Deep learning for automated segmentation of central cartilage tumors on MRI
Automated segmentation may potentially increase the reliability and applicability of radiomics-based models. A deep learning architecture was proposed for automated segmentation of appendicular cartilage tumors on MRI. Deep learning proved excellent with a mean Dice Score of 0.828 in the external test cohort.
Authors
- Anna Corti (ORCID: https://orcid.org/0000-0001-9603-7825)
- Laura Ferrari (ORCID: https://orcid.org/0000-0003-2379-6227)
- Kirsten van Langevelde (ORCID: https://orcid.org/0000-0002-8871-9081)
- Luca Mainardi (ORCID: https://orcid.org/0000-0002-6276-6314)
- Luca Maria Sconfienza (ORCID: https://orcid.org/0000-0003-0759-8431)
- Valentina Corino (ORCID: https://orcid.org/0000-0003-1825-0422)
- Salvatore Gitto (ORCID: https://orcid.org/0000-0002-3623-7822)
- Antonino Cincotta
- Ana Navas Cañete (ORCID: https://orcid.org/0000-0002-5952-5210)
- Carmelo Messina
- Domenico Albano
- Carlotta Vignaga
Institutions
- University of Milan (IT)
- Leiden University Medical Center (NL)
- Centro Cardiologico Monzino (IT)
- IRCCS Ospedale Galeazzi - Sant'Ambrogio (IT)
- Istituto Ortopedico Gaetano Pini (IT)
- Politecnico di Milano (IT)
Publication Details
- Journal
- European Radiology Experimental
- Published
- 2025-09-12
- DOI
- https://doi.org/10.1186/s41747-025-00633-7
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Associazione Italiana per la Ricerca sul Cancro