Patents by Inventor Zhoubing XU
Zhoubing XU has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 12333729Abstract: Systems and methods for automatically staging non-small cell lung cancer are provided. Patient data relating to a cancer of a patient is received. The patient data comprises one or more medical images and one or more biopsy reports. A T-stage of the cancer is determined based on a location and a size of one or more tumors of the cancer determined using the patient data. An N-stage of the cancer is determined by combining a metastasis evaluation of the cancer to regional lymph nodes determined from the one or more medical images and a metastasis evaluation of the cancer in the regional lymph nodes determined from the one or more biopsy reports. An M-stage of the cancer is determined based on a metastasis evaluation of the cancer to anatomical structures based on the patient data. The T-stage, the N-stage, and the M-stage are output.Type: GrantFiled: August 11, 2022Date of Patent: June 17, 2025Assignee: Siemens Medical Solutions USA, Inc.Inventors: Julian Rosenman, Zhoubing Xu, Ali Kamen, Fernando Vega, Nicolo Capobianco, Bruce Spottiswoode, Sasa Grbic
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Patent number: 12236604Abstract: A computer-implemented method is for providing quantitative airway information. In an embodiment, the method includes receiving and/or determining first medical image data of an airway segment; applying a first trained function to the first medical image data, to generate output data; determining the at least one quantitative airway information of the airway segment based on the output data; and providing the at least one quantitative airway information.Type: GrantFiled: February 17, 2021Date of Patent: February 25, 2025Assignee: SIEMENS HEALTHINEERS AGInventors: Dominik Neumann, Puyang Wang, Anna Boehm, Sasa Grbic, Zhoubing Xu, Siqi Liu
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Patent number: 12211204Abstract: Systems and methods for performing an assessment of a lesion are provided. A plurality of input medical images of a lesion is received. The plurality of input medical images comprises an initial input medical image and one or more additional input medical images. The initial input medical image comprises a region of interest around the lesion. A mask of the lesion is curated for the initial input medical image based on the region of interest and a set of candidate masks. The region of interest in the initial input medical image is propagated to the one or more additional input medical images based on prior registration transformations. A mask of the lesion is curated for each of the one or more additional input medical images based on the propagated regions of interest and the set of candidate masks. One or more assessments of the lesion are performed based on the mask for the initial input medical image, the masks for the one or more additional input medical images, and prior assessments of lesions.Type: GrantFiled: January 26, 2022Date of Patent: January 28, 2025Assignee: Siemens Healthineers AGInventors: Zhoubing Xu, Guillaume Chabin, Matteo Barbieri, Alin Madalin Draghia, Manasi Datar, Thomas Pheiffer, Ioan Marius Popdan, Robert Grimm, Heinrich von Busch, Sasa Grbic
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Publication number: 20240428401Abstract: Systems and methods for performing an assessment of one or more tumors are provided. A plurality of input medical images of a patient acquired at a plurality of points in time is received. One or more tumors are identified in each of the plurality of input medical images. A tumor burden of the patient is determined for each of the plurality of points in time based on the one or more identified tumors using one or more machine learning based networks. An assessment of the one or more tumors is performed based on the tumor burden of the patient determined for each of the plurality of points in time. Results of the assessment of the one or more tumors are output.Type: ApplicationFiled: June 22, 2023Publication date: December 26, 2024Inventors: Bin Lou, Julian Rosenman, Patrick Kupelian, Zhoubing Xu, Sasa Grbic, Ali Kamen, Dorin Comaniciu
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Publication number: 20240379244Abstract: Systems and methods for performing an analysis on a patient population are provided. A biomarker bank storing lesion-related features extracted from medical images of a patient population is maintained. An analysis is performed on the patient population based on the lesion-related features stored in the biomarker bank. Results of the analysis.Type: ApplicationFiled: May 12, 2023Publication date: November 14, 2024Inventors: Zhoubing Xu, Guillaume Chabin, Alin Madalin Draghia, Sasa Grbic
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Patent number: 12125208Abstract: The invention describes a method for automatically localizing organ segments in a three-dimensional image comprising the following steps: providing a three-dimensional image showing at least one organ and at least one tubular network comprising a plurality of tubular structures, the organ comprising organ segments; performing automatic separation of the organ from other parts of the image; performing automatic tracing of the tubular network to obtain a branch map; performing automatic analysis of the branch map to identify specific tubular structures; performing automatically assigning regions of the organ to the specific tubular structures to segment the organ into localized organ segments; and outputting the localized organ segments and the traced and analyzed tubular network as image data. The invention further describes a localization arrangement and a medical imaging system.Type: GrantFiled: September 2, 2021Date of Patent: October 22, 2024Assignee: Siemens Healthineers AGInventors: Zhoubing Xu, Sasa Grbic, Dominik Neumann, Guillaume Chabin, Bruce Spottiswoode, Fei Gao, Günther Platsch
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Publication number: 20240339199Abstract: A computer-implemented method for pretraining a downstream neural network for a novel image-to-image task to be performed on medical imaging data received from a medical scanner is provided. A database of augmented training data sets is generated based on a database of pre-existing training data sets. A set of at least two pretext neural network subsystems are jointly trained for performing (in particular partly self-supervised and partly weakly supervised) pretext tasks using the generated database. The downstream neural network is pretrained for the novel image-to-image task to be performed on medical imaging data received from a medical scanner. The pretraining is based on a subset of the modified weights of the pretext neural network subsystems, and/or on an output of a subset of layers of the set of pretext neural network subsystems.Type: ApplicationFiled: March 28, 2024Publication date: October 10, 2024Inventors: Dominik Neumann, Alexandru Constantin Serban, Zhoubing Xu, Bogdan Georgescu, Florin-Cristian Ghesu
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Publication number: 20240296936Abstract: Systems and methods for performing a medical imaging analysis task are provided. One or more input medical images are received. A medical imaging analysis task is performed based on the one or more input medical images using a machine learning based model. Results of the medical imaging analysis task are output.Type: ApplicationFiled: February 28, 2024Publication date: September 5, 2024Inventors: Han Liu, Zhoubing Xu, Sasa Grbic
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Publication number: 20240054650Abstract: Systems and methods for automatically staging non-small cell lung cancer are provided. Patient data relating to a cancer of a patient is received. The patient data comprises one or more medical images and one or more biopsy reports. A T-stage of the cancer is determined based on a location and a size of one or more tumors of the cancer determined using the patient data. An N-stage of the cancer is determined by combining a metastasis evaluation of the cancer to regional lymph nodes determined from the one or more medical images and a metastasis evaluation of the cancer in the regional lymph nodes determined from the one or more biopsy reports. An M-stage of the cancer is determined based on a metastasis evaluation of the cancer to anatomical structures based on the patient data. The T-stage, the N-stage, and the M-stage are output.Type: ApplicationFiled: August 11, 2022Publication date: February 15, 2024Inventors: Julian Rosenman, Zhoubing Xu, Ali Kamen, Fernando Vega, Nicolo Capobianco, Bruce Spottiswoode, Sasa Grbic
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Publication number: 20240046466Abstract: Techniques for determining at least one characteristic of adipose tissue included in an anatomical structure are provided. The at least one characteristic of the adipose tissue is determined based on one or more segmented CT images using a trained neural network. For example, the at least one characteristic of the adipose tissue may be determined by inputting the one or more segmented CT images into the trained neural network. The one or more segmented CT images are obtained by segmenting each one of one or more CT images depicting the anatomical structure including the adipose tissue to determine a contour of the adipose tissue.Type: ApplicationFiled: July 31, 2023Publication date: February 8, 2024Applicant: Siemens Healthcare GmbHInventors: Michael SUEHLING, Felix LADES, Sasa GRBIC, Bernhard GEIGER, Zhoubing XU
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Patent number: 11810291Abstract: Systems and methods for generating a synthesized medical image are provided. An input medical image is received. A synthesized segmentation mask is generated. The input medical image is masked based on the synthesized segmentation mask. The masked input medical image has an unmasked portion and a masked portion. An initial synthesized medical image is generated using a trained machine learning based generator network. The initial synthesized medical image includes a synthesized version of the unmasked portion of the masked input medical image and synthesized patterns in the masked portion of the masked input medical image. The synthesized patterns is fused with the input medical image to generate a final synthesized medical image.Type: GrantFiled: May 1, 2020Date of Patent: November 7, 2023Assignee: Siemens Healthcare GmbHInventors: Siqi Liu, Bogdan Georgescu, Zhoubing Xu, Youngjin Yoo, Guillaume Chabin, Shikha Chaganti, Sasa Grbic, Sebastien Piat, Brian Teixeira, Thomas Re, Dorin Comaniciu
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Representation Learning for Organs at Risk and Gross Tumor Volumes for Treatment Response Prediction
Publication number: 20230342933Abstract: For prediction of response of radiation therapy, radiomics are used for unsupervised machine training of an encoder-decoder network to predict based on input of image data, such as computed tomography image data and from segmentation. The trained encoder is then used to generate latent representations to be used as input to different classifiers or regressors for prediction of therapy responses, such as one classifier to predict response for an organ at risk and another classifier to predict another type of response for the organ at risk or to predict a response for the tumor.Type: ApplicationFiled: April 22, 2022Publication date: October 26, 2023Inventors: Bin Lou, Zhoubing Xu, Ali Kamen, Sasa Grbic, Dorin Comaniciu -
Patent number: 11763454Abstract: Embodiments of the invention relate to a method of processing a medical image to remove one or more portions of the image corresponding to bone structures, the method comprising: receiving first image data representing a first, three-dimensional, medical image; processing the first image data to generate second image data representing a plurality of two-dimensional image channels each corresponding to a different slice of the first medical image; receiving the second image data at a neural network system; applying an attention mechanism at the neural network system to the second image data to generate an attention map representing one or more regions of interest; and determining, at least partly on the basis of the attention map, that one or more portions of the second image data represent a bone structure.Type: GrantFiled: March 11, 2020Date of Patent: September 19, 2023Assignee: Siemens Healthcare GmbHInventors: Puyang Wang, Zhoubing Xu, Sasa Grbic
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Patent number: 11717233Abstract: Systems and methods for assessing a disease are provided. An input medical image in a first modality is received. Lungs are segmented from the input medical image using a trained lung segmentation network and abnormality patterns associated with the disease are segmented from the input medical image using a trained abnormality pattern segmentation network. The trained lung segmentation network and the trained abnormality pattern segmentation network are trained based on 1) synthesized images in the first modality generated from training images in a second modality and 2) target segmentation masks for the synthesized images generated from training segmentation masks for the training images. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality patterns.Type: GrantFiled: July 21, 2020Date of Patent: August 8, 2023Assignee: Siemens Healthcare GmbHInventors: Florin-Cristian Ghesu, Siqi Liu, Awais Mansoor, Sasa Grbic, Sebastian Vogt, Dorin Comaniciu, Ruhan Sa, Zhoubing Xu
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Publication number: 20230237647Abstract: Systems and methods for performing an assessment of a lesion are provided. A plurality of input medical images of a lesion is received. The plurality of input medical images comprises an initial input medical image and one or more additional input medical images. The initial input medical image comprises a region of interest around the lesion. A mask of the lesion is curated for the initial input medical image based on the region of interest and a set of candidate masks. The region of interest in the initial input medical image is propagated to the one or more additional input medical images based on prior registration transformations. A mask of the lesion is curated for each of the one or more additional input medical images based on the propagated regions of interest and the set of candidate masks. One or more assessments of the lesion are performed based on the mask for the initial input medical image, the masks for the one or more additional input medical images, and prior assessments of lesions.Type: ApplicationFiled: January 26, 2022Publication date: July 27, 2023Inventors: Zhoubing Xu, Guillaume Chabin, Matteo Barbieri, Alin Madalin Draghia, Manasi Datar, Thomas Pheiffer, Ioan Marius Popdan, Robert Grimm, Heinrich von Busch, Sasa Grbic
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Publication number: 20230157761Abstract: Systems and methods for automatically navigating a catheter in a patient are provided. An image of a current view of a catheter in a patient is received. A set of actions of a robotic navigation system for navigating the catheter from the current view towards a target view is determined using a machine learning based network. The catheter is automatically navigated in the patient from the current view towards the target view using the robotic navigation system based on the set of actions.Type: ApplicationFiled: November 24, 2021Publication date: May 25, 2023Inventors: Rui Liao, Young-Ho Kim, Jarrod Collins, Abdoul Aziz Amadou, Sebastien Piat, Ankur Kapoor, Tommaso Mansi, Noha El-Zehiry, Sasa Grbic, Dorin Comaniciu, Xianjun S. Zheng, Bo Liu, Zhoubing Xu, Jin-hyeong Park
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Patent number: 11430121Abstract: Systems and methods for assessing a disease are provided. Medical imaging data of lungs of a patient is received. The lungs are segmented from the medical imaging data and abnormality regions associated with a disease are segmented from the medical imaging data. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality regions. The disease may be COVID-19 (coronavirus disease 2019) or diseases, such as, e.g., SARS (severe acute respiratory syndrome), MERS (Middle East respiratory syndrome), or other types of viral and non-viral pneumonia.Type: GrantFiled: April 1, 2020Date of Patent: August 30, 2022Assignee: Siemens Healthcare GmbHInventors: Shikha Chaganti, Sasa Grbic, Bogdan Georgescu, Zhoubing Xu, Siqi Liu, Youngjin Yoo, Thomas Re, Guillaume Chabin, Thomas Flohr, Valentin Ziebandt, Dorin Comaniciu, Brian Teixeira, Sebastien Piat
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Patent number: 11393229Abstract: Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.Type: GrantFiled: November 24, 2020Date of Patent: July 19, 2022Assignee: Siemens Healthcare GmbHInventors: Shaohua Kevin Zhou, Mingqing Chen, Hui Ding, Bogdan Georgescu, Mehmet Akif Gulsun, Tae Soo Kim, Atilla Peter Kiraly, Xiaoguang Lu, Jin-hyeong Park, Puneet Sharma, Shanhui Sun, Daguang Xu, Zhoubing Xu, Yefeng Zheng
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Patent number: 11328412Abstract: Systems and methods are provided for performing medical imaging analysis. Input medical imaging data is received for performing a particular one of a plurality of medical imaging analyses. An output that provides a result of the particular medical imaging analysis on the input medical imaging data is generated using a neural network trained to perform the plurality of medical imaging analyses. The neural network is trained by learning one or more weights associated with the particular medical imaging analysis using one or more weights associated with a different one of the plurality of medical imaging analyses. The generated output is outputted for performing the particular medical imaging analysis.Type: GrantFiled: January 9, 2018Date of Patent: May 10, 2022Assignee: Siemens Healthcare GmbHInventors: Shaohua Kevin Zhou, Mingqing Chen, Daguang Xu, Zhoubing Xu, Shun Miao, Dong Yang, He Zhang
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Publication number: 20220092786Abstract: The invention describes a method for automatically localizing organ segments in a three-dimensional image comprising the following steps: providing a three-dimensional image showing at least one organ and at least one tubular network comprising a plurality of tubular structures, the organ comprising organ segments; performing automatic separation of the organ from other parts of the image; performing automatic tracing of the tubular network to obtain a branch map; performing automatic analysis of the branch map to identify specific tubular structures; performing automatically assigning regions of the organ to the specific tubular structures to segment the organ into localized organ segments; and outputting the localized organ segments and the traced and analyzed tubular network as image data. The invention further describes a localization arrangement and a medical imaging system.Type: ApplicationFiled: September 2, 2021Publication date: March 24, 2022Inventors: Zhoubing Xu, Sasa Grbic, Dominik Neumann, Guillaume Chabin, Bruce Spottiswoode, Fei Gao, Günther Platsch