公开(公告)号：US20240203039A1

公开(公告)日：2024-06-20

申请号：US18065964

申请日：2022-12-14

Applicant: GE Precision Healthcare LLC

Inventor： Deepa Anand ,  Bipul Das ,  Vanika Singhal ,  Rakesh Mullick ,  Sanjay Kumar NT

IPC: G06T15/20 ,  G06T15/08 ,  G06V10/82

CPC classification number: G06T15/20 ,  G06T15/08 ,  G06V10/82

Abstract: Systems/techniques that facilitate interpretable task-specific dimensionality-reduction are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can generate, via execution of a first deep learning neural network, a voxel-wise weight map corresponding to the three-dimensional medical image and a set of projection vectors corresponding to the three-dimensional medical image. In various instances, the system can generate a set of two-dimensional projection images of the three-dimensional medical image, based on the voxel-wise weight map and the set of projection vectors. In various cases, the first deep learning neural network can be trained in a serial pipeline with a second deep learning neural network that is configured to perform an inferencing task on two-dimensional inputs. This can cause the set of two-dimensional projection images to be tailored to the inferencing task.

公开(公告)号：US20240160915A1

公开(公告)日：2024-05-16

申请号：US18055648

申请日：2022-11-15

Applicant: GE Precision Healthcare LLC

Inventor： Prasad Sudhakara Murthy ,  Utkarsh Agrawal ,  Bipul Das

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Systems/techniques that facilitate explainable deep interpolation are provided. In various embodiments, a system can access a data candidate, wherein a set of numerical elements of the data candidate are missing. In various aspects, the system can generate, via execution of a deep learning neural network on the data candidate, a set of weight maps for the set of missing numerical elements. In various instances, the system can compute the set of missing numerical elements by respectively combining, according to the set of weight maps, available interpolation neighbors of the set of missing numerical elements.

公开(公告)号：US20240078669A1

公开(公告)日：2024-03-07

申请号：US18497912

申请日：2023-10-30

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Máté Fejes ,  Gopal Avinash ,  Ravi Soni ,  Bipul Das ,  Rakesh Mullick ,  Pál Tegzes ,  Lehel Ferenczi ,  Vikram Melapudi ,  Krishna Seetharam Shriram

IPC: G06T7/00 ,  G06N3/08 ,  G06T15/08

CPC classification number: G06T7/0012 ,  G06N3/08 ,  G06T15/08 ,  G06T2207/10088 ,  G06T2207/10104

Abstract: Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.

公开(公告)号：US11727086B2

公开(公告)日：2023-08-15

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

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：US20230071535A1

公开(公告)日：2023-03-09

申请号：US17470076

申请日：2021-09-09

Applicant: GE Precision Healthcare LLC ,  University of Zurich

Inventor： Sidharth Abrol ,  Bipul Das ,  Vanika Singhal ,  Amy Deubig ,  Sandeep Dutta ,  Daphné GERBAUD ,  Bianca Sintini ,  Ronny BÜCHEL ,  Philipp KAUFMANN

IPC: G06T7/00 ,  G06T5/00 ,  G06T7/11 ,  G06N20/00 ,  G16H30/40 ,  G06K9/32

Abstract: Systems/techniques that facilitate learning-based domain transformation for medical images are provided. In various embodiments, a system can access a medical image. In various aspects, the medical image can depict an anatomical structure according to a first medical scanning domain. In various instances, the system can generate, via execution of a machine learning model, a predicted image based on the medical image. In various aspects, the predicted image can depict the anatomical structure according to a second medical scanning domain that is different from the first medical scanning domain. In some cases, the first and second medical scanning domains can be first and second energy levels of a computed tomography (CT) scanning modality. In other cases, the first and second medical scanning domains can be first and second contrast phases of the CT scanning modality.

公开(公告)号：US20230048231A1

公开(公告)日：2023-02-16

申请号：US17444881

申请日：2021-08-11

Applicant: GE Precision Healthcare LLC

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

IPC: G06T11/00 ,  G06T15/08 ,  G06T3/40 ,  G06N3/08 ,  G06N3/04

Abstract: Various methods and systems are provided for computed tomography imaging. In one embodiment, a method includes acquiring, with an x-ray detector and an x-ray source coupled to a gantry, a three-dimensional image volume of a subject while the subject moves through a bore of the gantry and the gantry rotates the x-ray detector and the x-ray source around the subject, inputting the three-dimensional image volume to a trained deep neural network to generate a corrected three-dimensional image volume with a reduction in aliasing artifacts present in the three-dimensional image volume, and outputting the corrected three-dimensional image volume. In this way, aliasing artifacts caused by sub-sampling may be removed from computed tomography images while preserving details, texture, and sharpness in the computed tomography images.

公开(公告)号：US20220284570A1

公开(公告)日：2022-09-08

申请号：US17192804

申请日：2021-03-04

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Máté Fejes ,  Gopal Avinash ,  Ravi Soni ,  Bipul Das ,  Rakesh Mullick ,  Pál Tegzes ,  Lehel Ferenczi ,  Vikram Melapudi ,  Krishna Seetharam Shriram

IPC: G06T7/00 ,  G06N3/08 ,  G06T15/08

Abstract: Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.

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：US12141900B2

公开(公告)日：2024-11-12

申请号：US17543234

申请日：2021-12-06

Applicant: GE Precision Healthcare LLC

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

IPC: G06T11/00 ,  G06N20/20 ,  G06T5/50 ,  G06T5/70

Abstract: Systems/techniques that facilitate machine learning generation of low-noise and high structural conspicuity images are provided. In various embodiments, a system can access an image and can apply at least one of image denoising or image resolution enhancement to the image, thereby yielding a first intermediary image. In various instances, the system can generate, via execution of a plurality of machine learning models, a plurality of second intermediary images based on the first intermediary image, wherein a given machine learning model in the plurality of machine learning models receives as input the first intermediary image, wherein the given machine learning model produces as output a given second intermediary image in the plurality of second intermediary images, and wherein the given second intermediary image represents a kernel-transformed version of the first intermediary image. In various cases, the system can generate a blended image based on the plurality of second intermediary images.

公开(公告)号：US11842485B2

公开(公告)日：2023-12-12

申请号：US17192804

申请日：2021-03-04

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Máté Fejes ,  Gopal Avinash ,  Ravi Soni ,  Bipul Das ,  Rakesh Mullick ,  Pál Tegzes ,  Lehel Ferenczi ,  Vikram Melapudi ,  Krishna Seetharam Shriram

IPC: G06T7/00 ,  G06N3/08 ,  G06T15/08

CPC classification number: G06T7/0012 ,  G06N3/08 ,  G06T15/08 ,  G06T2207/10088 ,  G06T2207/10104

Abstract: Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.