haematoxylin and eosin-stained and digital slides were generated. Cisplatin, a widely used chemotherapy agent, with strong nephrotoxicity, was included in a ...
A Novel Automated Histopathological Method for Kidney Compartmentalisation: A Toxicological Pathology Perspective. Ryan A.
1 Hutchinson ,
Erio
2 Barale-Thomas ,
An
2 Vynckier ,
Peter W.
2 Hamilton
1– Centre for Cancer Research and Cell Biology, Queen's University Belfast, School of Medicine Dentistry & Biomedical Sciences, 97 Lisburn Road, Belfast BT9 7BL 2 Drug Safety Sciences, Janssen Research & Development, A division of Janssen Pharmaceutica N.V., Beerse, Belgium
Results
Introduction The detection of nephrotoxicity is an integral aspect of both pre-clinical drug development and its complex architecture, with several cell and tubular types organized in several compartments, makes its automated analysis difficult because the visual histological identification of the borders between these compartments can be difficult, subjective and poorly reproducible. Automated quantification of various biomarkers (Ki-67, TUNEL method, KIM-1) would greatly benefit from an automatic detection of the compartments, so that the quantification could be restricted to specific compartments depending on the mechanism expected and the biomarker of interest.
Figure 1
Figure 2
Methods Kidneys of control (untreated) and of cisplatin-treated male rats were haematoxylin and eosin-stained and digital slides were generated. Cisplatin, a widely used chemotherapy agent, with strong nephrotoxicity, was included in a mechanistic study to characterize the KIM-1 biomarker; early renal damage was observed in the outer stripe of the outer medulla, then extended to medullary rays, cortex, and eventually inner stripe and inner medulla. The digitised sections were imported into Definiens Tissue Studio to develop a customised, automated method for kidney compartmentalisation. Prior to commencing the algorithm development, a training session using the digital slides was undertaken with an expert veterinary pathologist (EBT) to identify distinguishing regional morphology. The maximum of 12 image subsets from each histological region of interest were chosen from the control sections to develop an automated algorithm to identify and classify these regions. A strict histomorphometric feature set was developed and implemented into the algorithm to ensure true anatomical boundaries were identified.
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Conclusions
Figures 1 & 2: Automated detection of histological boundaries in Day 1 and 3 rats. Figure 3: Whole slide section demonstrating compartmentalisation Figure 4: Automated segmentation of cortex only to detect glomeruli
The automated method was able to successfully identify the four compartments of interest (cortex, outer stripe of the outer medulla, inner stripe of the outer medulla, inner papilla) in both the absence and presence of glomeruli across the range of study animals. Staining heterogeneity was observed to be a factor which effected the initial detection and classification of the outer stripe. Using our refined, automated method, compartmentalisation demonstrated a homogenous and an appropriate histological border separating each region. In this project the feasibility of developing a reliable and reproducible automated algorithm on haematoxylin and eosin stained sections was the study goal. Further validation studies will be conducted to ensure the algorithm is robust and to include additional features for further detailed histological assessment. The customised algorithm provides a useful tool for performing further image analysis in specific compartments, without selecting manually the regions of interest, thus better characterizing the cells affected and the underlying mechanisms.
Acknowledgements
