Literature DB >> 27720522

Fully Automated Muscle Ultrasound Analysis (MUSA): Robust and Accurate Muscle Thickness Measurement.

Cristina Caresio1, Massimo Salvi1, Filippo Molinari2, Kristen M Meiburger1, Marco Alessandro Minetto3.   

Abstract

Musculoskeletal ultrasound imaging allows non-invasive measurement of skeletal muscle thickness. Current techniques generally suffer from manual operator dependency, while all the computer-aided approaches are limited to be semi-automatic or specifically optimized for a single muscle. The aim of this study was to develop and validate a fully automatic method, named MUSA (Muscle UltraSound Analysis), for measurement of muscle thickness on longitudinal ultrasound images acquired from different skeletal muscles. The MUSA algorithm was tested on a database of 200 B-mode ultrasound images of rectus femoris, vastus lateralis, tibialis anterior and medial gastrocnemius. The automatic muscle thickness measurements were compared to the manual measurements obtained by three operators. The MUSA algorithm achieved a 100% segmentation success rate, with mean differences between the automatic and manual measurements in the range of 0.06-0.45 mm. MUSA performance was statistically equal to the operators and its measurement accuracy was independent of the muscle thickness value. Copyright Â
© 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Automatic muscle thickness measurements; Muscle ultrasonography; Musculoskeletal ultrasound

Mesh:

Year:  2016        PMID: 27720522     DOI: 10.1016/j.ultrasmedbio.2016.08.032

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  7 in total

1.  Lower Limb Motion Estimation Using Ultrasound Imaging: A Framework for Assistive Device Control.

Authors:  Mohammad Hassan Jahanandish; Nicholas P Fey; Kenneth Hoyt
Journal:  IEEE J Biomed Health Inform       Date:  2019-01-09       Impact factor: 5.772

2.  Ultrasound-based detection of glucocorticoid-induced impairments of muscle mass and structure in Cushing's disease.

Authors:  M A Minetto; C Caresio; M Salvi; V D'Angelo; N E Gorji; F Molinari; G Arnaldi; S Kesari; E Arvat
Journal:  J Endocrinol Invest       Date:  2018-11-15       Impact factor: 4.256

3.  Automatic Extraction of Muscle Parameters with Attention UNet in Ultrasonography.

Authors:  Sofoklis Katakis; Nikolaos Barotsis; Alexandros Kakotaritis; George Economou; Elias Panagiotopoulos; George Panayiotakis
Journal:  Sensors (Basel)       Date:  2022-07-13       Impact factor: 3.847

4.  Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods.

Authors:  Philippe Burlina; Seth Billings; Neil Joshi; Jemima Albayda
Journal:  PLoS One       Date:  2017-08-30       Impact factor: 3.240

5.  Sarcopenia Detection System Using RGB-D Camera and Ultrasound Probe: System Development and Preclinical In-Vitro Test.

Authors:  Yeoun-Jae Kim; Seongjun Kim; Jaesoon Choi
Journal:  Sensors (Basel)       Date:  2020-08-09       Impact factor: 3.576

6.  Relationship between sonography of sternocleidomastoid muscle and cervical passive range of motion in infants with congenital muscular torticollis.

Authors:  Chu-Hsu Lin; Hung-Chih Hsu; Yu-Jen Hou; Kai-Hua Chen; Shang-Hong Lai; Wen-Ming Chang
Journal:  Biomed J       Date:  2019-01-04       Impact factor: 4.910

7.  Simple Muscle Architecture Analysis (SMA): An ImageJ macro tool to automate measurements in B-mode ultrasound scans.

Authors:  Olivier R Seynnes; Neil J Cronin
Journal:  PLoS One       Date:  2020-02-12       Impact factor: 3.240

  7 in total

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