公开(公告)号：US20250069218A1

公开(公告)日：2025-02-27

申请号：US18453954

申请日：2023-08-22

Applicant: GE Precision Healthcare LLC

Inventor： Deepa Anand ,  Bipul Das ,  Vanika Singhal ,  Rakesh Mullick ,  Sandeep Dutta ,  Amy L Deubig ,  Maud Bonnard ,  Christine Smith

IPC: G06T7/00 ,  G06T7/60 ,  G06T15/00 ,  G06V10/24 ,  G06V10/44

Abstract: The current disclosure provides systems and methods for automatic image alignment of three-dimensional (3D) medical image volumes. The method includes pre-processing the 3D medical image volume by selecting a sub-volume of interest, detecting anatomical landmarks in the sub-volume using a deep neural network, estimating transformation parameters based on the anatomical landmarks to adjust rotation angles and translation of the sub-volume, adjusting the rotation angles and translation to produce a first aligned sub-volume, determining confidence in the transformation parameters based on the first aligned sub-volume, and iteratively refining the transformation parameters if the confidence is below a predetermined threshold. The disclosed approach for automated image alignment reduces the need for manual alignment and, increases a probability of the 3D image volume converging to a desired orientation compared to conventional approaches.

公开(公告)号：US20240428567A1

公开(公告)日：2024-12-26

申请号：US18340246

申请日：2023-06-23

Applicant: GE Precision Healthcare LLC

Inventor： Pavan Annangi ,  Vikram Reddy Melapudi ,  Hariharan Ravishankar ,  Deepa Anand

IPC: G06V10/774 ,  G06T7/00 ,  G06V10/776 ,  G06V10/82

Abstract: Techniques are described for refining or updating medical image inferencing models post deployment using synthetic images generated from non-image data feedback. In an example, a system can comprise a memory that stores computer-executable components and a processor that executes the computer-executable components stored in the memory. The computer-executable components can comprise an image generation component that generates synthetic medical images based on feedback information associated with performance of a medical image inferencing model received in association with application of the medical image inferencing model to medical images in a deployment environment, wherein the feedback information excludes image data. The computer-executable components can further comprise a refinement component that updates the medical image inferencing model using the synthetic images and a model updating process.

公开(公告)号：US20240273731A1

公开(公告)日：2024-08-15

申请号：US18166907

申请日：2023-02-09

Applicant: GE Precision Healthcare LLC

Inventor： Arathi Sreekumari ,  Krishna Seetharam Shriram ,  Deepa Anand ,  Pavan Annangi ,  Bhushan Patil ,  Stephan W. Anzengruber

IPC: G06T7/136 ,  G06T7/00

CPC classification number: G06T7/136 ,  G06T7/0012 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016 ,  G06T2207/30096

Abstract: Systems/techniques that facilitate anatomy-driven augmentation of medical images are provided. In various embodiments, a system can access a medical image and a ground-truth segmentation mask corresponding to the medical image, wherein the ground-truth segmentation mask can indicate a location of a first anatomical structure depicted in the medical image. In various aspects, the system can create an augmented version of the medical image and an augmented version of the ground-truth segmentation mask, by applying a continuous deformation field to fewer than all pixels or voxels in the medical image and in the ground-truth segmentation mask. In various instances, the continuous deformation field can encompass: pixels or voxels that correspond to the first anatomical structure; and pixels or voxels that correspond to a surrounding periphery of the first anatomical structure.

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：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

IPC: G06T7/00 ,  G06N20/00 ,  G06T3/147 ,  G06T7/11

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：US11803967B2

公开(公告)日：2023-10-31

申请号：US17220770

申请日：2021-04-01

Applicant: GE Precision Healthcare LLC

Inventor： Pavan Annangi ,  Rahul Venkataramani ,  Deepa Anand ,  Eigil Samset

IPC: G06T7/00 ,  A61B8/08 ,  G06N3/088 ,  G06V10/25 ,  G06V10/98 ,  G06F18/24 ,  G06F18/214 ,  G06N3/045

CPC classification number: G06T7/0014 ,  A61B8/0883 ,  A61B8/5223 ,  G06F18/2148 ,  G06F18/24 ,  G06N3/045 ,  G06N3/088 ,  G06V10/25 ,  G06V10/98 ,  G06T2207/10016 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/30048 ,  G06V2201/031

Abstract: Various methods and systems are provided for bicuspid valve detection with ultrasound imaging. In one embodiment, a method comprises acquiring ultrasound video of a heart over at least one cardiac cycle, identifying frames in the ultrasound video corresponding to at least one cardiac phase, and classifying a cardiac structure in the identified frames as a bicuspid valve or a tricuspid valve. A generative model such as a variational autoencoder trained on ultrasound image frames at the at least one cardiac phase may be used to classify the cardiac structure. In this way, relatively rare occurrences of bicuspid aortic valves may be automatically detected during regular cardiac ultrasound screenings.

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.

公开(公告)号：US20220335597A1

公开(公告)日：2022-10-20

申请号：US17233807

申请日：2021-04-19

Applicant: GE Precision Healthcare LLC

Inventor： Dattesh Shanbhag ,  Deepa Anand ,  Chitresh Bhushan ,  Arathi Sreekumari ,  Soumya Ghose

IPC: G06T7/00 ,  G06N20/00

Abstract: Systems and methods for workflow management for labeling the subject anatomy are provided. The method comprises obtaining at least one localizer image of a subject anatomy using a low-resolution medical imaging device. The method further comprises labeling at least one anatomical point within the at least one localizer image. The method further comprises extracting using a machine learning module a mask of the at least one localizer image comprising the at least one anatomical point label. The method further comprises using the mask to label at least one anatomical point on a high-resolution image of the subject anatomy based on the at least one anatomical point within the localizer image.

公开(公告)号：US20250149169A1

公开(公告)日：2025-05-08

申请号：US18504649

申请日：2023-11-08

Applicant: GE Precision Healthcare LLC

Inventor： Deepa Anand ,  Dattesh Shanbhag ,  Hariharan Ravishankar ,  Suresh Emmanuel Devadoss Joel ,  Rakesh Mullick ,  Rachana Sathish ,  Rahul Venkataramani ,  Krishna Seetharam Shriram ,  Prasad Sudhakara Murthy

IPC: G16H50/20 ,  G06T7/00 ,  G16H30/40

Abstract: Systems or techniques for facilitating learnable visual prompt engineering are provided. In various embodiments, a system can access a medical image and a pre-trained machine learning model that is configured to perform a diagnostic or prognostic inferencing task. In various aspects, the system can apply a pre-processing transformation to one or more pixels or voxels of the medical image, thereby yielding a transformed version of the medical image, wherein the pre-processing transformation can convert an input pixel or voxel intensity value to an output pixel or voxel intensity value via one or more parameters that are iteratively learned. In various instances, the system can perform the diagnostic or prognostic inferencing task, by executing the pre-trained machine learning model on the transformed version of the medical image.

公开(公告)号：US20250104221A1

公开(公告)日：2025-03-27

申请号：US18491992

申请日：2023-10-23

Applicant: GE Precision Healthcare LLC

Inventor： Dattesh Dayanand Shanbhag ,  Deepa Anand ,  Rakesh Mullick ,  Sudhanya Chatterjee ,  Aanchal Mongia ,  Uday Damodar Patil

IPC: G06T7/00 ,  G06T5/40 ,  G06V10/44 ,  G06V20/70 ,  G16H15/00 ,  G16H30/20 ,  G16H30/40

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.