公开(公告)号：US12249067B2

公开(公告)日：2025-03-11

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

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：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：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.

公开(公告)号：US20230342427A1

公开(公告)日：2023-10-26

申请号：US18343266

申请日：2023-06-28

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

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：US20230341914A1

公开(公告)日：2023-10-26

申请号：US18343258

申请日：2023-06-28

Applicant: GE Precision Healthcare LLC

Inventor： Chitresh Bhushan ,  Dattesh Dayanand Shanbhag ,  Rakesh Mullick

IPC: G06F1/26 ,  H03F3/45 ,  G05F1/46 ,  G06F13/16 ,  G06F13/42 ,  H03K5/1252

CPC classification number: G06F1/266 ,  H03F3/45475 ,  G05F1/46 ,  G06F13/1668 ,  G06F13/4282 ,  H03K5/1252 ,  G06F2213/0026

Abstract: Techniques are described for generating reformatted views of a three-dimensional (3D) anatomy scan using deep-learning estimated scan prescription masks. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a mask generation component that employs a pre-trained neural network model to generate masks for different anatomical landmarks depicted in one or more calibration images captured of an anatomical region of a patient. The computer executable components further comprise a reformatting component that reformats 3D image data captured of the anatomical region of the patient using the masks to generate different representations of the 3D image data that correspond to the different anatomical landmarks.

公开(公告)号：US20230298136A1

公开(公告)日：2023-09-21

申请号：US17654864

申请日：2022-03-15

Applicant: GE Precision Healthcare LLC

Inventor： Bipul Das ,  Rakesh Mullick ,  Deepa Anand ,  Sandeep Dutta ,  Uday Damodar Patil ,  Maud Bonnard

IPC: G06T3/60 ,  G06T7/73 ,  G06V10/82 ,  G06V10/774 ,  G16H50/20

CPC classification number: G06T3/60 ,  G06T7/73 ,  G06V10/82 ,  G06V10/774 ,  G16H50/20 ,  G06T2200/04 ,  G06V2201/031 ,  G06T2207/20084 ,  G06T2207/20081

Abstract: Systems/techniques that facilitate deep learning multi-planar reformatting of medical images are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can localize, via execution of a machine learning model, a set of landmarks depicted in the three-dimensional medical image, a set of principal anatomical planes depicted in the three-dimensional medical image, and a set of organs depicted in the three-dimensional medical image. In various instances, the system can determine an anatomical orientation exhibited by the three-dimensional medical image, based on the set of landmarks, the set of principal anatomical planes, or the set of organs. In various cases, the system can rotate the three-dimensional medical image, such that the anatomical orientation now matches a predetermined anatomical orientation.

公开(公告)号：US20220101048A1

公开(公告)日：2022-03-31

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: G06K9/62 ,  G06T5/50 ,  G06T7/30 ,  G06N5/04 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50 ,  A61B6/03 ,  A61B6/00 ,  A61B5/055 ,  A61B5/00

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：US12272023B2

公开(公告)日：2025-04-08

申请号：US17654864

申请日：2022-03-15

Applicant: GE Precision Healthcare LLC

Inventor： Bipul Das ,  Rakesh Mullick ,  Deepa Anand ,  Sandeep Dutta ,  Uday Damodar Patil ,  Maud Bonnard

IPC: G06T3/60 ,  G06T7/73 ,  G06V10/774 ,  G06V10/82 ,  G16H50/20

Abstract: Systems/techniques that facilitate deep learning multi-planar reformatting of medical images are provided. In various embodiments, a system can access a three-dimensional medical image. In various aspects, the system can localize, via execution of a machine learning model, a set of landmarks depicted in the three-dimensional medical image, a set of principal anatomical planes depicted in the three-dimensional medical image, and a set of organs depicted in the three-dimensional medical image. In various instances, the system can determine an anatomical orientation exhibited by the three-dimensional medical image, based on the set of landmarks, the set of principal anatomical planes, or the set of organs. In various cases, the system can rotate the three-dimensional medical image, such that the anatomical orientation now matches a predetermined anatomical orientation.

公开(公告)号：US20250095642A1

公开(公告)日：2025-03-20

申请号：US18468593

申请日：2023-09-15

Applicant: GE Precision Healthcare LLC

Inventor： Soumya Ghose ,  Sanand Sasidharan ,  Sanghee Cho ,  Akshit Achara ,  Rakesh Mullick ,  Anuradha Kanamarlapudi ,  Fiona Ginty ,  Annamraju Ravi Bhardwaj ,  Sundararajan Mani ,  Brion Sarachan

IPC: G10L15/197 ,  G06N3/0455 ,  G06N3/09 ,  G10L15/06 ,  G10L15/16 ,  G10L15/22 ,  G16H10/60 ,  G16H50/20 ,  G16H70/20

Abstract: The current disclosure provides methods for an automated clinical recommendation system that generates clinical recommendations for patients of a health care system based on natural language queries submitted by care providers of the health care system. The natural language queries may be typed into a user interface (UI) of the clinical recommendation system, or submitted by voice via a microphone. The clinical recommendation system provides a clinically explainable disease state of a patient based on patient data included in a query, and recommends a next course of action (e.g., a treatment) based on clinical guidelines and population statistics, in a manner that reduces a current burden of clinicians in consulting digital clinical manuals via a series of time-consuming and cumbersome interactions with a graphical user interface (GUI) of the digital clinical manuals.

公开(公告)号：US11978137B2

公开(公告)日：2024-05-07

申请号：US18343258

申请日：2023-06-28

Applicant: GE Precision Healthcare LLC

Inventor： Chitresh Bhushan ,  Dattesh Dayanand Shanbhag ,  Rakesh Mullick

IPC: G06T11/00 ,  A61B5/00 ,  A61B5/055 ,  G05F1/46 ,  G06F1/26 ,  G06F13/16 ,  G06F13/42 ,  G06T7/00 ,  G06T7/11 ,  G06T7/80 ,  G06V10/25 ,  H03F3/45 ,  H03K5/1252

CPC classification number: G06T11/008 ,  A61B5/055 ,  A61B5/742 ,  G05F1/46 ,  G06F1/266 ,  G06F13/1668 ,  G06F13/4282 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/80 ,  G06V10/25 ,  H03F3/45475 ,  H03K5/1252 ,  G06F2213/0026 ,  G06T2207/10072 ,  G06T2207/20081 ,  G06T2207/30016

Abstract: Techniques are described for generating reformatted views of a three-dimensional (3D) anatomy scan using deep-learning estimated scan prescription masks. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a mask generation component that employs a pre-trained neural network model to generate masks for different anatomical landmarks depicted in one or more calibration images captured of an anatomical region of a patient. The computer executable components further comprise a reformatting component that reformats 3D image data captured of the anatomical region of the patient using the masks to generate different representations of the 3D image data that correspond to the different anatomical landmarks.