Deep Learning Approaches for the Segmentation of Glomeruli in Kidney Histopathological Images

Giovanna Maria Dimitri, Paolo Andreini, Simone Bonechi, Monica Bianchini, Alessandro Mecocci, Franco Scarselli, Alberto Zacchi, Guido Garosi, Thomas Marcuzzo and Sergio Antonio Tripodi
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Giovanna Maria Dimitri: Department of Information Engineering and Mathematics (DIISM), University of Siena, 53100 Siena, Italy
Paolo Andreini: Department of Information Engineering and Mathematics (DIISM), University of Siena, 53100 Siena, Italy
Simone Bonechi: Department of Information Engineering and Mathematics (DIISM), University of Siena, 53100 Siena, Italy
Monica Bianchini: Department of Information Engineering and Mathematics (DIISM), University of Siena, 53100 Siena, Italy
Alessandro Mecocci: Department of Information Engineering and Mathematics (DIISM), University of Siena, 53100 Siena, Italy
Franco Scarselli: Department of Information Engineering and Mathematics (DIISM), University of Siena, 53100 Siena, Italy
Alberto Zacchi: Azienda Sanitaria Universitaria Integrata di Trieste, ASUITS, 34127 Trieste, Italy
Guido Garosi: Nephrology Dialysis and Transplantation Unit, Siena University, Azienda Ospedaliera Universitaria Senese, Le Scotte, 53100 Siena, Italy
Thomas Marcuzzo: Azienda Sanitaria Universitaria Integrata di Trieste, ASUITS, 34127 Trieste, Italy
Sergio Antonio Tripodi: Nephrology Dialysis and Transplantation Unit, Siena University, Azienda Ospedaliera Universitaria Senese, Le Scotte, 53100 Siena, Italy

Mathematics, 2022, vol. 10, issue 11, 1-10

Abstract: Deep learning is widely applied in bioinformatics and biomedical imaging, due to its ability to perform various clinical tasks automatically and accurately. In particular, the application of deep learning techniques for the automatic identification of glomeruli in histopathological kidney images can play a fundamental role, offering a valid decision support system tool for the automatic evaluation of the Karpinski metric. This will help clinicians in detecting the presence of sclerotic glomeruli in order to decide whether the kidney is transplantable or not. In this work, we implemented a deep learning framework to identify and segment sclerotic and non-sclerotic glomeruli from scanned Whole Slide Images (WSIs) of human kidney biopsies. The experiments were conducted on a new dataset collected by both the Siena and Trieste hospitals. The images were segmented using the DeepLab V2 model, with a pre-trained ResNet101 encoder, applied to 512 × 512 patches extracted from the original WSIs. The results obtained are promising and show a good performance in the segmentation task and a good generalization capacity, despite the different coloring and typology of the histopathological images. Moreover, we present a novel use of the CD10 staining procedure, which gives promising results when applied to the segmentation of sclerotic glomeruli in kidney tissues.

Keywords: deep learning; image segmentation; kidney transplantation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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