Skeletal Muscle Ultrasound Changes in Patients With Early Stroke

2021 ◽  
Vol 102 (10) ◽  
pp. e26
Author(s):  
Matthew Rong Jie Tay ◽  
Jong Moon Kim ◽  
Deshan Kumar Rajeswaran ◽  
Shuen-Loong Tham ◽  
Wen Li Lui ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Muscle Ultrasound ◽  
Early Stroke

Skeletal Muscle Ultrasound: Correlation Between Fibrous Tissue and Echo Intensity

2009 ◽  
Vol 35 (3) ◽  
pp. 443-446 ◽  
Author(s):  
Sigrid Pillen ◽  
Ramon O. Tak ◽  
Machiel J. Zwarts ◽  
Martin M.Y. Lammens ◽  
Kiek N. Verrijp ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Fibrous Tissue ◽  
Echo Intensity ◽  
Muscle Ultrasound

Quantitative skeletal muscle ultrasound: Diagnostic value in childhood neuromuscular disease

2007 ◽  
Vol 17 (7) ◽  
pp. 509-516 ◽  
Author(s):  
S. Pillen ◽  
A. Verrips ◽  
N. van Alfen ◽  
I.M.P. Arts ◽  
L.T.L. Sie ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Neuromuscular Disease ◽  
Diagnostic Value ◽  
Muscle Ultrasound

scholarly journals Reliability of skeletal muscle ultrasound in critically ill trauma patients

2019 ◽  
Vol 31 (4) ◽  
Author(s):  
Luciana Vieira ◽  
Lara Patrícia Bastos Rocha ◽  
Sunita Mathur ◽  
Larissa Santana ◽  
Priscilla Flávia de Melo ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Critically Ill ◽  
Trauma Patients ◽  
Muscle Ultrasound

scholarly journals Skeletal muscle ultrasound

2011 ◽  
Vol 33 (10) ◽  
pp. 1016-1024 ◽  
Author(s):  
Sigrid Pillen ◽  
Nens van Alfen
Keyword(s):  
Skeletal Muscle ◽  
Muscle Ultrasound

Evaluation of Skeletal Muscle Ultrasound For The Assessment of Body Composition In Healthy Controls And Patients With Heart Failure

2020 ◽  
Vol 26 (10) ◽  
pp. S42
Author(s):  
Laura Telfer ◽  
Joronia Chery ◽  
Matthew Lawrence ◽  
Abbey Haynes ◽  
Amanda R. Vest
Keyword(s):  
Heart Failure ◽  
Skeletal Muscle ◽  
Body Composition ◽  
Healthy Controls ◽  
Patients With Heart Failure ◽  
Muscle Ultrasound

scholarly journals Qualitative and quantitative skeletal muscle ultrasound in late-onset acid maltase deficiency

2011 ◽  
Vol 44 (3) ◽  
pp. 418-423 ◽  
Author(s):  
Craig M. Zaidman ◽  
Elizabeth C. Malkus ◽  
Catherine Siener ◽  
Julaine Florence ◽  
Alan Pestronk ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Late Onset ◽  
Acid Maltase Deficiency ◽  
Qualitative And Quantitative ◽  
Acid Maltase ◽  
Muscle Ultrasound

PW2-5 Advances in skeletal muscle ultrasound

2010 ◽  
Vol 121 ◽  
pp. S86
Author(s):  
S. Pillen ◽  
N. Van Alfen
Keyword(s):  
Skeletal Muscle ◽  
Muscle Ultrasound

scholarly journals A Review of the Challenges in Deep Learning for Skeletal and Smooth Muscle Ultrasound Images

2021 ◽  
Vol 11 (9) ◽  
pp. 4021
Author(s):  
Peter Ardhianto ◽  
Jen-Yung Tsai ◽  
Chih-Yang Lin ◽  
Ben-Yi Liau ◽  
Yih-Kuen Jan ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Smooth Muscle ◽  
Deep Learning ◽  
Image Quality ◽  
Smooth Muscles ◽  
Muscle Thickness ◽  
Learning Performance ◽  
Ultrasound Images ◽  
Muscle Segmentation ◽  
Muscle Ultrasound

Deep learning has aided in the improvement of diagnosis identification, evaluation, and the interpretation of muscle ultrasound images, which may benefit clinical personnel. Muscle ultrasound images presents challenges such as low image quality due to noise, insufficient data, and different characteristics between skeletal and smooth muscles that can affect the effectiveness of deep learning results. From 2018 to 2020, deep learning has the improved solutions used to overcome these challenges; however, deep learning solutions for ultrasound images have not been compared to the conditions and strategies used to comprehend the current state of knowledge for handling skeletal and smooth muscle ultrasound images. This study aims to look at the challenges and trends of deep learning performance, especially in regard to overcoming muscle ultrasound image problems such as low image quality, muscle movement in skeletal muscles, and muscle thickness in smooth muscles. Skeletal muscle segmentation presents difficulties due to the regular movement of muscles and resulting noise, recording data through skipped connections, and modified layers required for upsampling. In skeletal muscle classification, the problems faced are area-specific, thus making a cropping strategy useful. Furthermore, there is no need to add additional layer modifications for smooth muscle segmentation as muscle thickness is the main problem in such cases.

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