Segmentation of the thoracolumbar fascia in ultrasound imaging: a deep learning approach.

Journal: BMC medical imaging
PMID: 40375198

Abstract

BACKGROUND: Only in recent years it has been demonstrated that the thoracolumbar fascia is involved in low back pain (LBP), thus highlighting its implications for treatments. Furthermore, an easily accessible and non-invasive way to investigate the fascia in real time is the ultrasound examination, which to be reliable as is, it must overcome the challenges related to the configuration of the machine and the experience of the operator. Therefore, the lack of a clear understanding of the fascial system combined with the penalty related to the setting of the ultrasound acquisition has generated a gap that makes its effective evaluation difficult during clinical routine. The aim of the present work is to fill this gap by investigating the effectiveness of using a deep learning approach to segment the thoracolumbar fascia from ultrasound imaging.

Authors

  • Lorenza Bonaldi
    Department of Civil, Environmental and Architectural Engineering, University of Padova, 35131, Padova, Italy.
  • Carmelo Pirri
    Center for Mechanics of Biological Materials, University of Padova, 35131, Padova, Italy.
  • Federico Giordani
    Neurological Rehabilitation Centre, Villa Rosa, Trento, Italy.
  • Chiara Giulia Fontanella
    Center for Mechanics of Biological Materials, University of Padova, 35131, Padova, Italy. chiaragiulia.fontanella@unipd.it.
  • Carla Stecco
    Center for Mechanics of Biological Materials, University of Padova, 35131, Padova, Italy.
  • Francesca Uccheddu
    Department of Industrial Engineering, Padova University, Padova, Italy.

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