Siemens Healthineers Ag

Siemens Medical Solutions Usa, Inc.

Abdoul Aziz Amadou

Rui Liao

Yue Zhang

Tommaso Mansi

Sasa Grbic

Ankur Kapoor

Viorel Mihalef

Zhoubing Xu

Ingmar Voigt

Helene C Houle

Jin-hyeong Park

Bimba Rao

Young-Ho Kim

Sebastien Piat

Venkatesh Narasimha Murthy

Bo Liu

Jarrod Collins

Matthias John

Xianjun S Zheng

Systems and methods for automatically navigating a catheter in a patient are provided. An image of a current view of a catheter in a patient is received. A set of actions of a robotic navigation system for navigating the catheter from the current view towards a target view is determined using a machine learning based network. The catheter is automatically navigated in the patient from the current view towards the target view using the robotic navigation system based on the set of actions.

Smart image navigation for intracardiac echocardiography

A tip of a vascular catheter is detected in a sequence of vessel image frames obtained using x-rays. Within the vessel image frames, a proximal point corresponding to a vessel ostium is determined. The sequence of vessel image frames are cropped to an image area surrounding the proximal point to generate a cropped sequence of cropped vessel image frames. Within cropped vessel image frames not indicative of a contrast medium, the tip of the vascular catheter is detected, and the detected tip of the vascular catheter is tracked.

Method and system for vascular catheter tip detection in medical images

Systems and methods for tracking a location of an object of interest through a sequence of medical images are provided. First and second input medical images of a patient are received. The first input medical image comprises an annotation of a location of an object of interest. Features are extracted from the first and the second input medical images. A location of the object of interest in the second input medical image is determined using a machine learning based location predictor network based on the annotation of the location of the object of interest in the first input medical image and the extracted features from the first and the second input medical images. The location of the object of interest in the second input medical image is output. The machine learning based location predictor network is trained based on a comparison between 1) locations of a particular object in a sequence of training images determined during a forward tracking of the particular object through the sequence of training images and 2) locations of the particular object determined during a backward tracking of the particular object through the sequence of training images.

Semi-supervised tracking in medical images with cycle tracking

A method, apparatus, and computer readable storage medium are provided herein for constructing a representation of an annular structure associated with an anatomical object. The method includes receiving three-dimensional image data of the anatomical object and detecting at least a first landmark point and a second landmark point on the annular structure. A plane positioned between the first landmark point and the second landmark point, and oriented in accordance with a predefined angular relationship to a line connecting the first landmark point and the second landmark point is determined. A third landmark point on the annular structure which lies in the plane is also detected and the representation of the annular structure is generated using at least the first landmark point, the second landmark point, and the third landmark point. The representation is then outputted.

Annular structure representation

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London, United Kingdom

Abdoul Aziz Amadou