An intensity-based self-supervised domain adaptation method for intervertebral disc segmentation in magnetic resonance imaging
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- dc.contributor.author Fiorentino, Maria Chiara
- dc.contributor.author Pla Villani, Francesca
- dc.contributor.author Benito-Herce, Rafael
- dc.contributor.author González Ballester, Miguel Ángel, 1973-
- dc.contributor.author Mancini, Adriano
- dc.contributor.author López-Linares, Karen
- dc.date.accessioned 2025-06-10T06:52:17Z
- dc.date.available 2025-06-10T06:52:17Z
- dc.date.issued 2024
- dc.description.abstract Methods: The study introduces an innovative method using intensity-based self-supervised learning for IVD segmentation in MRI scans. This approach is particularly suited for IVD segmentations due to its ability to effectively capture the subtle intensity variations that are characteristic of spinal structures. The model, a dual-task system, simultaneously segments IVDs and predicts intensity transformations. This intensity-focused method has the advantages of being easy to train and computationally light, making it highly practical in diverse clinical settings. Trained on unlabeled data from multiple domains, the model learns domain-invariant features, adeptly handling intensity variations across different MRI devices and protocols. Results: Testing on three public datasets showed that this model outperforms baseline models trained on single-domain data. It handles domain shifts and achieves higher accuracy in IVD segmentation. Conclusions: This study demonstrates the potential of intensity-based self-supervised domain adaptation for IVD segmentation. It suggests new directions for research in enhancing generalizability across datasets with domain shifts, which can be applied to other medical imaging fields.
- dc.format.mimetype application/pdf
- dc.identifier.citation Fiorentino MC, Pla Villani F, Benito Herce R, Gónzalez-Ballester MA, Mancini A, López-Linares Román K. An intensity-based self-supervised domain adaptation method for intervertebral disc segmentation in magnetic resonance imaging. Int J Comput Assist Radiol Surg. 2024;19:1753-61. DOI: 10.1007/s11548-024-03219-7
- dc.identifier.doi http://dx.doi.org/10.1007/s11548-024-03219-7
- dc.identifier.issn 1861-6410
- dc.identifier.uri http://hdl.handle.net/10230/70648
- dc.language.iso eng
- dc.publisher Nature Research
- dc.relation.ispartof International Journal of Computer Assisted Radiology and Surgery. 2024;19:1753-61
- dc.rights This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Deep learning
- dc.subject.keyword Domain adaptation
- dc.subject.keyword Intervertebral disc
- dc.subject.keyword Magnetic resonance imaging
- dc.title An intensity-based self-supervised domain adaptation method for intervertebral disc segmentation in magnetic resonance imaging
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion