in a manner that allows a user to navigate through the MR slices. Responsive to the user selecting one of the MR slices, contextual and global cardiac information associated with the selected slice may be determined and displayed. The contextual information may correspond to the selected slice and the global information may encompass information gathered across the plurality of MR slices. A user may have the ability to navigate between the different display areas and evaluate the health of the heart with both local and global perspectives.
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公开(公告)号：US20210063520A1
公开(公告)日：2021-03-04
申请号：US16555781
申请日：2019-08-29
Inventor： Zhang Chen , Shanhui Sun , Terrence Chen
Abstract: Methods and systems for acquiring a visualization of a target. For example, a computer-implemented method for acquiring a visualization of a target includes: generating a first sampling mask; acquiring first k-space data of the target at a first phase using the first sampling mask; generating a first image of the target based at least in part on the first k-space data; generating a second sampling mask using a model based on at least one selected from the first sampling mask, the first k-space data, and the first image; acquiring second k-space data of the target at a second phase using the second sampling mask; and generating a second image of the target based at least in part on the second k-space data.
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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：US20250111517A1
公开(公告)日：2025-04-03
申请号：US18374473
申请日：2023-09-28
Inventor： Wenzhe Cui , Meng Zheng , Arun Innanje , Ziyan Wu , Terrence Chen
Abstract: An apparatus for annotating a medical image may be configured to obtain, automatically, an outline of a region of interest (ROI) in the medical image and determine, based on one or more inner control points and one or more outer control points. The one or more inner control points may be located within the ROI and the one or more outer control points may be located outside of the ROI. The outline may be subsequently adjusted based on a user input and the adjusted outline may be used to generate a segmentation of the ROI.
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Patent Agency Ranking
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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公开(公告)号：US12205277B2
公开(公告)日：2025-01-21
申请号：US17564304
申请日：2021-12-29
Inventor： Yikang Liu , Shanhui Sun , Terrence Chen , Zhang Chen , Xiao Chen
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.
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公开(公告)号：US20240420334A1
公开(公告)日：2024-12-19
申请号：US18209704
申请日：2023-06-14
Inventor： Yikang Liu , Zhang Chen , Xiao Chen , Shanhui Sun , Terrence Chen
IPC: G06T7/11
Abstract: An apparatus may obtain a sequence of medical images of a target structure and determine, using a first ANN, a first segmentation and a second segmentation of the target structure based on a first medical image and a second medical image, respectively. The first segmentation may indicate a first plurality of pixels that may belong to the target structure. The second segmentation may indicate a second plurality of pixels that may belong to the target structure. The apparatus may identify, using a second ANN, a first subset of true positive pixels among the first plurality of pixels that may belong to the target structure, and a second subset of true positive pixels among the second plurality of pixels that may belong to the target structure. The apparatus may determine a first refined segmentation and a second refined segmentation of the target structure based on the true positive pixels.
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公开(公告)号：US12141990B2
公开(公告)日：2024-11-12
申请号：US17475534
申请日：2021-09-15
Inventor： Shanhui Sun , Zhang Chen , Xiao Chen , Terrence Chen , Junshen Xu
Abstract: Deep learning based systems, methods, and instrumentalities are described herein for registering images from a same imaging modality and different imaging modalities. Transformation parameters associated with the image registration task are determined using a neural ordinary differential equation (ODE) network that comprises multiple layers, each configured to determine a respective gradient update for the transformation parameters based on a current state of the transformation parameters received by the layer. The gradient updates determined by the multiple ODE layers are then integrated and applied to initial values of the transformation parameters to obtain final parameters for completing the image registration task. The operations of the ODE network may be facilitated by a feature extraction network pre-trained to determine content features shared by the input images. The input images may be resampled into different scales, which are then processed by the ODE network iteratively to improve the efficiency of the ODE operations.
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公开(公告)号：US12141234B2
公开(公告)日：2024-11-12
申请号：US17741323
申请日：2022-05-10
Inventor： Xiao Chen , Yikang Liu , Zhang Chen , Shanhui Sun , Terrence Chen , Daniel Hyungseok Pak
IPC: G06F18/214 , A61B5/00 , G01R33/56 , G06N20/00 , G06T11/00
Abstract: Described herein are systems, methods, and instrumentalities associated with processing complex-valued MRI data using a machine learning (ML) model. The ML model may be learned based on synthetically generated MRI training data and by applying one or more meta-learning techniques. The MRI training data may be generated by adding phase information to real-valued MRI data and/or by converting single-coil MRI data into multi-coil MRI data based on coil sensitivity maps. The meta-learning process may include using portions of the training data to conduct a first round of learning to determine updated model parameters and using remaining portions of the training data to test the updated model parameters. Losses associated with the testing may then be determined and used to refine the model parameters. The ML model learned using these techniques may be adopted for a variety of tasks including, for example, MRI image reconstruction and/or de-noising.
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