-
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.
-
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.
-
公开(公告)号：US20230196742A1
公开(公告)日：2023-06-22
申请号：US17557984
申请日：2021-12-21
Inventor： Shanhui Sun , Yikang Liu , Xiao Chen , Zhang Chen , Terrence Chen
IPC: G06V10/774 , G06V10/82 , G06T7/00
CPC classification number: G06V10/7747 , G06V10/82 , G06T7/0012 , 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.
-
公开(公告)号：US20230135995A1
公开(公告)日：2023-05-04
申请号：US17513320
申请日：2021-10-28
Inventor： Xiao Chen , Zhang Chen , Shanhui Sun , Terrence Chen
Abstract: Disclosed herein are systems, methods, and instrumentalities associated with reconstructing magnetic resonance (MR) images based on multi-slice, under-sampled MRI data (e.g., k-space data). The multi-slice MRI data may be acquired using a simultaneous multi-slice (SMS) technique and MRI information associated with multiple MRI slices may be entangled in the multi-slice MRI data. A neural network may be trained and used to disentangle the MRI information and reconstruct MRI images for the different slices. A data consistency component may be used to estimate k-space data based on estimates made by the neural network, from which respective MRI images associated with multiple MRI slices may be obtained by applying a Fourier transform to the k-space data.
-
公开(公告)号：US11521323B2
公开(公告)日：2022-12-06
申请号：US17076641
申请日：2020-10-21
Inventor： Yimo Guo , Xiao Chen , Shanhui Sun , Terrence Chen
Abstract: A bullseyes plot may be generated based on cardiac magnetic resonance imaging (CMRI) to facilitate the diagnosis and treatment of heart diseases. Described herein are systems, methods, and instrumentalities associated with bullseyes plot generation. A plurality of myocardial segments may be obtained for constructing the bullseye plot based on landmark points detected in short-axis and long-axis magnetic resonance (MR) slices of the heart and by arranging the short-axis MR slices sequentially in accordance with the order in which the slices are generated during the CMRI. The sequential order of the short-axis MR slices may be determined utilizing projected locations of the short-axis MR slices on a long-axis MR slice and respective distances of the projected locations to a landmark point of the long-axis MR slice. The myocardium and/or landmark points may be identified in the short-axis and/or long-axis MR slices using artificial neural networks.
-
Patent Agency Ranking
公开(公告)号：US20210272297A1
公开(公告)日：2021-09-02
申请号：US17154450
申请日：2021-01-21
Inventor： Xiao Chen , Shanhui Sun , Terrence Chen
Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with cardiac assessment. An apparatus as described herein may obtain electrocardiographic imaging (ECGI) information associated with a human heart and magnetic resonance imaging (MRI) information associated with the human heart, and integrate the ECGI and MRI information using a machine-learned model. Using the integrated ECGI and MRI information, the apparatus may predict target ablation sites, estimate electrophysiology (EP) measurements, and/or simulate the electrical system of the human heart.
-
公开(公告)号：US12229954B2
公开(公告)日：2025-02-18
申请号：US17953484
申请日：2022-09-27
Inventor： Arun Innanje , Xiao Chen , Shanhui Sun , Zhanhong Wei , Terrence Chen
IPC: G06T7/00
Abstract: An anatomy-aware contouring editing method includes receiving an image, wherein the image represents an anatomically recognizable structure; identifying a first image segment representing part of the anatomically recognizable structure; annotating the first image segment to generate a label of the part; drawing a contour along a boundary of the part; receiving a first input from a user device indicative of a region of contour failure, wherein the region of contour failure includes a portion of a contour that requires editing; editing the contour for generating an edited contour based on the first input and anatomical information; and updating another contour of another part of the anatomically recognizable structure based on the edited contour, wherein the another part is anatomically related to the part.
-
公开(公告)号：US12056853B2
公开(公告)日：2024-08-06
申请号：US17565714
申请日：2021-12-30
Inventor： Shanhui Sun , Li Chen , Yikang Liu , Xiao Chen , Zhang Chen
CPC classification number: G06T5/70 , G06T5/10 , G06T5/50 , G06T2207/10121 , G06T2207/20048 , G06T2207/20084 , G06T2207/30052
Abstract: An apparatus for stent visualization includes a hardware processor that is configured to input one or more stent images from a sequence of X-ray images and corresponding balloon marker location data to a cascaded spatial transform network. The background is separated from the one or more stent images using the cascaded spatial transform network and a transformed stent image with a clear background and a non-stent background image is generated. The stent layer and non-stent layer are generated using a neural network without online optimization. A mapping function f maps the inputs, the sequence images and marker coordinates, into the two single image outputs.
-
29.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.
-
公开(公告)号：US20240127929A1
公开(公告)日：2024-04-18
申请号：US17966948
申请日：2022-10-17
Inventor： Arun Innanje , Abhishek Sharma , Xiao Chen , Zhanhong Wei , Terrence Chen
IPC: G16H30/40 , G06F3/0482 , G06F3/0484 , G06F40/169 , G06V10/776 , G06V20/70
CPC classification number: G16H30/40 , G06F3/0482 , G06F3/0484 , G06F40/169 , G06V10/776 , G06V20/70 , G06V2201/03
Abstract: Disclosed is a method and a system for reviewing annotated medical images. The method includes receiving a dataset of medical images comprising one or more pre-existing annotations therein. The method also includes displaying, via a first graphical user interface, at a given instance, one of the medical images, and detecting a first input comprising a modification of at least one pre-existing annotation in the one of the medical images being displayed to define at least one modified annotation therefor and a reference for the at least one modified annotation to be associated therewith. The method also includes displaying, via a second graphical user interface, the one of the medical images having the at least one modified annotation and the associated reference for the at least one modified annotation, and detecting a second input comprising one of verification, correction, or rejection of the at least one modified annotation.
-
-
-
-
-
-
-
-
-
Search Results
75
records
(took 0.025 seconds)
