Quantifying acute kidney injury in an Ischaemia-Reperfusion Injury mouse model using Deep Learning-based semantic segmentation in histology.

Luchian, Andreea, Cepeda, Katherine Trivino, Harwood, Rachel ORCID: 0000-0003-3440-3142, Murray, Patricia ORCID: 0000-0003-1316-148X, Wilm, Bettina ORCID: 0000-0002-9245-993X, Kenny, Simon, Pregel, Paola and Ressel, Lorenzo ORCID: 0000-0002-6614-1223 (2023) Quantifying acute kidney injury in an Ischaemia-Reperfusion Injury mouse model using Deep Learning-based semantic segmentation in histology. Biology open, 12 (9). bio.059988-bio.059988.

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Official URL: http://dx.doi.org/10.1242/bio.059988

Abstract

This study focuses on Ischaemia-Reperfusion Injury (IRI) in kidneys, a cause of acute kidney injury (AKI) and end-stage kidney disease (ESKD). Traditional kidney damage assessment methods are semi-quantitative and subjective. This study aims to use a Convolutional Neural Network (CNN) to segment murine kidney structures after IRI, quantify damage via CNN-generated pathological measurements, and compare this to conventional scoring. The CNN was able to accurately segment the different pathological classes, such as Intratubular Casts and Tubular Necrosis, with an F1 score of over 0.75. Some classes, such as Glomeruli and Proximal Tubules, had even higher statistical values with F1 scores over 0.90. The scoring generated based on the segmentation approach statistically correlated with the semiquantitative assessment (Spearman Correlation coefficient=0.94). The heatmap approach localised the intratubular necrosis mainly in the outer stripe of the outer medulla, while the tubular casts were also present in more superficial or deeper portions of the cortex and medullary areas. This study presents a CNN model capable of segmenting multiple classes of interest, including acute IRI-specific pathological changes, in a whole mouse kidney section and can provide insights into the distribution of pathological classes within the whole mouse kidney section.

Item Type:	Article
Uncontrolled Keywords:	Animals, Mice, Reperfusion Injury, Disease Models, Animal, Necrosis, Semantics, Acute Kidney Injury, Deep Learning
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences Faculty of Health and Life Sciences > Institute of Infection, Veterinary and Ecological Sciences
Depositing User:	Symplectic Admin
Date Deposited:	01 Sep 2023 09:34
Last Modified:	29 Sep 2023 08:27
DOI:	10.1242/bio.059988
Open Access URL:	https://journals.biologists.com/bio/article/doi/10...
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3172484