公开(公告)号：US12070354B2

公开(公告)日：2024-08-27

申请号：US16162285

申请日：2018-10-16

Applicant: General Electric Company

Inventor： Christian Fritz Perrey ,  Stephan Anzengruber

IPC: A61B8/00 ,  A61B8/08 ,  A61B34/20 ,  G06N20/00

CPC classification number: A61B8/4254 ,  A61B8/5207 ,  A61B34/20 ,  A61B8/483 ,  A61B2034/2055 ,  A61B2034/2063 ,  G06N20/00

Abstract: Various methods and systems are provided for automatically detecting scan planes of anatomical structures and providing guidance for obtaining target views during an imaging procedure. As one example, a method includes outputting to a user, instructions for navigating a medical imaging probe from a current scan position to a next scan position for obtaining a desired scan plane of a target anatomical structure based on received image data of the target anatomical structure and position data, the position data obtained from a position sensor on the probe, during receiving the image data.

公开(公告)号：US20200315569A1

公开(公告)日：2020-10-08

申请号：US16372446

申请日：2019-04-02

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Suvadip Mukherjee ,  Rahul Venkataramani ,  Anuprriya Gogna ,  Stephan Anzengruber

IPC: A61B8/08 ,  A61B5/00

Abstract: A method for determining a nervous system condition includes obtaining an estimate of a first scan plane among a plurality of planes of a maternal subject using a first deep learning network during a guided scanning procedure. The method further includes receiving a three-dimensional (3D) ultrasound volume corresponding to the initial estimate and determining an optimal first scan plane from the first deep learning network. The method further includes determining at least one of a second scan plane, a third scan plane and a fourth scan plane among the plurality of planes, based on the optimal first scan plane and at least one of a clinical constraint corresponding to the plurality of planes using a second deep learning network. The method includes determining a biometric parameter corresponding to nervous system based on at least one of the plurality of planes using a third deep learning network.