Kinect-based ultrasound probe pose estimation to build an affordable knee ultrasound learning database

Pose Estimation of 2D Ultrasound Probe from Ultrasound Image Sequences Using CNN and RNN

10.1007/978-3-030-87583-1_10 ◽

2021 ◽

pp. 96-105

Author(s):

Kanta Miura ◽

Koichi Ito ◽

Takafumi Aoki ◽

Jun Ohmiya ◽

Satoshi Kondo

Keyword(s):

Pose Estimation ◽

Ultrasound Image ◽

Image Sequences ◽

Ultrasound Probe ◽

2D Ultrasound

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Three-Dimensional Ultrasound Probe Pose Estimation from Single-Perspective X-Rays for Image-Guided Interventions

Lecture Notes in Computer Science - Medical Imaging and Augmented Reality ◽

10.1007/978-3-642-15699-1_36 ◽

2010 ◽

pp. 344-352 ◽

Cited By ~ 4

Author(s):

Pencilla Lang ◽

Petar Seslija ◽

Damiaan F. Habets ◽

Michael W. A. Chu ◽

David W. Holdsworth ◽

...

Keyword(s):

Pose Estimation ◽

Three Dimensional ◽

X Rays ◽

Ultrasound Probe ◽

Image Guided

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Model-Based Human Pose Estimation and Its Analysis Using Hausdorff Matching

TECHART Journal of Arts and Imaging Science ◽

10.15323/techart.2015.05.2.2.51 ◽

2015 ◽

Vol 2 (2) ◽

pp. 51 ◽

Cited By ~ 1

Author(s):

Vivek Maik ◽

Jinho Park ◽

Daehee Kim ◽

Joonki Paik

Keyword(s):

Pose Estimation ◽

Human Pose Estimation ◽

Model Based ◽

Its Analysis ◽

Human Pose

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Pose Estimation for Non-cooperative Spacecraft based on Deep Learning

2020 39th Chinese Control Conference (CCC) ◽

10.23919/ccc50068.2020.9189253 ◽

2020 ◽

Author(s):

Wenxiu Huan ◽

Mingmin Liu ◽

Qinglei Hu

Keyword(s):

Deep Learning ◽

Pose Estimation

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Case Report: FAST Ultrasound Interpretation in Trauma Resuscitation

POCUS Journal ◽

10.24908/pocus.v1i3.13263 ◽

2016 ◽

Vol 1 (3) ◽

pp. 13-14

Author(s):

Stuart Douglas, PGY4 ◽

Joseph Newbigging, MD ◽

David Robertson, MD

Keyword(s):

Case Report ◽

Negative Predictive Value ◽

Predictive Value ◽

Life Support ◽

Advanced Trauma Life Support ◽

Trauma Patients ◽

Ultrasound Probe ◽

Trauma Resuscitation ◽

Highly Sensitive ◽

Pouch Of Douglas

FAST Background: Focused Assessment with Sonography for Trauma (FAST) is an integral adjunct to primary survey in trauma patients (1-4) and is incorporated into Advanced Trauma Life Support (ATLS) algorithms (4). A collection of four discrete ultrasound probe examinations (pericardial sac, hepatorenal fossa (Morison’s pouch), splenorenal fossa, and pelvis/pouch of Douglas), it has been shown to be highly sensitive for detection of as little as 100cm3 of intraabdominal fluid (4,5), with a sensitivity quoted between 60-98%, specificity of 84-98%, and negative predictive value of 97-99% (3).

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3D Object Recognition and Relative Localization using a 3D sensor Embedded on a Mobile Robot

10.31237/osf.io/p4thn ◽

2020 ◽

Author(s):

Gopi Krishna Erabati

Keyword(s):

Pose Estimation ◽

Autonomous Systems ◽

Microsoft Kinect ◽

Object Localization ◽

Initial Alignment ◽

Relative Localization ◽

3D Data ◽

Industry Automation ◽

Ofthe Object ◽

3D Descriptors

The technology in current research scenario is marching towards automation forhigher productivity with accurate and precise product development. Vision andRobotics are domains which work to create autonomous systems and are the keytechnology in quest for mass productivity. The automation in an industry canbe achieved by detecting interactive objects and estimating the pose to manipulatethem. Therefore the object localization ( i.e., pose) includes position andorientation of object, has profound ?significance. The application of object poseestimation varies from industry automation to entertainment industry and fromhealth care to surveillance. The objective of pose estimation of objects is verysigni?cant in many cases, like in order for the robots to manipulate the objects,for accurate rendering of Augmented Reality (AR) among others.This thesis tries to solve the issue of object pose estimation using 3D dataof scene acquired from 3D sensors (e.g. Kinect, Orbec Astra Pro among others).The 3D data has an advantage of independence from object texture and invarianceto illumination. The proposal is divided into two phases : An o?ine phasewhere the 3D model template of the object ( for estimation of pose) is built usingIterative Closest Point (ICP) algorithm. And an online phase where the pose ofthe object is estimated by aligning the scene to the model using ICP, providedwith an initial alignment using 3D descriptors (like Fast Point Feature Transform(FPFH)).The approach we develop is to be integrated on two di?erent platforms :1)Humanoid robot `Pyrene' which has Orbec Astra Pro 3D sensor for data acquisition,and 2)Unmanned Aerial Vehicle (UAV) which has Intel Realsense Euclidon it. The datasets of objects (like electric drill, brick, a small cylinder, cake box)are acquired using Microsoft Kinect, Orbec Astra Pro and Intel RealSense Euclidsensors to test the performance of this technique. The objects which are used totest this approach are the ones which are used by robot. This technique is testedin two scenarios, fi?rstly, when the object is on the table and secondly when theobject is held in hand by a person. The range of objects from the sensor is 0.6to 1.6m. This technique could handle occlusions of the object by hand (when wehold the object), as ICP can work even if partial object is visible in the scene.

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