公开(公告)号：US20250032086A1

公开(公告)日：2025-01-30

申请号：US18225811

申请日：2023-07-25

Applicant: GE Precision Healthcare LLC

Inventor： Stephan Anzengruber ,  Balint Czupi ,  Pavan Kumar V. Annangi ,  Deepa Anand ,  Bhushan Patil ,  Cindy L. Smrt ,  Martin Swoboda

IPC: A61B8/08 ,  A61B8/00 ,  G06T7/00 ,  G06T7/12

Abstract: Systems and methods for enhancing visualization and documentation of fibroid quantity, size, and location with respect to a uterus and endometrium in ultrasound imaging are provided. The method includes receiving, by at least one processor, an ultrasound volume including ultrasound image data of a region of interest that includes a uterus. The method includes automatically segmenting, by the at least one processor, the uterus and an endometrium in the ultrasound volume. The method includes segmenting one or more fibroids in the ultrasound image data. The method includes generating a classification of each of the one or more fibroids based on a location of each of the one or more fibroids with respect to the endometrium and the uterus. The method includes causing, by the at least one processor, a display system to present at least one image identifying the segmented uterus, endometrium, and one or more fibroids.

公开(公告)号：US20240062331A1

公开(公告)日：2024-02-22

申请号：US17821058

申请日：2022-08-19

Applicant: GE Precision Healthcare LLC

Inventor： Rajesh Langoju ,  Prasad Sudhakara Murthy ,  Utkarsh Agrawal ,  Risa Shigemasa ,  Bhushan Patil ,  Bipul Das ,  Yasuhiro Imai

IPC: G06T3/40 ,  G06T7/00 ,  G06T5/00 ,  G06T7/11 ,  G06N3/08

CPC classification number: G06T3/4046 ,  G06T7/0012 ,  G06T5/002 ,  G06T7/11 ,  G06N3/08

Abstract: Systems/techniques that facilitate deep learning robustness against display field of view (DFOV) variations are provided. In various embodiments, a system can access a deep learning neural network and a medical image. In various aspects, a first DFOV, and thus a first spatial resolution, on which the deep learning neural network is trained can fail to match a second DFOV, and thus a second spatial resolution, exhibited by the medical image. In various instances, the system can execute the deep learning neural network on a resampled version of the medical image, where the resampled version of the medical image can exhibit the first DFOV and thus the first spatial resolution. In various cases, the system can generate the resampled version of the medical image by up-sampling or down-sampling the medical image until it exhibits the first DFOV and thus the first spatial resolution.

公开(公告)号：US20230052078A1

公开(公告)日：2023-02-16

申请号：US17889201

申请日：2022-08-16

Applicant: GE Precision Healthcare LLC

Inventor： Pavan Annangi ,  Deepa Anand ,  Bhushan Patil ,  Rahul Venkataramani

IPC: G06V10/778 ,  G06V10/20 ,  G06V10/26 ,  G16H30/40

Abstract: Systems and methods for self-supervised representation learning as a means to generate context-specific pretrained models include selecting data from a set of available data sets; selecting a pretext task from domain specific pretext tasks; selecting a target problem specific network architecture based on a user selection from available choices or any customized model as per user preference; and generating a pretrained model for the selected network architecture using the selected data obtained from the set of available data sets and a pretext task as obtained from domain specific pretext tasks.

公开(公告)号：US20230029188A1

公开(公告)日：2023-01-26

申请号：US17385600

申请日：2021-07-26

Applicant: GE Precision Healthcare LLC

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

IPC: G06T5/00 ,  G06T7/00 ,  G06N3/08 ,  G06K9/62

Abstract: The current disclosure provides methods and systems to reduce an amount of structured and unstructured noise in image data. Specifically, a multi-stage deep learning method is provided, comprising training a deep learning network using a set of training pairs interchangeably including input data from a first noisy dataset with a first noise level and target data from a second noisy dataset with a second noise level, and input data from the second noisy dataset and target data from the first noisy dataset; generating an ultra-low noise data equivalent based on a low noise data fed into the trained deep learning network; and retraining the deep learning network on the set of training pairs using the target data of the set of training pairs in a first retraining step, and using the ultra-low noise data equivalent as target data in a second retraining step.

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：US20230342917A1

公开(公告)日：2023-10-26

申请号：US17728003

申请日：2022-04-25

Applicant: GE Precision Healthcare LLC

Inventor： Arathi Sreekumari ,  Pavan Annangi ,  Bhushan Patil ,  Stephan Anzengruber

IPC: A61B8/00 ,  G06T7/00 ,  A61B8/08 ,  G06T7/11 ,  G06T7/62

CPC classification number: G06T7/0012 ,  A61B8/085 ,  A61B8/463 ,  A61B8/466 ,  A61B8/483 ,  A61B8/5215 ,  G06T7/11 ,  G06T7/62 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Systems and methods for automatically segmenting and detecting a menstrual cycle phase in ultrasound images of anatomical structures that change over a patient menstrual cycle are provided. The method includes acquiring, by an ultrasound probe of an ultrasound system, an ultrasound image of a region of interest having an anatomical structure that changes over a patient menstrual cycle. The method includes automatically segmenting, by at least one processor of the ultrasound system, an anatomical structure depicted in the ultrasound image. The method includes automatically predicting, by the at least one processor, a menstrual cycle phase based on the segmentation of the anatomical structure. The method includes causing, by the at least one processor, a display system to present at least one rendering of the segmented anatomical structure and the predicted menstrual cycle phase.