Segmentation of the thoracolumbar fascia in ultrasound imaging: a deep learning approach
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 exami...
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BMC
2025-05-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-025-01720-2 |
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| author | Lorenza Bonaldi Carmelo Pirri Federico Giordani Chiara Giulia Fontanella Carla Stecco Francesca Uccheddu |
| author_facet | Lorenza Bonaldi Carmelo Pirri Federico Giordani Chiara Giulia Fontanella Carla Stecco Francesca Uccheddu |
| author_sort | Lorenza Bonaldi |
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| description | 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. Methods A total of 538 ultrasound images of the thoracolumbar fascia of LBP subjects were finally used to train and test a deep learning network. An additional test set (so-called Test set 2) was collected from another center, operator, machine manufacturer, patient cohort, and protocol to improve the generalizability of the study. Results A U-Net-based architecture was demonstrated to be able to segment these structures with a final training accuracy of 0.99 and a validation accuracy of 0.91. The accuracy of the prediction computed on a test set (87 images not included in the training set) reached the 0.94, with a mean intersection over union index of 0.82 and a Dice-score of 0.76. These latter metrics were outperformed by those in Test set 2. The validity of the predictions was also verified and confirmed by two expert clinicians. Conclusions Automatic identification of the thoracolumbar fascia has shown promising results to thoroughly investigate its alteration and target a personalized rehabilitation intervention based on each patient-specific scenario. |
| format | Article |
| id | doaj-art-c0a8f6210fa74ee78f3f7ef196dd9db7 |
| institution | OA Journals |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-c0a8f6210fa74ee78f3f7ef196dd9db72025-08-20T02:25:12ZengBMCBMC Medical Imaging1471-23422025-05-012511710.1186/s12880-025-01720-2Segmentation of the thoracolumbar fascia in ultrasound imaging: a deep learning approachLorenza Bonaldi0Carmelo Pirri1Federico Giordani2Chiara Giulia Fontanella3Carla Stecco4Francesca Uccheddu5Department of Civil, Environmental and Architectural Engineering, University of PadovaCenter for Mechanics of Biological Materials, University of PadovaNeurological Rehabilitation CentreCenter for Mechanics of Biological Materials, University of PadovaCenter for Mechanics of Biological Materials, University of PadovaCenter for Mechanics of Biological Materials, University of PadovaAbstract 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. Methods A total of 538 ultrasound images of the thoracolumbar fascia of LBP subjects were finally used to train and test a deep learning network. An additional test set (so-called Test set 2) was collected from another center, operator, machine manufacturer, patient cohort, and protocol to improve the generalizability of the study. Results A U-Net-based architecture was demonstrated to be able to segment these structures with a final training accuracy of 0.99 and a validation accuracy of 0.91. The accuracy of the prediction computed on a test set (87 images not included in the training set) reached the 0.94, with a mean intersection over union index of 0.82 and a Dice-score of 0.76. These latter metrics were outperformed by those in Test set 2. The validity of the predictions was also verified and confirmed by two expert clinicians. Conclusions Automatic identification of the thoracolumbar fascia has shown promising results to thoroughly investigate its alteration and target a personalized rehabilitation intervention based on each patient-specific scenario.https://doi.org/10.1186/s12880-025-01720-2SegmentationDeep learningDeep fasciaThoracolumbarLow back pain |
| spellingShingle | Lorenza Bonaldi Carmelo Pirri Federico Giordani Chiara Giulia Fontanella Carla Stecco Francesca Uccheddu Segmentation of the thoracolumbar fascia in ultrasound imaging: a deep learning approach BMC Medical Imaging Segmentation Deep learning Deep fascia Thoracolumbar Low back pain |
| title | Segmentation of the thoracolumbar fascia in ultrasound imaging: a deep learning approach |
| title_full | Segmentation of the thoracolumbar fascia in ultrasound imaging: a deep learning approach |
| title_fullStr | Segmentation of the thoracolumbar fascia in ultrasound imaging: a deep learning approach |
| title_full_unstemmed | Segmentation of the thoracolumbar fascia in ultrasound imaging: a deep learning approach |
| title_short | Segmentation of the thoracolumbar fascia in ultrasound imaging: a deep learning approach |
| title_sort | segmentation of the thoracolumbar fascia in ultrasound imaging a deep learning approach |
| topic | Segmentation Deep learning Deep fascia Thoracolumbar Low back pain |
| url | https://doi.org/10.1186/s12880-025-01720-2 |
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