Transverse Muscle Ultrasound Analysis (TRAMA): Robust and Accurate Segmentation of Muscle Cross-Sectional Area.


Journal

Ultrasound in medicine & biology
ISSN: 1879-291X
Titre abrégé: Ultrasound Med Biol
Pays: England
ID NLM: 0410553

Informations de publication

Date de publication:
03 2019
Historique:
received: 08 05 2018
revised: 10 11 2018
accepted: 29 11 2018
pubmed: 15 1 2019
medline: 18 12 2019
entrez: 15 1 2019
Statut: ppublish

Résumé

Ultrasonography allows non-invasive and real time-measurement of the visible cross-sectional area (CSA) of muscles, which is a clinically relevant descriptor of muscle size. The aim of this study was to develop and validate a fully automatic method called transverse muscle ultrasound analysis (TRAMA) for segmentation of the muscle in B-mode transverse ultrasound images and measurement of muscle CSA. TRAMA was tested on a database of 200 ultrasound images of the rectus femoris, vastus lateralis, tibialis anterior and medial gastrocnemius muscles. The automatic CSA measurements were compared with manual measurements obtained by two operators. There were no statistical differences between the automatic and manual measurements of CSA of the four muscles, and TRAMA performance was comparable to intra-operator variability in terms of the Dice similarity coefficient and Hausdorff distance between the automatic and manual segmentations. Compared with manual segmentation, the Dice similarity coefficient for the proposed method was always higher than 93%; the Hausdorff distance never exceeded 4 mm, and the maximum absolute error was 62 mm

Identifiants

pubmed: 30638696
pii: S0301-5629(18)30523-4
doi: 10.1016/j.ultrasmedbio.2018.11.012
pii:
doi:

Types de publication

Journal Article Validation Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

672-683

Informations de copyright

Copyright © 2018 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Auteurs

Massimo Salvi (M)

Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy. Electronic address: massimo.salvi@polito.it.

Cristina Caresio (C)

Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.

Kristen M Meiburger (KM)

Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.

Bruno De Santi (B)

Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.

Filippo Molinari (F)

Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.

Marco Alessandro Minetto (MA)

Division of Physical Medicine and Rehabilitation, Department of Surgical Sciences, University of Turin, Turin, Italy.

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Classifications MeSH