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公开(公告)号：US12138015B2
公开(公告)日：2024-11-12
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: G06T7/00 , A61B5/00 , G06F18/21 , G06F18/214 , G06T7/50 , G06T7/70 , G06T7/90 , G06T17/00 , G06T17/20 , G06V10/40 , G06V10/42 , G06V10/764 , G06V10/774 , G06V10/778 , G06V10/82 , G06V20/62 , G06V20/64 , G06V40/10 , G06V40/20 , G16H10/60 , G16H30/20 , G16H30/40
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：US12045958B2
公开(公告)日：2024-07-23
申请号：US17378448
申请日：2021-07-16
Inventor： Xiao Chen , Shuo Han , Zhang Chen , Shanhui Sun , Terrence Chen
IPC: G06T5/70 , G01R33/48 , G01R33/565 , G06T7/00
CPC classification number: G06T5/70 , G01R33/4818 , G01R33/56509 , G06T7/0014 , G06T2207/10088 , G06T2207/20081 , G06T2207/20084
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：US20240169486A1
公开(公告)日：2024-05-23
申请号：US17989205
申请日：2022-11-17
Inventor： Yikang Liu , Zhang Chen , Xiao Chen , Shanhui Sun , Terrence Chen
CPC classification number: G06T5/50 , G06T5/002 , G06T5/003 , G06T2207/10016 , G06T2207/10121 , G06T2207/20081 , G06T2207/20084 , G06T2207/30004
Abstract: Deblurring and denoising a medical image such as X-ray fluoroscopy images may be challenging, and deep-learning based techniques may be employed to meet the challenge. An artificial neural network (ANN) may be trained using training images with synthetic noise and as well as training images with real noise. The parameters of the ANN may be adjusted during the training based on at least a first loss designed to maintain continuity between consecutive medical images generated by the ANN and a second loss designed to maintain similarity of patches inside a medical image generated by the ANN. The parameters of the ANN may be further adjusted based on a third loss that may be calculated from ground truth associated with the synthetic training images. Transfer learning between the synthetic images and the real images may be accomplished using these techniques.
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公开(公告)号：US20240153094A1
公开(公告)日：2024-05-09
申请号：US17981988
申请日：2022-11-07
Inventor： Yikang Liu , Shanhui Sun , Terrence Chen
CPC classification number: G06T7/11 , G06T7/0012 , G16H50/50 , G06T2207/30101
Abstract: Described herein are systems, methods, and instrumentalities associated with automatically annotating a tubular structure (e.g., such as a blood vessel, a catheter, etc.) in medical images. The automatic annotation may be accomplished using a machine-learning image annotation model and based on a marking of the tubular structure created or confirmed by a user. A user interface may be provided for a user to create, modify, and/or confirm the marking, and the ML model may be trained using a training dataset that comprises marked images of the tubular structure paired with ground truth annotations of the tubular structure.
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公开(公告)号：US11967004B2
公开(公告)日：2024-04-23
申请号：US17378465
申请日：2021-07-16
Inventor： Zhang Chen , Shanhui Sun , Xiao Chen , Terrence Chen
IPC: G06T11/00 , A61B5/00 , A61B5/055 , G06F18/214 , G06F18/22 , G06K9/62 , G06N3/04 , G06N3/08 , G06T5/50
CPC classification number: G06T11/005 , A61B5/055 , A61B5/7267 , G06F18/214 , G06F18/22 , G06N3/04 , G06N3/08 , G06T5/50 , G06T2207/10088 , G06T2207/20081 , G06T2207/20084 , G06T2207/20221
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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Patent Agency Ranking
公开(公告)号：US11948288B2
公开(公告)日：2024-04-02
申请号：US17340635
申请日：2021-06-07
Inventor： Xiao Chen , Shuo Han , Zhang Chen , Shanhui Sun , Terrence Chen
CPC classification number: G06T5/70 , G06N3/08 , G16H30/20 , G16H30/40 , G16H50/20 , G16H50/70 , G06T2207/10088 , G06T2207/20056 , G06T2207/20081 , G06T2207/20084 , G16H50/50
Abstract: Motion contaminated magnetic resonance (MR) images for training an artificial neural network to remove motion artifacts from the MR images are difficult to obtain. Described herein are systems, methods, and instrumentalities for injecting motion artifacts into clean MR images and using the artificially contaminated images for machine learning and neural network training. The motion contaminated MR images may be created based on clean source MR images that are associated with multiple physiological cycles of a scanned object, and by deriving MR data segments for the multiple physiological cycles based on the source MR images. The MR data segments thus derived may be combined to obtain a simulated MR data set, from which one or more target MR images may be generated to exhibit a motion artifact. The motion artifact may be created by manipulating the source MR images and/or controlling the manner in which the MR data set or the target MR images are generated.
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公开(公告)号：US20240062047A1
公开(公告)日：2024-02-22
申请号：US17891702
申请日：2022-08-19
Inventor： Zhang Chen , Siyuan Dong , Shanhui Sun , Xiao Chen , Yikang Liu , Terrence Chen
IPC: G06N3/04
CPC classification number: G06N3/0472 , G06N3/0454 , G06T2207/20081 , G06T2207/20084
Abstract: Deep learning-based systems, methods, and instrumentalities are described herein for MRI reconstruction and/or refinement. An MRI image may be reconstructed based on under-sampled MRI information and a generative model may be trained to refine the reconstructed image, for example, by increasing the sharpness of the MRI image without introducing artifacts into the image. The generative model may be implemented using various types of artificial neural networks including a generative adversarial network. The model may be trained based on an adversarial loss and a pixel-wise image loss, and once trained, the model may be used to improve the quality of a wide range of 2D or 3D MRI images including those of a knee, brain, or heart.
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公开(公告)号：US20240013510A1
公开(公告)日：2024-01-11
申请号：US17858663
申请日：2022-07-06
Inventor： Yikang Liu , Luojie Huang , Zhang Chen , Xiao Chen , Shanhui Sun
IPC: G06V10/75 , G06V10/764 , G06T7/73 , G06N3/04 , G06N3/08
CPC classification number: G06V10/751 , G06V10/764 , G06T7/73 , G06N3/0454 , G06N3/08 , G06T2207/20084 , G06T2207/20081
Abstract: Described herein are systems, methods, and instrumentalities associated with tracking groups of small objects in medical images. The tracking may be accomplished by, for each one of a sequence of medical images, determining a plurality of candidate objects captured in the medical image, grouping the plurality of candidate objects into a plurality of groups of candidate objects and dividing the medical image into a plurality of regions that each surrounds a corresponding group of candidate objects. Each of the plurality of regions may be examined to extract respective features associated with each corresponding group of candidate objects. A match between a first group of candidate objects in a first medical image and a second group of candidate objects in a second medical image may be determined based on first features associated with the first group and second features associated with the second group.
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公开(公告)号：US20230206429A1
公开(公告)日：2023-06-29
申请号：US17564317
申请日：2021-12-29
Inventor： Yikang Liu , Luojie Huang , Shanhui Sun , Zhang Chen , Xiao Chen
IPC: G06T7/00 , G06V10/762 , G06V10/82 , G06N3/02
CPC classification number: G06T7/0012 , G06V10/762 , G06V10/82 , G06N3/02 , G06T2207/10121 , G06T2207/20081 , G06T2207/20084 , G06T2207/30104
Abstract: Described herein are systems, methods, and instrumentalities associated with automatically detecting and enhancing multiple objects in medical scan images. The detection and/or enhancement may be accomplished utilizing artificial neural networks such as one or more classification neural networks and/or one or more graph neural networks. The neural networks may be used to detect areas in the medical scan images that may correspond to the objects of interest and cluster the areas belonging to a same object into a respective cluster. These tasks may be accomplished, for example, by representing the areas corresponding to the objects of interest and their interrelationships with a graph and processing the graph through the one or more graph neural networks so that the areas belonging to each object may be properly labeled and clustered. The clusters may then be used to enhance the objects of interests in one or more output scan images.
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公开(公告)号：US20230206401A1
公开(公告)日：2023-06-29
申请号：US17564348
申请日：2021-12-29
Inventor： Yikang Liu , Shanhui Sun , Terrence Chen , Zhang Chen , Xiao Chen
CPC classification number: G06T5/002 , G06T5/50 , G06N3/0454 , G06N3/08 , G16H30/40 , G06T2207/20081 , G06T2207/20084 , G06T2207/10064
Abstract: Described herein are systems, methods, and instrumentalities associated with denoising medical images such as fluoroscopic images using deep learning techniques. A first artificial neural network (ANN) is trained to denoise an input medical image in accordance with a provided target noise level. The training of the first ANN is conducted by pairing a noisy input image with target denoised images that include different levels of noise. These target denoised images are generated using a second ANN as intermediate outputs of the second ANN during different training iterations. As such, the first ANN may learn to perform the denoising task in an unsupervised manner without requiring noise-free training images as the ground truth.
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