Deep Learning for Sensorless 3D Freehand Ultrasound Imaging

2017 ◽  
pp. 628-636 ◽  
Author(s):  
Raphael Prevost ◽  
Mehrdad Salehi ◽  
Julian Sprung ◽  
Alexander Ladikos ◽  
Robert Bauer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ultrasound Imaging ◽  
Freehand Ultrasound

Erratum to: Deep Learning for Sensorless 3D Freehand Ultrasound Imaging

2017 ◽  
pp. E1-E1
Author(s):  
Raphael Prevost ◽  
Mehrdad Salehi ◽  
Julian Sprung ◽  
Alexander Ladikos ◽  
Robert Bauer ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ultrasound Imaging ◽  
Freehand Ultrasound

scholarly journals Reverse Scan Conversion and Efficient Deep Learning Network Architecture for Ultrasound Imaging on a Mobile Device

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2629
Author(s):  
Kunkyu Lee ◽  
Min Kim ◽  
Changhyun Lim ◽  
Tai-Kyong Song
Keyword(s):  
Deep Learning ◽  
Ultrasound Imaging ◽  
Mobile Device ◽  
Network Architecture ◽  
Point Of Care ◽  
Ultrasound Image ◽  
Training Dataset ◽  
Point Of Care Ultrasound ◽  
Reverse Scan ◽  
Scan Conversion

Point-of-care ultrasound (POCUS), realized by recent developments in portable ultrasound imaging systems for prompt diagnosis and treatment, has become a major tool in accidents or emergencies. Concomitantly, the number of untrained/unskilled staff not familiar with the operation of the ultrasound system for diagnosis is increasing. By providing an imaging guide to assist clinical decisions and support diagnosis, the risk brought by inexperienced users can be managed. Recently, deep learning has been employed to guide users in ultrasound scanning and diagnosis. However, in a cloud-based ultrasonic artificial intelligence system, the use of POCUS is limited due to information security, network integrity, and significant energy consumption. To address this, we propose (1) a structure that simultaneously provides ultrasound imaging and a mobile device-based ultrasound image guide using deep learning, and (2) a reverse scan conversion (RSC) method for building an ultrasound training dataset to increase the accuracy of the deep learning model. Experimental results show that the proposed structure can achieve ultrasound imaging and deep learning simultaneously at a maximum rate of 42.9 frames per second, and that the RSC method improves the image classification accuracy by more than 3%.

Deep Learning Based Angular Compounding for Accelerated Plane Wave Ultrasound Imaging

2021 ◽  
Author(s):  
Hannah Strohm ◽  
Sven Rothlubbers ◽  
Jurgen Jenne ◽  
Matthias Gunther
Keyword(s):  
Deep Learning ◽  
Plane Wave ◽  
Ultrasound Imaging

Using game controller as position tracking sensor for 3D freehand ultrasound imaging

2019 ◽  
Vol 58 (5) ◽  
pp. 889-902
Author(s):  
Vei Siang Chan ◽  
Farhan Mohamed ◽  
Yusman Azimi Yusoff ◽  
Dyah Ekashanti Octorina Dewi ◽  
Alfiera Anuar ◽  
...  
Keyword(s):  
Ultrasound Imaging ◽  
Position Tracking ◽  
Game Controller ◽  
Freehand Ultrasound

scholarly journals Deep Learning in Ultrasound Imaging

2020 ◽  
Vol 108 (1) ◽  
pp. 11-29 ◽  
Author(s):  
Ruud J. G. van Sloun ◽  
Regev Cohen ◽  
Yonina C. Eldar
Keyword(s):  
Deep Learning ◽  
Ultrasound Imaging

Volume quantification by fuzzy logic modelling in freehand ultrasound imaging

Ultrasonics ◽  
2009 ◽  
Vol 49 (8) ◽  
pp. 646-652 ◽  
Author(s):  
N. Betrouni ◽  
R. Lopes ◽  
N. Makni ◽  
A.S. Dewalle ◽  
M. Vermandel ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Ultrasound Imaging ◽  
Freehand Ultrasound
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