Apr 2, 2020

VISTA: Virtual ImmunoSTAining for pancreatic disease quantification in murine cohorts

BioRxiv : the Preprint Server for Biology
L. TernesYoung Hwan Chang

Abstract

Mechanistic studies of pancreatic disease progression using animal models require objective and quantifiable assessment of tissue changes among animal cohorts. Disease state quantification, however, relies heavily on tissue immunostaining, which can be expensive, labor- and time-intensive, and all too often produces uneven staining that is prone to variable interpretation between experts and inaccurate quantification. Here we develop a fully automated semantic segmentation tool using deep learning for the rapid and objective quantification of histologic features using hematoxylin and eosin (H&E) stained pancreatic tissue sections acquired from murine pancreatic cancer models. The tool was successfully trained to segment and quantify multiple histopathologic features of pancreatic pre-cancer evolution, including normal acinar structures, the ductal phenotype of acinar-to ductal metaplasia (ADM), dysplasia, and the expanding stromal compartment. Disease quantifications produced by our computational tool were highly correlated to the results obtained by immunostaining markers of normal and diseased tissue (DAPI, amylase, and cytokeratins; correlation score= 0.9, 0.95, and 0.91, respectively) and were able to accurately reproduce i...Continue Reading

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Mentioned in this Paper

Laboratory Procedures
Genes
Sequence Determinations, RNA
Transcription, Genetic
Gene Expression
Gene Expression Profiling
CLASP1
Analysis
Mucosal-Associated Invariant T-Cell
SPG21

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