公开(公告)号：US12051178B2

公开(公告)日：2024-07-30

申请号：US18304947

申请日：2023-04-21

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Pál Tegzes ,  Levente Imre Török ,  Lehel Ferenczi ,  Gopal B. Avinash ,  László Ruskó ,  Gireesha Chinthamani Rao ,  Khaled Younis ,  Soumya Ghose

IPC: G06T5/50 ,  G06F18/21 ,  G06F18/214 ,  G06F18/22 ,  G06F18/23 ,  G06F18/28 ,  G06T7/00 ,  G06V10/74 ,  G06V10/762 ,  G06V10/772 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

CPC classification number: G06T5/50 ,  G06F18/214 ,  G06F18/217 ,  G06F18/22 ,  G06F18/23 ,  G06F18/28 ,  G06T7/00 ,  G06V10/761 ,  G06V10/762 ,  G06V10/772 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82

Abstract: Techniques are described for optimizing deep learning model performance using image harmonization as a pre-processing step. According to an embodiment, a method comprises decomposing, by a system operatively coupled to a processor, an input image into sub-images. The method further comprises harmonizing the sub-images with corresponding reference sub-images of at least one reference image based on two or more different statistical values respectively calculated for the sub-images and the corresponding reference-sub images, resulting in transformation of the sub-images into modified sub-images images. In some implementations, the modified sub-images can be combined into a harmonized image having a more similar appearance to the at least one reference image relative to the input image. In other implementations, harmonized images and/or modified sub-images generated using these techniques can be used as ground-truth training samples for training one or more deep learning model to transform input images with appearance variations into harmonized images.

公开(公告)号：US20240020792A1

公开(公告)日：2024-01-18

申请号：US17813264

申请日：2022-07-18

Applicant: GE Precision Healthcare LLC

Inventor： Michel S. Tohme ,  Vincent Bismuth ,  Ludovic Boilevin Kayl ,  German Guillermo Vera Gonzalez ,  Tao Tan ,  Gopal B. Avinash

IPC: G06T5/00 ,  G06T7/80

CPC classification number: G06T5/002 ,  G06T7/80 ,  G06T2207/10081 ,  G06T2207/20081

Abstract: Various methods and systems are provided for denoising images. In one example, a method includes obtaining an input image and a noise map representing noise in the input image, generating, from the noise map and based on a calibration factor, a strength map, entering the input image and the strength map as input to a denoising model trained to output a denoised image based on the input image and the strength map, and displaying and/or saving the denoised image output by the denoising model.

公开(公告)号：US12121382B2

公开(公告)日：2024-10-22

申请号：US17690258

申请日：2022-03-09

Applicant: GE Precision Healthcare LLC

Inventor： Dejun Wang ,  Buer Qi ,  Tao Tan ,  Gireesha Chinthamani Rao ,  Gopal B. Avinash ,  Qingming Peng ,  Yaan Ge ,  Sylvain Bernard ,  Vincent Bismuth

IPC: A61B6/40 ,  A61B6/00 ,  A61B6/02 ,  G06N3/045 ,  G06T3/4046 ,  G06V10/25

CPC classification number: A61B6/4429 ,  A61B6/025 ,  A61B6/5205 ,  G06N3/045 ,  G06T3/4046 ,  G06V10/25

Abstract: A tomosynthesis machine allows for faster image acquisition and improved signal-to-noise by acquiring a projection attenuation data and using machine learning to identify a subset of the projection attenuation data for the production of thinner slices and/or higher resolution slices using machine learning.

公开(公告)号：US12100170B2

公开(公告)日：2024-09-24

申请号：US17543283

申请日：2021-12-06

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Balázs Péter Cziria ,  Pál Tegzes ,  Gopal Biligeri Avinash ,  German Guillermo Vera Gonzalez ,  Lehel Mihály Ferenczi ,  Zita Herczeg ,  Ravi Soni ,  Dibyajyoti Pati

IPC: G06T7/30 ,  G06F18/214

CPC classification number: G06T7/30 ,  G06F18/214 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Systems/techniques that facilitate multi-layer image registration are provided. In various embodiments, a system can access a first image and a second image. In various aspects, the system can generate, via execution of a machine learning model on the first image and the second image, a plurality of registration fields and a plurality of weight matrices that respectively correspond to the plurality of registration fields. In various instances, the system can register the first image with the second image based on the plurality of registration fields and the plurality of weight matrices.

公开(公告)号：US20240193761A1

公开(公告)日：2024-06-13

申请号：US18064541

申请日：2022-12-12

Applicant: GE Precision Healthcare LLC

Inventor： Hongxu Yang ,  Gopal Biligeri Avinash ,  Lehel Mihály Ferenczi ,  Xiaomeng Dong ,  Najib Akram Maheen Aboobacker ,  Gireesha Chinthamani Rao ,  Tao Tan ,  German Guillermo Vera Gonzalez

IPC: G06T7/00 ,  G06T3/40

CPC classification number: G06T7/0012 ,  G06T3/4046 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004

Abstract: Systems/techniques that facilitate improved deep learning image processing are provided. In various embodiments, a system can access a medical image, wherein pixels or voxels of the medical image can be allocated among a plurality of regions. In various aspects, the system can generate, via execution of a deep learning neural network on the medical image, a set of region-wise parameter maps, wherein a region-wise parameter map can consist of one predicted parameter per region of the medical image. In various instances, the system can generate a transformed version of the medical image by feeding the set of region-wise parameter maps to an analytical transformation function. In various cases, the system can render the transformed version of the medical image on an electronic display. In various aspects, the plurality of regions can be irregular or tissue-based.

公开(公告)号：US20230386022A1

公开(公告)日：2023-11-30

申请号：US17664702

申请日：2022-05-24

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Hongxiang Yi ,  Rakesh Mullick ,  Lehel Mihály Ferenczi ,  Gopal Biligeri Avinash ,  Borbála Deák-Karancsi ,  Balázs Péter Cziria ,  Laszlo Rusko

IPC: G06T7/00 ,  A61B8/08 ,  G06T7/149 ,  G06T7/174

CPC classification number: G06T7/0012 ,  A61B8/0883 ,  G06T7/149 ,  G06T7/174 ,  G06T2207/10136 ,  G06T2207/20061 ,  G06T2207/20124 ,  G06T2207/30048

Abstract: Techniques are described that facilitate dynamic multimodal segmentation selection and fusion in medical imaging. In one example embodiment, a computer processing system receives a segmentation dataset comprising a combination of different image segmentations of an anatomical object of interest respectively segmented via different segmentation models from different medical images captured of the (same) anatomical object, wherein the different medical images and the different image segmentations vary with respect to at least one of, capture modality, acquisition protocol, or acquisition parameters. The system employs a dynamic ranking protocol as opposed to a static ranking protocol to determine ranking scores for the different image segmentations that control relative contributions of the different image segmentations in association with combining the different image segmentations into a fused segmentation for the anatomical object. The system further combines the different image segmentations based on the ranking scores to generate the fused image segmentation.

公开(公告)号：US11720647B2

公开(公告)日：2023-08-08

申请号：US16999665

申请日：2020-08-21

Applicant: GE Precision Healthcare LLC

Inventor： Ravi Soni ,  Tao Tan ,  Gopal B. Avinash ,  Dibyajyoti Pati ,  Hans Krupakar ,  Venkata Ratnam Saripalli

IPC: G06F18/214 ,  G06N20/00 ,  G06F18/21 ,  G06N3/08 ,  G06N20/10 ,  G06V10/774 ,  G16H30/40 ,  G06T11/00

CPC classification number: G06F18/2148 ,  G06F18/214 ,  G06F18/2163 ,  G06N3/08 ,  G06N20/00 ,  G06N20/10 ,  G06T11/00 ,  G06V10/774 ,  G16H30/40 ,  G06V2201/03

Abstract: Systems and techniques that facilitate synthetic training data generation for improved machine learning generalizability are provided. In various embodiments, an element augmentation component can generate a set of preliminary annotated training images based on an annotated source image. In various aspects, a preliminary annotated training image can be formed by inserting at least one element of interest or at least one background element into the annotated source image. In various instances, a modality augmentation component can generate a set of intermediate annotated training images based on the set of preliminary annotated training images. In various cases, an intermediate annotated training image can be formed by varying at least one modality-based characteristic of a preliminary annotated training image. In various aspects, a geometry augmentation component can generate a set of deployable annotated training images based on the set of intermediate annotated training images. In various instances, a deployable annotated training image can be formed by varying at least one geometric characteristic of an intermediate annotated training image. In various embodiments, a training component can train a machine learning model on the set of deployable annotated training images.

公开(公告)号：US20230041575A1

公开(公告)日：2023-02-09

申请号：US17392431

申请日：2021-08-03

Applicant: GE Precision Healthcare LLC

Inventor： Tao Tan ,  Buer Qi ,  Dejun Wang ,  Gopal B. Avinash ,  Gireesha Chinthamani Rao ,  German Guillermo Vera Gonzalez ,  Lehel Ferenczi

IPC: G06T11/00 ,  G06N20/00

Abstract: Systems/techniques that facilitate AI-based region-of-interest masks for improved data reconstructions are provided. In various embodiments, a system can access a set of two-dimensional medical scan projections. In various aspects, the system can generate a set of two-dimensional region-of-interest masks respectively corresponding to the set of two-dimensional medical scan projections. In various instances, the system can generate a region-of-interest visualization based on the set of two-dimensional region-of-interest masks and the set of two-dimensional medical scan projections. In various cases, the system can generate the set of two-dimensional region-of-interest masks by executing a machine learning segmentation model on the set of two-dimensional medical scan projections.

公开(公告)号：US12274575B2

公开(公告)日：2025-04-15

申请号：US17965228

申请日：2022-10-13

Applicant: GE Precision Healthcare LLC

Inventor： Balázs P. Cziria ,  German Guillermo Vera Gonzalez ,  Tao Tan ,  Pál Tegzes ,  Justin M. Wanek ,  Gopal B. Avinash ,  Zita Herczeg ,  Ravi Soni ,  Gireesha Chinthamani Rao

IPC: A61B6/00 ,  A61B6/46 ,  G01N23/04 ,  G06T5/50 ,  G06T5/73 ,  G16H30/40

Abstract: A dual energy x-ray imaging system and method of operation includes an artificial intelligence-based motion correction system to minimize the effects of motion artifacts in images produced by the imaging system. The motion correction system is trained to apply simulated motion to various objects of interest within the LE and HE projections in the training dataset to improve registration of the LE and HE projections. The motion correction system is also trained to enhance the correction of small motion artifacts using noise attenuation and subtraction image-based edge detection on the training dataset images reduce noise from the LE projection, consequently improving small motion artifact correction. The motion correction system additionally employs separate motion corrections for soft and bone tissue in forming subtraction soft tissue and bone tissue images, and includes a motion alarm to indicate when motion between LE and HE projections requires a retake of the projections.

公开(公告)号：US12229953B2

公开(公告)日：2025-02-18

申请号：US17951281

申请日：2022-09-23

Applicant: GE Precision Healthcare LLC

Inventor： Pal Tegzes ,  Zita Herczeg ,  Tao Tan ,  Balazs Peter Cziria ,  Alec Joseph Baenen ,  Gireesha Chintharnani Rao ,  Lehel Ferenczi ,  Gopal Biligeri Avinash ,  Zoltan Kiss ,  Hongxu Yang ,  Beth Ann Heckel

IPC: G06T7/00 ,  G16H50/20

Abstract: An image processing system is provided. The image processing system includes a display, a processor, and a memory. The memory stores processor-executable code that when executed by the processor causes receiving an image of a region of interest of a patient with an enteric tube or line disposed within the region of interest, detecting the medical tube or line within the image, generating a combined image by superimposing graphical markers on the image that indicate placement or misplacement of the enteric tube or line, and displaying the combined image on a display. In further aspects, a classification of the enteric tube or line (e.g., correctly placed tube present, malpositioned tube present, and so forth) may be determined and communicated to one or more clinicians. Additionally, the outputs of the image processing system may also be provided to facilitate triage of patients, helping prioritize which tube placements require further attention and in what order.