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Article

Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys

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Department of Electronics and Telecommunications, Polytechnic of Turin, 10129 Turin, Italy
2
Pathology Unit, Department of Medical Sciences, University of Turin, 10126 Turin, Italy
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Division of Pathology, A.O.U. Città della Salute e della Scienza Hospital, 10126 Turin, Italy
4
Department of Oncology, University of Turin, 10126 Turin, Italy
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(10), 1644; https://doi.org/10.3390/electronics9101644
Received: 14 September 2020 / Revised: 1 October 2020 / Accepted: 3 October 2020 / Published: 8 October 2020
(This article belongs to the Special Issue Biomedical Image Processing and Classification)
In 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. View Full-Text
Keywords: kidney transplantation; digital pathology; deep learning; kidney fibrosis; blood vessel segmentation; convolutional neural networks kidney transplantation; digital pathology; deep learning; kidney fibrosis; blood vessel segmentation; convolutional neural networks
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MDPI and ACS Style

Salvi, M.; Mogetta, A.; Meiburger, K.M.; Gambella, A.; Molinaro, L.; Barreca, A.; Papotti, M.; Molinari, F. Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys. Electronics 2020, 9, 1644. https://doi.org/10.3390/electronics9101644

AMA Style

Salvi M, Mogetta A, Meiburger KM, Gambella A, Molinaro L, Barreca A, Papotti M, Molinari F. Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys. Electronics. 2020; 9(10):1644. https://doi.org/10.3390/electronics9101644

Chicago/Turabian Style

Salvi, Massimo, Alessandro Mogetta, Kristen M. Meiburger, Alessandro Gambella, Luca Molinaro, Antonella Barreca, Mauro Papotti, and Filippo Molinari. 2020. "Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys" Electronics 9, no. 10: 1644. https://doi.org/10.3390/electronics9101644

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