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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
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.
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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
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.
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3.发明公开公开(公告)号：US20240285256A1
公开(公告)日：2024-08-29
申请号：US18175307
申请日：2023-02-27
Applicant: GE Precision Healthcare LLC
Inventor： Pavan Annangi , Deepa Anand , Stephan Anzengruber , Bhushan D. Patil , Arathi Sreekumari
CPC classification number: A61B8/483 , A61B8/466 , G06T2207/20084
Abstract: Various methods and ultrasound imaging systems are provided for segmenting an object. In one example, a method includes accessing a volumetric ultrasound dataset, receiving an identification of a seed point for an object in an image generated based on the volumetric ultrasound dataset, and implementing a two-dimensional segmentation model on a first plurality of parallel slices based on the seed point to generate a first plurality of segmented regions. The method includes implementing the two-dimensional segmentation model on a second plurality of parallel slices based on the seed point to generate a second plurality of segmented regions. The method includes generating a detected region by accumulating the first plurality of segmented regions and the second plurality of segmented regions. The method includes implementing a shape completion model to generate a three-dimensional shape model for the object, and displaying rendering of the object based on the three-dimensional shape model.
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公开(公告)号：US20220067919A1
公开(公告)日：2022-03-03
申请号：US17003467
申请日：2020-08-26
Applicant: GE Precision Healthcare LLC
Inventor： Krishna Seetharam Shriram , Arathi Sreekumari , Rakesh Mullick
Abstract: The present disclosure relates to a system and method for identifying a tumor or lesion in a probability map. In accordance with certain embodiments, a method includes identifying, with a processor, a first region of interest in a first projection image, generating, with the processor, a first probability map from the first projection image and a second probability map from a second projection image, wherein the first probability map includes a second region of interest that has location that corresponds to a location of the first region of interest, interpolating the first probability map and the second probability map, thereby generating a probability volume, wherein the probability volume includes the second region of interest, and outputting, with the processor, a representation of the probability volume to a display.
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公开(公告)号：US12106859B2
公开(公告)日：2024-10-01
申请号：US17540487
申请日：2021-12-02
Applicant: GE Precision Healthcare LLC
Inventor： Naga Durga Purnima Pilli , Nagapriya Kavoori Sethumadhavan , Arathi Sreekumari , Anu Antony
CPC classification number: G16H50/30 , A61B8/02 , A61B8/0866 , A61B8/5223 , G06N20/00
Abstract: Systems/techniques that facilitate two-tiered machine learning generation of birth risk score are provided. In various embodiments, a system can access a plurality of medical feature collections associated with a pregnant patient. In various aspects, the system can generate, via execution of a plurality of first trained machine learning models, a plurality of embedded features based on the plurality of medical feature collections. In various instances, the system can compute, via execution of a second trained machine learning model, a risk score based on the plurality of embedded features, wherein the risk score indicates an amount of risk to a health of the pregnant patient or a health of a fetus of the pregnant patient that is associated with performing a caesarian-section on the pregnant patient or with waiting for the pregnant patient to give birth naturally.
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Patent Agency Ranking
公开(公告)号：US11195277B2
公开(公告)日：2021-12-07
申请号：US16457710
申请日：2019-06-28
Applicant: GE Precision Healthcare LLC
Inventor： Dattesh Dayanand Shanbhag , Arathi Sreekumari , Sandeep Kaushik
Abstract: Methods and systems are provided for generating a normative medical image from an anomalous medical image. In an example, the method includes receiving an anomalous medical image, wherein the anomalous medical image includes anomalous data, mapping the anomalous medical image to a normative medical image using a trained generative network of a generative adversarial network (GAN), wherein the anomalous data of the anomalous medical image is mapped to normative data in the normative medical image. In some examples, the method may further include displaying the normative medical image via a display device, and/or utilizing the normative medical image for further image analysis tasks to generate robust outcomes from the anomalous medical image.
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公开(公告)号：US20230293014A1
公开(公告)日：2023-09-21
申请号：US18142726
申请日：2023-05-03
Applicant: GE Precision Healthcare LLC
Inventor： Dattesh Dayanand Shanbhag , Rekesh Mullick , Arathi Sreekumari , Uday Damodar Patil , Trevor John Kolupar , Chitresh Bhushan , Andre de Almeida Maximo , Thomas Kwok-Fah Foo , Maggie MeiKei Fung
CPC classification number: A61B5/0033 , A61B5/055 , G06T7/0012
Abstract: The present disclosure relates to use of a workflow for automatic prescription of different radiological imaging scan planes across different anatomies and modalities. The automated prescription of such imaging scan planes helps ensure contiguous visualization of the different landmark structures. Unlike prior approaches, the disclosed technique determines the necessary planes using the localizer images itself and does not explicitly segment or delineate the landmark structures to perform plane prescription.
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公开(公告)号：US11610313B2
公开(公告)日：2023-03-21
申请号：US17512469
申请日：2021-10-27
Applicant: GE Precision Healthcare LLC
Inventor： Dattesh Dayanand Shanbhag , Arathi Sreekumari , Sandeep Kaushik
Abstract: Methods and systems are provided for generating a normative medical image from an anomalous medical image. In an example, the method includes receiving an anomalous medical image, wherein the anomalous medical image includes anomalous data, mapping the anomalous medical image to a normative medical image using a trained generative network of a generative adversarial network (GAN), wherein the anomalous data of the anomalous medical image is mapped to normative data in the normative medical image. In some examples, the method may further include displaying the normative medical image via a display device, and/or utilizing the normative medical image for further image analysis tasks to generate robust outcomes from the anomalous medical image.
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公开(公告)号：US11452494B2
公开(公告)日：2022-09-27
申请号：US16575092
申请日：2019-09-18
Applicant: GE Precision Healthcare LLC
Inventor： Krishna Seetharam Shriram , Arathi Sreekumari , Rakesh Mullick
Abstract: Systems and methods are provided for projection profile enabled computer aided detection (CAD). Volumetric ultrasound dataset may be generated, based on echo ultrasound signals, and based on the volumetric ultrasound dataset, a three-dimensional (3D) ultrasound volume may generated. Selective structure detection may be applied to the three-dimensional (3D) ultrasound volume. The selective structure detection may include generating based on a projection of the three-dimensional (3D) ultrasound volume in a particular spatial direction, a two-dimensional (2D) image; applying two-dimensional (2D) structure detection to the two-dimensional (2D) image, to identify structure candidates associated with a particular type of structures; selecting for each identified structure candidate, a corresponding local volume within the three-dimensional (3D) ultrasound volume; applying three-dimensional (3D) structure detection to each selected local volume; and identifying based on applying the three-dimensional (3D) structure detection, one or more structure candidates that match the particular type of structures.
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公开(公告)号：US20230178244A1
公开(公告)日：2023-06-08
申请号：US17540487
申请日：2021-12-02
Applicant: GE Precision Healthcare LLC
Inventor： Naga Durga Purnima Pilli , Nagapriya Kavoori Sethumadhavan , Arathi Sreekumari , Anu Antony
CPC classification number: G16H50/30 , A61B8/02 , A61B8/0866 , A61B8/5223 , G06N20/00
Abstract: Systems/techniques that facilitate two-tiered machine learning generation of birth risk score are provided. In various embodiments, a system can access a plurality of medical feature collections associated with a pregnant patient. In various aspects, the system can generate, via execution of a plurality of first trained machine learning models, a plurality of embedded features based on the plurality of medical feature collections. In various instances, the system can compute, via execution of a second trained machine learning model, a risk score based on the plurality of embedded features, wherein the risk score indicates an amount of risk to a health of the pregnant patient or a health of a fetus of the pregnant patient that is associated with performing a caesarian-section on the pregnant patient or with waiting for the pregnant patient to give birth naturally.
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