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EnglishIn kidney transplantations, the evaluation of the vascular structures and stromal areas is crucial for determining kidney acceptance, which is currently based on the pathologist’s visual evaluation. In this context, an accurate assessment of the vascular and stromal injury is fundamental to assessing the nephron status. In the present paper, the authors present a fully automated algorithm, called RENFAST (Rapid EvaluatioN of Fibrosis And vesselS Thickness), for the segmentation of kidney blood vessels and fibrosis in histopathological images. The proposed method employs a novel strategy based on deep learning to accurately segment blood vessels, while interstitial fibrosis is assessed using an adaptive stain separation method. The RENFAST algorithm is developed and tested on 350 periodic acid–Schiff (PAS) images for blood vessel segmentation and on 300 Massone’s trichrome (TRIC) stained images for the detection of renal fibrosis. In the TEST set, the algorithm exhibits excellent segmentation performance in both blood vessels (accuracy: 0.8936) and fibrosis (accuracy: 0.9227) and outperforms all the compared methods. To the best of our knowledge, the RENFAST algorithm is the first fully automated method capable of detecting both blood vessels and fibrosis in digital histological images. Being very fast (average computational time 2.91 s), this algorithm paves the way for automated, quantitative, and real-time kidney graft assessments.
Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys / Salvi, Massimo; Mogetta, Alessandro; Meiburger, Kristen M.; Gambella, Alessandro; Molinaro, Luca; Barreca, Antonella; Papotti, Mauro; Molinari, Filippo. - In: ELECTRONICS. - ISSN 2079-9292. - ELETTRONICO. - 9:10(2020), p. 1644. [10.3390/electronics9101644]
| Titolo: | Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys | |
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| Data di pubblicazione: | 2020 | |
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| Digital Object Identifier (DOI): | http://dx.doi.org/10.3390/electronics9101644 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| (2020) paper - RENFAST.pdf | 2a Post-print versione editoriale / Version of Record |  | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2848152
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