公开(公告)号：US12229670B2

公开(公告)日：2025-02-18

申请号：US17358694

申请日：2021-06-25

Applicant: GE Precision Healthcare LLC

Inventor： Chandan Aladahalli ,  Krishna Seetharam Shriram ,  Vikram Melapudi

IPC: G06N3/08 ,  G06F18/25

Abstract: Systems, computer-implemented methods, and computer program products that facilitate temporalizing and/or spatializing a machine learning and/or artificial intelligence network are provided. In various embodiments, a processor can combine output data from different layers of an artificial neural network trained on static image data. In various embodiments, the processor can employ the artificial neural network to infer an outcome from an image instance in a sequence of images based on combined output data from the different layers of the artificial neural network.

公开(公告)号：US20240221912A1

公开(公告)日：2024-07-04

申请号：US18149485

申请日：2023-01-03

Applicant: GE Precision Healthcare LLC

Inventor： Chandan Kumar Mallappa Aladahalli ,  Krishna Seetharam Shriram ,  Vikram Reddy Melapudi ,  Shital Sheshrao Yelne

IPC: G16H30/40 ,  G06T7/00 ,  G06V10/54 ,  G06V10/771 ,  G06V10/82

CPC classification number: G16H30/40 ,  G06T7/0012 ,  G06V10/54 ,  G06V10/771 ,  G06V10/82 ,  G06T2207/10132 ,  G06T2207/20084

Abstract: Systems/techniques that facilitate task-specific image style transfer are provided. In various embodiments, a system can access a first medical image, wherein the first medical image can exhibit anatomical content and a first visual style. In various aspects, the system can generate, via execution of an optimization algorithm, a second medical image based on the first medical image, wherein the second medical image can exhibit the anatomical content and a second visual style that is different from the first visual style. In various instances, the optimization algorithm can be based on feature maps extracted from a pre-trained deep learning neural network that has been configured to perform an inferencing task on medical images exhibiting the second visual style.

公开(公告)号：US11972584B2

公开(公告)日：2024-04-30

申请号：US17488750

申请日：2021-09-29

Applicant: GE Precision Healthcare LLC

Inventor： Rahul Venkataramani ,  Krishna Seetharam Shriram ,  Aditi Garg

IPC: G06T7/40 ,  G06T7/11

CPC classification number: G06T7/40 ,  G06T7/11 ,  G06T2207/10132 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30061 ,  G06T2207/30084

Abstract: Systems and methods for tissue specific time gain compensation of an ultrasound image are provided. The method comprises acquiring an ultrasound image of a subject and displaying the ultrasound image over a console. The method further comprises selecting by a user a region within the ultrasound image that requires time gain compensation. The method further comprises carrying out time gain compensation of the user selected region of the ultrasound image. The method further comprises identifying a region having a similar texture to the user selected region and carrying out time gain compensation of the user selected region by an artificial intelligence (AI) based deep learning module.

公开(公告)号：US11903760B2

公开(公告)日：2024-02-20

申请号：US17447094

申请日：2021-09-08

Applicant: GE Precision Healthcare LLC

Inventor： Chandan Kumar Aladahalli ,  Krishna Seetharam Shriram ,  Vikram Melapudi

IPC: A61B8/00 ,  A61B8/08

CPC classification number: A61B8/4254 ,  A61B8/463 ,  A61B8/5207

Abstract: The current disclosure provides systems and methods for providing guidance information to an operator of a medical imaging device. In an embodiment, a method is provided, comprising training a deep learning neural network on training pairs including a first medical image of an anatomical neighborhood and a second medical image of the anatomical neighborhood as input data, and a ground truth displacement between a first scan plane of the first medical image and a second scan plane of the second medical image as target data; using the neural network to predict a displacement between a first scan plane of a new medical image of the anatomical neighborhood and a target scan plane of a reference medical image of the anatomical neighborhood; and displaying guidance information for an imaging device used to acquire the new medical image on a display screen.

公开(公告)号：US11712224B2

公开(公告)日：2023-08-01

申请号：US16600394

申请日：2019-10-11

Applicant: GE Precision Healthcare LLC

Inventor： Chandan Kumar Mallappa Aladahalli ,  Krishna Seetharam Shriram ,  Vikram Melapudi

IPC: A61B8/08 ,  G06T7/00 ,  G06V20/20 ,  G06V10/764 ,  G06V10/82 ,  G06V10/26

CPC classification number: A61B8/5215 ,  G06T7/0012 ,  G06V10/26 ,  G06V10/764 ,  G06V10/82 ,  G06V20/20 ,  G06T2207/10132

Abstract: Various methods and systems are provided for generating a context awareness graph for a medical scan image. In one example, the context awareness graph includes relative size and relative position annotations with regard to one or more internal anatomical features in the scan image to enable a user to determine a current scan plane and further, to guide the user to a target scan plane.

公开(公告)号：US11419585B2

公开(公告)日：2022-08-23

申请号：US16687392

申请日：2019-11-18

Applicant: GE Precision Healthcare LLC

Inventor： Chandan Kumar Mallappa Aladahalli ,  Krishna Seetharam Shriram ,  Vikram Melapudi

IPC: A61B8/08 ,  A61B8/00 ,  G06T7/194

Abstract: Methods and systems are provided for turbulence monitoring during ultrasound scanning. In one example, during scanning with an ultrasound probe, a turbulence amount between two successive frames may be monitored, and in response to the turbulence amount at or above the higher threshold, deployment of the one or more image interpretation protocols may be stopped or delayed until the turbulence amount decreases below the higher threshold.

公开(公告)号：US20220092768A1

公开(公告)日：2022-03-24

申请号：US17122709

申请日：2020-12-15

Applicant: GE Precision Healthcare LLC

Inventor： Vikram Melapudi ,  Bipul Das ,  Krishna Seetharam Shriram ,  Prasad Sudhakar ,  Rakesh Mullick ,  Sohan Rashmi Ranjan ,  Utkarsh Agarwal

IPC: G06T7/00 ,  G06T5/00 ,  G06T7/10 ,  G06T11/00 ,  A61B6/00

Abstract: Techniques are provided for generating enhanced image representations from original X-ray images using deep learning techniques. In one embodiment, a system is provided that includes a memory storing computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a reception component, an analysis component, and an artificial intelligence component. The analysis component analyzes the original X-ray image using an AI-based model with respect to a set of features of interest. The AI component generates a plurality of enhanced image representations. Each enhanced image representation highlights a subset of the features of interest and suppresses remaining features of interest in the set that are external to the subset.

公开(公告)号：US20250057508A1

公开(公告)日：2025-02-20

申请号：US18449088

申请日：2023-08-14

Applicant: GE Precision Healthcare LLC

Inventor： Rahul Venkataramani ,  Krishna Seetharam Shriram ,  Chandan Kumar Mallappa Aladahalli ,  Christian Fritz Perrey ,  Michaela Hofbauer

IPC: A61B8/00 ,  A61B8/08

Abstract: Systems and methods for characterizing uncertainty on a boundary of a region of interest includes inputting, via a processor, an ultrasound image having the region of interest into a trained neural network. Systems and methods also include outputting, via the processor, from the trained neural network a first prediction of the boundary of the region of interest and a second prediction of a region segmentation of the region of interest. Systems and methods further include determining, via the processor, an uncertainty on the boundary based on mismatch between the first prediction and the second prediction.

公开(公告)号：US20240428421A1

公开(公告)日：2024-12-26

申请号：US18337968

申请日：2023-06-20

Applicant: GE Precision Healthcare LLC

Inventor： Krishna Seetharam Shriram ,  Vikram Reddy Melapudi ,  Hariharan Ravishankar ,  Pavan Annangi ,  Chandan Kumar Mallappa Aladahalli

IPC: G06T7/12 ,  G06T7/00 ,  G06T7/13 ,  G06V10/25

Abstract: Systems/techniques that facilitate improved uncertainty estimation via object-specific and object-agnostic segmentation disagreement are provided. In various embodiments, a system can access an image depicting an object. In various aspects, the system can localize, via execution of an object-specific segmentation model on the image, a first inferred boundary of the object. In various instances, the system can generate an uncertainty score for the first inferred boundary, based on a second inferred boundary of the object generated via execution of an object-agnostic segmentation model on the image.

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.