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91.发明公开公开(公告)号：US20240233212A1
公开(公告)日：2024-07-11
申请号：US18095149
申请日：2023-01-10
Inventor： Zhang Chen , Shanhui Sun , Xiao Chen , Yikang Liu , Terrence Chen , Chi Zhang
CPC classification number: G06T11/008 , G06T3/20 , G06T3/4046 , G06T5/50 , G06T7/11 , G06T9/002 , G06T2207/10088 , G06T2207/20021 , G06T2207/20084 , G06T2207/20221 , G06T2207/30016 , G06T2211/424
Abstract: Described herein are systems, methods, and instrumentalities associated with using a multi-layer perceptron (MLP) neural network to process medical images of an anatomical structure. The processing may include padding an input image in accordance with the training of the MLP neural network, splitting the input image (e.g., the padded input image) into patches of a same size, and processing the patches through the MLP neural network over one or more iterations. During an iteration of the processing, the patches may be processed separately and re-combined into an intermediate image before the intermediate image is shifted to concatenate portions of the image that are derived from different patches. This way, global features of the anatomical structure may be learned and used to improve the quality of the image generated by the MLP neural network, without incurring significant computation or memory costs.
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公开(公告)号：US20240164758A1
公开(公告)日：2024-05-23
申请号：US17989251
申请日：2022-11-17
Inventor： Ziyan Wu , Shanhui Sun , Arun Innanje , Benjamin Planche , Abhishek Sharma , Meng Zheng
CPC classification number: A61B8/5261 , A61B6/5247 , A61B8/4254 , A61B8/466 , A61B8/5223
Abstract: Sensing device(s) may be installed in a medical environment to captures images of the medical environment, which may include an ultrasound probe and a patient. The images may be processed to determine, automatically, the position of the ultrasound probe relative to the patient's body. Based on the determined position, ultrasound image(s) taken by the ultrasound probe may be aligned with a 3D patient model and displayed with the 3D patient model, for example, to track the movements of the ultrasound probe and/or provide a visual representation of the anatomical structure(s) captured in the ultrasound image(s) against the 3D patient model. The ultrasound images may also be used to reconstruct a 3D ultrasound model of the anatomical structure(s).
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公开(公告)号：US20240090859A1
公开(公告)日：2024-03-21
申请号：US17948822
申请日：2022-09-20
Inventor： Yikang Liu , Zhang Chen , Xiao Chen , Shanhui Sun , Terrence Chen
IPC: A61B6/00
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：US11693919B2
公开(公告)日：2023-07-04
申请号：US16908148
申请日：2020-06-22
Inventor： Xiao Chen , Pingjun Chen , Zhang Chen , Terrence Chen , Shanhui Sun
IPC: G06F18/213 , G16H50/50 , G16H30/20 , G16H50/70 , G16H50/20 , G16H30/40 , G06V40/20 , G06N3/08 , G06F18/214
CPC classification number: G06F18/213 , G06F18/2148 , G06N3/08 , G06V40/20 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/50 , G16H50/70 , G06V2201/031
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 utilizing pre-learned knowledge of the anatomy of the anatomical structure. The anatomical knowledge may be learned via a variational autoencoder, which may then be used to optimize the parameters of a motion estimation neural network system such that, when performing motion estimation for the anatomical structure, the motion estimation neural network system may produce results that conform with the underlying anatomy of anatomical structure.
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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.
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Patent Agency Ranking
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.
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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.
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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.
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公开(公告)号：US20220277836A1
公开(公告)日：2022-09-01
申请号：US17737694
申请日：2022-05-05
Inventor： Ziyan Wu , Srikrishna Karanam , Arun Innanje , Shanhui Sun , Abhishek Sharma , Yimo Guo , Zhang Chen
IPC: G16H30/40 , G06T7/00 , G06T7/90 , G06T17/00 , G06T7/50 , G06T7/70 , G06K9/62 , G06T17/20 , G16H10/60 , G16H30/20 , A61B5/00 , G06V10/40 , G06V10/42 , G06V20/64 , G06V40/10 , G06V40/20 , G06V20/62
Abstract: A medical system may utilize a modular and extensible sensing device to derive a two-dimensional (2D) or three-dimensional (3D) human model for a patient in real-time based on images of the patient captured by a sensor such as a digital camera. The 2D or 3D human model may be visually presented on one or more devices of the medical system and used to facilitate a healthcare service provided to the patient. In examples, the 2D or 3D human model may be used to improve the speed, accuracy and consistency of patient positioning for a medical procedure. In examples, the 2D or 3D human model may be used to enable unified analysis of the patient's medical conditions by linking different scan images of the patient through the 2D or 3D human model. In examples, the 2D or 3D human model may be used to facilitate surgical navigation, patient monitoring, process automation, and/or the like.
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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.
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