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公开(公告)号：US12125217B2
公开(公告)日：2024-10-22
申请号：US17520254
申请日：2021-11-05
Applicant: GE PRECISION HEALTHCARE LLC
Inventor： Prem Venugopal , Cynthia Elizabeth Landberg Davis , Jed Douglas Pack , Jhimli Mitra , Soumya Ghose
CPC classification number: G06T7/248 , G06T7/10 , G16H30/20 , G06T2207/30104
Abstract: A computer-implemented method includes obtaining, via a processor, segmented image patches of a vessel along a coronary tree path and associated coronary flow distribution for respective vessel segments in the segmented image patches. The method also includes determining, via the processor, a pressure drop distribution along an axial length of the vessel from the segmented image patches and the associated coronary flow distribution. The method further includes determining, via the processor, critical points in the pressure drop distribution. The method even further includes detecting, via the processor, a presence of a stenosis based on the critical points in the pressure drop distribution.
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公开(公告)号：US20240029415A1
公开(公告)日：2024-01-25
申请号：US17814746
申请日：2022-07-25
Applicant: GE Precision Healthcare LLC
Inventor： Dattesh Dayanand Shanbhag , Chitresh Bhushan , Soumya Ghose , Deepa Anand
CPC classification number: G06V10/7747 , G06V10/7715 , G06T19/20 , G06T15/08 , G06T7/0014 , G16H30/40 , G16H50/50 , G06T2207/20081 , G06T2207/30096 , G06T2207/30012 , G06V2201/033 , G06T2219/2021 , G06T2210/41
Abstract: Systems and methods are provided for an image processing system. In an example, a method includes acquiring a pathology dataset, acquiring a reference dataset, generating a deformation field by mapping points of a reference case of the reference dataset to points of a patient image of the pathology dataset, manipulating the deformation field, applying the deformation field to the reference case to generate a simulated pathology image including a simulated deformation pathology, and outputting the simulated pathology image.
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公开(公告)号：US20250095642A1
公开(公告)日：2025-03-20
申请号：US18468593
申请日：2023-09-15
Applicant: GE Precision Healthcare LLC
Inventor： Soumya Ghose , Sanand Sasidharan , Sanghee Cho , Akshit Achara , Rakesh Mullick , Anuradha Kanamarlapudi , Fiona Ginty , Annamraju Ravi Bhardwaj , Sundararajan Mani , Brion Sarachan
IPC: G10L15/197 , G06N3/0455 , G06N3/09 , G10L15/06 , G10L15/16 , G10L15/22 , G16H10/60 , G16H50/20 , G16H70/20
Abstract: The current disclosure provides methods for an automated clinical recommendation system that generates clinical recommendations for patients of a health care system based on natural language queries submitted by care providers of the health care system. The natural language queries may be typed into a user interface (UI) of the clinical recommendation system, or submitted by voice via a microphone. The clinical recommendation system provides a clinically explainable disease state of a patient based on patient data included in a query, and recommends a next course of action (e.g., a treatment) based on clinical guidelines and population statistics, in a manner that reduces a current burden of clinicians in consulting digital clinical manuals via a series of time-consuming and cumbersome interactions with a graphical user interface (GUI) of the digital clinical manuals.
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公开(公告)号：US20240260919A1
公开(公告)日：2024-08-08
申请号：US18636614
申请日：2024-04-16
Applicant: GE Precision Healthcare LLC
Inventor： Prem Venugopal , Cynthia Elizabeth Landberg Davis , Jed Douglas Pack , Jhimli Mitra , Soumya Ghose , Peter Michael Edic
CPC classification number: A61B6/504 , A61B6/032 , A61B6/037 , A61B6/507 , A61B6/5217 , A61B6/5241 , G06N3/08 , G06T7/11
Abstract: A computer-implemented method includes obtaining, via a processor, clinical images including vessels and generating, via the processor, straightened-out images for each coronary tree path within respective clinical images, The method also includes extracting, via the processor, segmented 3D image patches, determining, via the processor, overlapping binary mask volumes for each segment, and predicting, via the processor, pressure drops across the segmented image patches using a trained deep neural network.
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公开(公告)号：US12039007B2
公开(公告)日：2024-07-16
申请号：US17067179
申请日：2020-10-09
Applicant: GE Precision Healthcare LLC
Inventor： Soumya Ghose , Dattesh Dayanand Shanbhag , Chitresh Bhushan , Andre De Almeida Maximo , Radhika Madhavan , Desmond Teck Beng Yeo , Thomas Kwok-Fah Foo
IPC: G06F18/214 , G06F18/211 , G06F18/22 , G06F18/232 , G06N3/08 , G16H30/40
CPC classification number: G06F18/2148 , G06F18/211 , G06F18/2155 , G06F18/22 , G06F18/232 , G06N3/08 , G16H30/40 , G06V2201/03
Abstract: A computer-implemented method of automatically labeling medical images is provided. The method includes clustering training images and training labels into clusters, each cluster including a representative template having a representative image and a representative label. The method also includes training a neural network model with a training dataset that includes the training images and the training labels, and target outputs of the neural network model are labels of the medical images. The method further includes generating a suboptimal label corresponding to an unlabeled test image using the trained neural network model, and generating an optimal label corresponding to the unlabeled test image using the suboptimal label and representative templates. In addition, the method includes updating the training dataset using the test image and the optimal label, retraining the neural network model, generating a label of an unlabeled image using the retrained neural network model, and outputting the generated label.
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Patent Agency Ranking
公开(公告)号：US11948677B2
公开(公告)日：2024-04-02
申请号：US17342280
申请日：2021-06-08
Applicant: GE Precision Healthcare LLC
Inventor： Soumya Ghose , Jhimli Mitra , Peter M Edic , Prem Venugopal , Jed Douglas Pack
CPC classification number: G16H30/40 , G06N3/045 , G06N3/088 , G06T7/10 , G06T2207/10081 , G06T2207/20081 , G06T2207/20084 , G06T2207/30101
Abstract: Systems and techniques that facilitate hybrid unsupervised and supervised image segmentation are provided. In various embodiments, a system can access a computed tomography (CT) image depicting an anatomical structure. In various aspects, the system can generate, via an unsupervised modeling technique, at least one class probability mask of the anatomical structure based on the CT image. In various instances, the system can generate, via a deep-learning model, an image segmentation based on the CT image and based on the at least one class probability mask.
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公开(公告)号：US20230004872A1
公开(公告)日：2023-01-05
申请号：US17365650
申请日：2021-07-01
Applicant: GE PRECISION HEALTHCARE LLC
Inventor： Soumya Ghose , Radhika Madhavan , Chitresh Bhushan , Dattesh Dayanand Shanbhag , Deepa Anand , Desmond Teck Beng Yeo , Thomas Kwok-Fah Foo
Abstract: A computer implemented method is provided. The method includes establishing, via multiple processors, a continuous federated learning framework including a global model at a global site and respective local models derived from the global model at respective local sites. The method also includes retraining or retuning, via the multiple processors, the global model and the respective local models without sharing actual datasets between the global site and the respective local sites but instead sharing synthetic datasets generated from the actual datasets.
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公开(公告)号：US20230307137A1
公开(公告)日：2023-09-28
申请号：US17704531
申请日：2022-03-25
Inventor： Soumya Ghose , Fiona Ginty , Cynthia Elizabeth Landberg Davis , Sanghee Cho , Sunil S. Badve , Yesim Gokmen-Polar
CPC classification number: G16H50/30 , G16H30/40 , G06T7/0012 , G06V10/26 , G06T2207/30096
Abstract: A method for determining a recurrence of a disease in a patient includes generating a medical image of an organ of the patient and then extracting an invasive edge around an area of interest in the medical image. A plurality of radiomics features is obtained from the invasive edge and the recurrence of the disease is determined based on the plurality of radiomics features.
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公开(公告)号：US20230252614A1
公开(公告)日：2023-08-10
申请号：US18304947
申请日：2023-04-21
Applicant: GE Precision Healthcare LLC
Inventor： Tao Tan , Pál Tegzes , Levente Imre Török , Lehel Ferenczi , Gopal B. Avinash , László Ruskó , Gireesha Chinthamani Rao , Khaled Younis , Soumya Ghose
IPC: G06T5/50 , G06V10/772 , G06V10/774 , G06V10/762 , G06V10/74 , G06V10/776 , G06T7/00 , G06V10/82 , G06F18/21 , G06F18/22 , G06F18/23 , G06F18/28 , G06F18/214
CPC classification number: G06T5/50 , G06V10/772 , G06V10/774 , G06V10/762 , G06V10/761 , G06V10/776 , G06T7/00 , G06V10/82 , G06F18/217 , G06F18/22 , G06F18/23 , G06F18/28 , G06F18/214
Abstract: Techniques are described for optimizing deep learning model performance using image harmonization as a pre-processing step. According to an embodiment, a method comprises decomposing, by a system operatively coupled to a processor, an input image into sub-images. The method further comprises harmonizing the sub-images with corresponding reference sub-images of at least one reference image based on two or more different statistical values respectively calculated for the sub-images and the corresponding reference-sub images, resulting in transformation of the sub-images into modified sub-images images. In some implementations, the modified sub-images can be combined into a harmonized image having a more similar appearance to the at least one reference image relative to the input image. In other implementations, harmonized images and/or modified sub-images generated using these techniques can be used as ground-truth training samples for training one or more deep learning model to transform input images with appearance variations into harmonized images.
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公开(公告)号：US20230144624A1
公开(公告)日：2023-05-11
申请号：US17520254
申请日：2021-11-05
Applicant: GE PRECISION HEALTHCARE LLC
Inventor： Prem Venugopal , Cynthia Elizabeth Landberg Davis , Jed Douglas Pack , Jhimli Mitra , Soumya Ghose
CPC classification number: G06T7/248 , G06T7/10 , G16H30/20 , G06T2207/30104
Abstract: A computer-implemented method includes obtaining, via a processor, segmented image patches of a vessel along a coronary tree path and associated coronary flow distribution for respective vessel segments in the segmented image patches. The method also includes determining, via the processor, a pressure drop distribution along an axial length of the vessel from the segmented image patches and the associated coronary flow distribution. The method further includes determining, via the processor, critical points in the pressure drop distribution. The method even further includes detecting, via the processor, a presence of a stenosis based on the critical points in the pressure drop distribution.
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