-
公开(公告)号:US20250104270A1
公开(公告)日:2025-03-27
申请号:US18475406
申请日:2023-09-27
Applicant: GE Precision Healthcare LLC
Inventor: Deepa Anand , Dattesh Dayanand Shanbhag , Chitresh Bhushan , Dawei Gui , Kavitha Manickam , Maggie MeiKei Fung , Gurunath Reddy Madhumani
IPC: G06T7/73 , G06T7/00 , G06V10/25 , G06V10/26 , G06V10/44 , G06V10/762 , G06V10/774 , G06V20/70
Abstract: A method for performing one-shot anatomy localization includes obtaining a medical image of a subject. The method includes receiving a selection of both a template image and a region of interest within the template image, wherein the template image includes one or more anatomical landmarks assigned a respective anatomical label. The method includes inputting both the medical image and the template image into a trained vision transformer model. The method includes outputting from the trained vision transformer model both patch level features and image level features for both the medical image and the template image. The method still further includes interpolating pixel level features from the patch level features for both the medical image and the template image. The method includes utilizing the pixel level features within the region of interest of the template image to locate and label corresponding pixel level features in the medical image.
-
公开(公告)号:US12078697B1
公开(公告)日:2024-09-03
申请号:US18111147
申请日:2023-02-17
Applicant: GE Precision Healthcare LLC
Inventor: Kavitha Manickam , Dattesh Dayanand Shanbhag , Dawei Gui , Chitresh Bhushan
CPC classification number: G01R33/288 , G01R33/546
Abstract: A computer-implemented method for performing a scan of a subject utilizing a magnetic resonance imaging (MRI) system includes initiating, via a processor, a prescan of the subject by an MRI scanner of the MRI system without a priori knowledge as to whether the subject has a metal implant. The computer-implemented method also includes executing, via the processor, a metal detection algorithm during a prescan entry point of the prescan to detect whether the metal implant is present in the subject. The computer-implemented method further includes determining, via the processor, to proceed with a calibration scan and the scan utilizing predetermined scan parameters when no metal implant is detected in the subject. The computer-implemented method even further includes switching, via the processor, into a metal implant scan mode when one or more metal implants are detected in the subject.
-
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.
-
4.
公开(公告)号:US20230341914A1
公开(公告)日:2023-10-26
申请号:US18343258
申请日:2023-06-28
Applicant: GE Precision Healthcare LLC
Inventor: Chitresh Bhushan , Dattesh Dayanand Shanbhag , Rakesh Mullick
CPC classification number: G06F1/266 , H03F3/45475 , G05F1/46 , G06F13/1668 , G06F13/4282 , H03K5/1252 , G06F2213/0026
Abstract: Techniques are described for generating reformatted views of a three-dimensional (3D) anatomy scan using deep-learning estimated scan prescription masks. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a mask generation component that employs a pre-trained neural network model to generate masks for different anatomical landmarks depicted in one or more calibration images captured of an anatomical region of a patient. The computer executable components further comprise a reformatting component that reformats 3D image data captured of the anatomical region of the patient using the masks to generate different representations of the 3D image data that correspond to the different anatomical landmarks.
-
公开(公告)号:US20230094940A1
公开(公告)日:2023-03-30
申请号:US17486796
申请日:2021-09-27
Applicant: GE Precision Healthcare LLC
Inventor: Radhika Madhavan , Soumya Ghose , Dattesh Dayanand Shanbhag , Andre De Almeida Maximo , Chitresh Bhushan , Desmond Teck Beng Yeo , Thomas Kwok-Fah Foo
Abstract: A deep learning-based continuous federated learning network system is provided. The system includes a global site comprising a global model and a plurality of local sites having a respective local model derived from the global model. The plurality of model tuning modules having a processing system are provided at the plurality of local sites for tuning the respective local model. The processing system is programmed to receive incremental data and select one or more layers of the local model for tuning based on the incremental data. Finally, the selected layers are tuned to generate a retrained model.
-
公开(公告)号:US20220114389A1
公开(公告)日:2022-04-14
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
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.
-
公开(公告)号:US20210080531A1
公开(公告)日:2021-03-18
申请号:US16573955
申请日:2019-09-17
Applicant: GE Precision Healthcare LLC
Inventor: Dawei Gui , Dattesh Dayanand Shanbhag , Chitresh Bhushan , André de Almeida Maximo
Abstract: Methods and systems are provided for determining scan settings for a localizer scan based on a magnetic resonance (MR) calibration image. In one example, a method for magnetic resonance imaging (MRI) includes acquiring an MR calibration image of an imaging subject, mapping, by a trained deep neural network, the MR calibration image to a corresponding anatomical region of interest (ROI) attribute map for an anatomical ROI of the imaging subject, adjusting one or more localizer scan parameters based on the anatomical ROI attribute map, and acquiring one or more localizer images of the anatomical ROI according to the one or more localizer scan parameters.
-
公开(公告)号:US12106478B2
公开(公告)日:2024-10-01
申请号:US17203196
申请日:2021-03-16
Applicant: GE Precision Healthcare LLC
Inventor: Florintina C. , Deepa Anand , Dattesh Dayanand Shanbhag , Chitresh Bhushan , Radhika Madhavan
CPC classification number: G06T7/0014 , G06N20/00 , G06T3/147 , G06T7/11 , G06T2207/20081 , G06T2207/20084 , G06T2207/30008
Abstract: A medical imaging system includes at least one medical imaging device providing image data of a subject and a processing system programmed to generate a plurality of training images having simulated medical conditions by blending a pathology region from a plurality of template source images to a plurality of target images. The processing system is further programmed to train a deep learning network model using the plurality of training images and input the image data of the subject to the deep learning network model. The processing system is further programmed to generate a medical image of the subject based on the output of the deep learning network model.
-
公开(公告)号:US20240005480A1
公开(公告)日:2024-01-04
申请号:US17810473
申请日:2022-07-01
Applicant: GE Precision Healthcare LLC
Inventor: Chitresh Bhushan , Dattesh Dayanand Shanbhag , Soumya Ghose , Amod Suhas Jog
CPC classification number: G06T7/0012 , G16H30/40 , G06T2207/20084 , G06T2207/20081
Abstract: Methods and systems are provided for automatic placement of at least one saturation band on a medical image, which may direct saturation pulses during a MRI scan. A method may include acquiring a localizer image of an imaging subject, determining a plane mask for the localizer image by entering the localizer image as input to a deep neural network trained to output the plane mask based on the localizer image, generating a saturation band based on the plane mask by positioning the saturation band at a position and an angulation of the plane mask, and outputting a graphical prescription for display on a display device, the graphical prescription including the saturation band overlaid on the medical image.
-
10.
公开(公告)号:US20230162487A1
公开(公告)日:2023-05-25
申请号:US18099530
申请日:2023-01-20
Applicant: GE PRECISION HEALTHCARE LLC
Inventor: Soumya Ghose , Desmond Teck Beng Yeo , Thomas Kwok-Fah Foo , Chitresh Bhushan , Deepa Anand , Dattesh Dayanand Shanbhag , Radhika Madhavan
IPC: G06V10/774 , G06N3/098 , G06V10/762
CPC classification number: G06V10/774 , G06N3/098 , G06V10/763
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.
-
-
-
-
-
-
-
-
-