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公开(公告)号：US20250094484A1
公开(公告)日：2025-03-20
申请号：US18369766
申请日：2023-09-18
Inventor： Meng Zheng , Ziyan Wu , Benjamin Planche , Zhongpai Gao , Terrence Chen
Abstract: Described herein are machine learning (ML) based on systems, methods, and instrumentalities associated with image search and/or retrieval. An apparatus as described herein may obtain a query image and a textual description associated with the query image, and generate, using an artificial neural network (ANN), a feature representation that may represent the image and the textual description as an associated pair. Based on the feature representation, the apparatus may identify one or more images from an image repository and provide an indication regarding the one or more identified images, for example, as a ranked list.
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公开(公告)号：US12141420B2
公开(公告)日：2024-11-12
申请号：US17960367
申请日：2022-10-05
Inventor： Arun Innanje , Zheng Peng , Ziyan Wu , Qin Liu , Terrence Chen
IPC: G06T7/12 , G06F3/04842 , G06T11/00
Abstract: Click based contour editing includes detecting a selection input with respect to an image presented on a graphical user interface; designating an area of the image corresponding to the selection input as a region of interest; detecting at least one other selection input on the graphical user interface with respect to the image; determining if the at least one other selection input is within the region of interest or outside of the region of interest; and if the at least one other selection input is within the region of interest, excluding the portion of the image corresponding to the other input; or if the other selection input is outside of the region of interest, including the portion of the image corresponding to an area of the image associated with the other selection input.
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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：US20240233419A9
公开(公告)日：2024-07-11
申请号：US18128290
申请日：2023-03-30
Inventor： Meng Zheng , Wenzhe Cui , Ziyan Wu , Arun Innanje , Benjamin Planche , Terrence Chen
CPC classification number: G06V20/70 , G06V10/235
Abstract: Described herein are systems, methods, and instrumentalities associated with automatically annotating a 3D image dataset. The 3D automatic annotation may be accomplished based on a 2D manual annotation provided by an annotator and by propagating, using a set of machine-learning (ML) based techniques, the 2D manual annotation through sequences of 2D images associated with the 3D image dataset. The automatically annotated 3D image dataset may then be used to annotate other 3D image datasets upon passing a readiness assessment conducted using another set of ML based techniques. The automatic annotation of the images may be performed progressively, e.g., by processing a subset or batch of images at a time, and the ML based techniques may be trained to ensure consistency between a forward propagation and a backward propagation.
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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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Patent Agency Ranking
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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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：US20240099774A1
公开(公告)日：2024-03-28
申请号：US17955279
申请日：2022-09-28
Inventor： Meng Zheng , Benjamin Planche , Ziyan Wu , Terrence Chen
CPC classification number: A61B34/10 , A61B34/30 , G06T17/20 , G16H50/50 , A61B2034/105 , A61B2034/107
Abstract: Systems, methods and instrumentalities are described herein for automatically devising and executing a surgical plan associated with a patient in a medical environment, e.g., under the supervision of a medical professional. The surgical plan may be devised based on images of the medical environment captured by one or more sensing devices. A processing device may determine, based on all or a first subset of the images, a patient model that may indicate a location and a shape of an anatomical structure of the patient and determine, based on all or a second subset of the images, an environment model that may indicate a three-dimensional (3D) spatial layout of the medical environment. The surgical plan may be devised based on the patient model and the environment model, and may indicate at least a movement path of a medical device towards the anatomical structure of the patient.
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公开(公告)号：US20240079128A1
公开(公告)日：2024-03-07
申请号：US17901349
申请日：2022-09-01
Inventor： Terrence Chen
Abstract: Traditional ways of seeking and receiving healthcare services are time-consuming and cumbersome. A digital healthcare service platform built on artificial intelligence (AI) technologies may improve the experience and efficiency associated with these services. The digital healthcare platform may use AI models trained for image classification and/or natural language processing to generate preliminary diagnoses for a care seeker based on images or descriptions provided by the care seeker. The digital healthcare platform may also use AI models to match service providers with the care seeker, and/or manage the logistical aspects of a service (e.g., coordinating activities, scheduling appointments, etc.) for the care seeker.
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