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公开(公告)号：US12232900B2
公开(公告)日：2025-02-25
申请号：US17560492
申请日：2021-12-23
Inventor： Meng Zheng , Elena Zhao , Srikrishna Karanam , Ziyan Wu , Terrence Chen
Abstract: An automated process for data annotation of medical images includes obtaining image data from an imaging sensor, partitioning the image data, identifying an object of interest in the partitioned image data, generating an initial contour with one or more control points with respect to the object of interest, identifying a manual adjustment of one of the control points, automatically adjust a position of at least one other control point within a predetermined range of the manually adjusted control point to a new position, the new position of the at least one other control point and manually adjusted control point defining a new contour, and generating an updated image with the new contour and corresponding control points.
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公开(公告)号：US12190508B2
公开(公告)日：2025-01-07
申请号：US17726383
申请日：2022-04-21
Inventor： Yikang Liu , Shanhui Sun , Terrence Chen , Zhang Chen , Xiao Chen
Abstract: Described herein are systems, methods, and instrumentalities associated with medical image enhancement. The medical image may include an object of interest and the techniques disclosed herein may be used to identify the object and enhance a contrast between the object and its surrounding area by adjusting at least the pixels associated with the object. The object identification may be performed using an image filter, a segmentation mask, and/or a deep neural network trained to separate the medical image into multiple layers that respectively include the object of interest and the surrounding area. Once identified, the pixels of the object may be manipulated in various ways to increase the visibility of the object. These may include, for example, adding a constant value to the pixels of the object, applying a sharpening filter to those pixels, increasing the weight of those pixels, and/or smoothing the edge areas surrounding the object of interest.
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公开(公告)号：US20240378731A1
公开(公告)日：2024-11-14
申请号：US18195009
申请日：2023-05-09
Inventor： Zhongpai Gao , Abhishek Sharma , Meng Zheng , Benjamin Planche , Ziyan Wu , Terrence Chen
Abstract: Detecting motions associated with a body part of a patient may include using an image sensor installed inside a medical scanner to capture first and second images of the patient inside the medical scanner, wherein the first image may depict the patient in a first state and the second image may depict the patient in a second state. A first area, in the first image, that corresponds to the body part of the patient may be identified and a second area, in the second image, that corresponds to the body part may also be identified so that a first plurality of features may be extracted from the first area of the first image and a second plurality of features may be extracted from the second area of the second image. A motion associated with the body part of the patient may be determined based on the first and second pluralities of features.
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公开(公告)号：US20240346684A1
公开(公告)日：2024-10-17
申请号：US18133185
申请日：2023-04-11
Inventor： Meng Zheng , Jun Wang , Benjamin Planche , Zhongpai Gao , Ziyan Wu
IPC: G06T7/73
CPC classification number: G06T7/73 , G06T2207/20081 , G06T2207/30196
Abstract: Disclosed herein are systems, methods and instrumentalities associated with multi-person joint location and pose estimation based on an image that depicts multiple people in a scene, where at least some of the joint locations of a person may be blocked or obstructed by other people or objects in the scene. The estimation may be performed by detecting and grouping joint locations in the image using a bottom-up approach, and refining each group of detected joint locations by recovering obstructed joint location(s) that may be missing from the group. The detection, grouping, and/or refinement may be accomplished based on one or more machine learning (ML) models that may be implemented using artificial neural networks such as convolutional neural networks.
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公开(公告)号：US12094080B2
公开(公告)日：2024-09-17
申请号：US17943724
申请日：2022-09-13
Inventor： Yikang Liu , Shanhui Sun , Terrence Chen
IPC: G06T3/4046 , G06T7/11 , G06F3/0482
CPC classification number: G06T3/4046 , G06T7/11 , G06F3/0482 , G06T2200/24 , G06T2207/20081
Abstract: A magnification system for magnifying an image based on trained neural networks is disclosed. The magnification system receives a first user input associated with a selection of a region of interest (ROI) within an input image of a site and a second user input associated with a first magnification factor of the selected ROI. The first magnification factor is associated with a magnification of the ROI in the input image. The ROI is modified based on an application of a first neural network model on the ROI. The modification of the ROI corresponds to a magnified image that is predicted in accordance with the first magnification factor. A display device is controlled to display the modified ROI.
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Patent Agency Ranking
公开(公告)号：US20240256707A1
公开(公告)日：2024-08-01
申请号：US18103249
申请日：2023-01-30
Inventor： Benjamin Planche , Zikui Cai , Zhongpai Gao , Ziyan Wu , Meng Zheng , Terrence Chen
CPC classification number: G06F21/6254 , G06V10/7715
Abstract: A person's privacy is protected by the law in many settings and disclosed herein are systems, methods, and instrumentalities associated with anonymizing an image of a person while still preserving the visual saliency and/or utility of the image for one or more downstream tasks. These objectives may be accomplished using various machine-learning (ML) techniques such as ML models trained for extracting identifying and residual features from the input image as well as ML models trained for transforming the identifying features into identity-concealing features and for preserving the utility features of the image. An output image may be generated based on the various ML models, wherein the identity of the person may be substantially disguised in the output image while the background and utility attributes of the original image may be substantially maintained in the output image.
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公开(公告)号：US20240177326A1
公开(公告)日：2024-05-30
申请号：US17994696
申请日：2022-11-28
Inventor： Srikrishna Karanam , Meng Zheng , Ziyan Wu
CPC classification number: G06T7/50 , G06N3/02 , G06T2207/20084
Abstract: A human model such as a 3D human mesh may be generated for a person in a medical environment based on one or more images of the person. The images may be captured using a sensing device that may be attached to an existing medical device such as a medical scanner in the medical environment. Such an arrangement may ensure that unblocked views of the person (e.g., body keypoints of the person) may be obtained and used to generate the human model. The position of the medical device in the medical environment may be determined and used to facilitate the human model construction such that the pose and body shape of the person in the medical environment may be accurately represented by the human model.
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公开(公告)号：US20240153089A1
公开(公告)日：2024-05-09
申请号：US17982023
申请日：2022-11-07
Inventor： Xiao Chen , Zhang Chen , Terrence Chen , Shanhui Sun
CPC classification number: G06T7/0016 , A61B5/0044 , A61B5/055 , A61B5/7267 , G06T7/30 , G06T2207/10088 , G06T2207/20081 , G06T2207/20212 , G06T2207/30048
Abstract: Real-time cardiac MRI images may be captured continuously across multiple cardiac phases and multiple slices. Machine learning-based techniques may be used to determine spatial (e.g., slices and/or views) and temporal (e.g., cardiac cycles and/or cardiac phases) properties of the cardiac images such that the images may be arranged into groups based on the spatial and temporal properties of the images and the requirements of a cardiac analysis task. Different groups of the cardiac MRI images may also be aligned with each other based on the timestamps of the images and/or by synthesizing additional images to fill in gaps.
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公开(公告)号：US11967136B2
公开(公告)日：2024-04-23
申请号：US17557984
申请日：2021-12-21
Inventor： Shanhui Sun , Yikang Liu , Xiao Chen , Zhang Chen , Terrence Chen
IPC: G06V10/774 , G06T7/00 , G06V10/82
CPC classification number: G06V10/7747 , G06T7/0012 , G06V10/82 , 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：US11948250B2
公开(公告)日：2024-04-02
申请号：US17513534
申请日：2021-10-28
Inventor： Srikrishna Karanam , Meng Zheng , Ziyan Wu
CPC classification number: G06T17/20 , A61B34/10 , A61B90/361 , G06T7/70 , A61B2034/105 , A61B2034/107 , A61B2090/367 , G06T2207/30196
Abstract: Systems, methods, and instrumentalities are described herein for constructing a multi-view patient model (e.g., a 3D human mesh model) based on multiple single-view models of the patient. Each of the single-view models may be generated based on images captured by a sensing device and, dependent on the field of the view of the sensing device, may depict some keypoints of the patient's body with a higher accuracy and other keypoints of the patient's body with a lower accuracy. The multi-view patient model may be constructed using respective portions of the single-view models that correspond to accurately depicted keypoints. This way, a comprehensive and accurate depiction of the patient's body shape and pose may be obtained via the multi-view model even if some keypoints of the patient's body are blocked from a specific sensing device.
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