Glomerular basement membrane thickness estimation and stratification via active semi-supervised learning model
The proposed pipeline obtained state-of-the-art performance in GBM segmentation, proving its robustness under image variations, such as magnification, contrast, and complex geometrical shapes. Automated measures could assist clinicians in standard clinical practice speeding up routine procedures with high diagnostic accuracy.
Authors
- Gianandrea Pasquinelli (ORCID: https://orcid.org/0000-0003-1506-1829)
- Sabrina Valente
- Gaetano La Manna (ORCID: https://orcid.org/0000-0001-5473-8551)
- Enrico Giampieri (ORCID: https://orcid.org/0000-0003-2269-2338)
- Daniel Remondini (ORCID: https://orcid.org/0000-0003-3185-7456)
- Gastone Castellani (ORCID: https://orcid.org/0000-0003-4892-925X)
- Gianluca Carlini (ORCID: https://orcid.org/0000-0003-2731-0300)
- Alessandra Merlotti (ORCID: https://orcid.org/0000-0002-3648-2828)
- Nico Curti (ORCID: https://orcid.org/0000-0001-5802-1195)
Institutions
- Azienda USL di Bologna (IT)
- University of Bologna (IT)
Publication Details
- Journal
- American Journal of Nephrology
- Published
- 2025-04-15
- DOI
- https://doi.org/10.1159/000542658
- Primary Topic
- Chronic Kidney Disease and Diabetes
- Type
- article
- Field-Weighted Citation Impact
- 0.00