公开(公告)号：US20230342427A1

公开(公告)日：2023-10-26

申请号：US18343266

申请日：2023-06-28

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Gopal B. Avinash ,  Máté Fejes ,  Ravi Soni ,  Dániel Attila Szabó ,  Rakesh Mullick ,  Vikram Melapudi ,  Krishna Seetharam Shriram ,  Sohan Rashmi Ranjan ,  Bipul Das ,  Utkarsh Agrawal ,  László Ruskó ,  Zita Herczeg ,  Barbara Darázs

IPC: G06F18/214 ,  G06N5/04 ,  G16H30/40 ,  A61B5/055 ,  G06T5/50 ,  G06F18/21 ,  G06T7/30 ,  A61B5/00 ,  G16H30/20 ,  G16H50/20 ,  G16H50/50 ,  A61B6/03 ,  G06F18/22 ,  G06F18/28 ,  A61B6/00

CPC classification number: G06F18/214 ,  A61B5/055 ,  A61B5/7267 ,  A61B6/032 ,  A61B6/5223 ,  G06F18/2178 ,  G06F18/22 ,  G06F18/28 ,  G06N5/04 ,  G06T5/50 ,  G06T7/30 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/10116 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20212 ,  G06T2207/30004 ,  G06V2201/03

Abstract: Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.

公开(公告)号：US20230298136A1

公开(公告)日：2023-09-21

申请号：US17654864

申请日：2022-03-15

Applicant: GE Precision Healthcare LLC

Inventor： Bipul Das ,  Rakesh Mullick ,  Deepa Anand ,  Sandeep Dutta ,  Uday Damodar Patil ,  Maud Bonnard

IPC: G06T3/60 ,  G06T7/73 ,  G06V10/82 ,  G06V10/774 ,  G16H50/20

CPC classification number: G06T3/60 ,  G06T7/73 ,  G06V10/82 ,  G06V10/774 ,  G16H50/20 ,  G06T2200/04 ,  G06V2201/031 ,  G06T2207/20084 ,  G06T2207/20081

Abstract: Systems/techniques that facilitate deep learning multi-planar reformatting of medical images are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can localize, via execution of a machine learning model, a set of landmarks depicted in the three-dimensional medical image, a set of principal anatomical planes depicted in the three-dimensional medical image, and a set of organs depicted in the three-dimensional medical image. In various instances, the system can determine an anatomical orientation exhibited by the three-dimensional medical image, based on the set of landmarks, the set of principal anatomical planes, or the set of organs. In various cases, the system can rotate the three-dimensional medical image, such that the anatomical orientation now matches a predetermined anatomical orientation.

公开(公告)号：US20230052595A1

公开(公告)日：2023-02-16

申请号：US17403017

申请日：2021-08-16

Applicant: GE Precision Healthcare LLC

Inventor： Rajesh Veera Venkata Lakshmi Langoju ,  Utkarsh Agrawal ,  Bipul Das ,  Risa Shigemasa ,  Yasuhiro Imai ,  Jiang Hsieh

IPC: G06T5/00 ,  G06T3/40 ,  G06T5/50

Abstract: Techniques are described for enhancing the quality of three-dimensional (3D) anatomy scan images using deep learning. 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 reception component that receives a scan image generated from 3D scan data relative to a first axis of a 3D volume, and an enhancement component that applies an enhancement model to the scan image to generate an enhanced scan image having a higher resolution relative to the scan image. The enhancement model comprises a deep learning neural network model trained on training image pairs respectively comprising a low-resolution scan image and a corresponding high-resolution scan image respectively generated relative to a second axis of the 3D volume.

公开(公告)号：US20230013779A1

公开(公告)日：2023-01-19

申请号：US17368534

申请日：2021-07-06

Applicant: GE Precision Healthcare LLC

Inventor： Rajesh Veera Venkata Lakshmi Langoju ,  Prasad Sudhakara Murthy ,  Utkarsh Agrawal ,  Bhushan D. Patil ,  Bipul Das

IPC: G06T5/00 ,  G06T5/20 ,  G06T3/40 ,  G06N20/00

Abstract: Systems/techniques that facilitate self-supervised deblurring are provided. In various embodiments, a system can access an input image generated by an imaging device. In various aspects, the system can train, in a self-supervised manner based on a point spread function of the imaging device, a machine learning model to deblur the input image. More specifically, the system can append to the model one or more non-trainable convolution layers having a blur kernel that is based on the point spread function of the imaging device. In various aspects, the system can feed the input image to the model, the model can generate a first output image based on the input image, the one or more non-trainable convolution layers can generate a second output image by convolving the first output image with the blur kernel, and the system can update parameters of the model based on a difference between the input image and the second output image.

公开(公告)号：US20220327664A1

公开(公告)日：2022-10-13

申请号：US17225395

申请日：2021-04-08

Applicant: GE PRECISION HEALTHCARE LLC

Inventor： Bhushan Dayaram Patil ,  Rajesh Langoju ,  Utkarsh Agrawal ,  Bipul Das ,  Jiang Hsieh

IPC: G06T5/00 ,  G06T11/00 ,  G06N3/08 ,  G16H30/20

Abstract: A computer-implemented method for correcting artifacts in computed tomography data is provided. The method includes inputting a sinogram into a trained sinogram correction network, wherein the sinogram is missing a pixel value for at least one pixel. The method also includes processing the sinogram via one or more layers of the trained sinogram correction network, wherein processing the sinogram includes deriving complementary information from the sinogram and estimating the pixel value for the at least one pixel based on the complementary information. The method further includes outputting from the trained sinogram correction network a corrected sinogram having the estimated pixel value.

公开(公告)号：US20220101048A1

公开(公告)日：2022-03-31

申请号：US17093960

申请日：2020-11-10

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Gopal B. Avinash ,  Máté Fejes ,  Ravi Soni ,  Dániel Attila Szabó ,  Rakesh Mullick ,  Vikram Melapudi ,  Krishna Seetharam Shriram ,  Sohan Rashmi Ranjan ,  Bipul Das ,  Utkarsh Agrawal ,  László Ruskó ,  Zita Herczeg ,  Barbara Darázs

IPC: G06K9/62 ,  G06T5/50 ,  G06T7/30 ,  G06N5/04 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50 ,  A61B6/03 ,  A61B6/00 ,  A61B5/055 ,  A61B5/00

Abstract: Techniques are described for generating mono-modality training image data from multi-modality image data and using the mono-modality training image data to train and develop mono-modality image inferencing models. A method embodiment comprises generating, by a system comprising a processor, a synthetic 2D image from a 3D image of a first capture modality, wherein the synthetic 2D image corresponds to a 2D version of the 3D image in a second capture modality, and wherein the 3D image and the synthetic 2D image depict a same anatomical region of a same patient. The method further comprises transferring, by the system, ground truth data for the 3D image to the synthetic 2D image. In some embodiments, the method further comprises employing the synthetic 2D image to facilitate transfer of the ground truth data to a native 2D image captured of the same anatomical region of the same patient using the second capture modality.

公开(公告)号：US20210312674A1

公开(公告)日：2021-10-07

申请号：US16899835

申请日：2020-06-12

Applicant: GE Precision Healthcare LLC

Inventor： Sidharth Abrol ,  Bipul Das ,  Sandeep Dutta ,  Saad A. Sirohey

IPC: G06T11/00 ,  G06T7/10 ,  G06N20/00 ,  G06K9/62

Abstract: Techniques are described for domain adaptation of image processing models using post-processing model correction According to an embodiment, a method comprises training, by a system operatively coupled to a processor, a post-processing model to correct an image-based inference output of a source image processing model that results from application of the source image processing model to a target image from a target domain that differs from a source domain, wherein the source image processing model was trained on source images from the source domain. In one or more implementations, the source imaging processing model comprises an organ segmentation model and the post-processing model can comprise a shape-autoencoder. The method further comprises applying, by the system, the source image processing model and the post-processing model to target images from the target domain to generate optimized image-based inference outputs for the target images.