Abstract
s / Pancreatology 14 (2014) S1eS129 S18 Aims: In this study, we utilized a novel computer-generated learning algorithm to construct an accurate and rapid method of quantifying acinar content. Materials&methods: HE-stained pancreatic sections were obtained in mice recovering from a 2-day, hourly caerulein hyperstimulation model of experimental pancreatitis. For training data, a pathologist carefully outlined discrete regions of acinar and non-acinar tissue in 21 sections at various stages of pancreatic injury and recovery (termed the “ground truth”). After the expert defined the ground truth, the computer was able to develop a prediction rule which was then applied to a unique set of high resolution images in order to validate the process. Results: For baseline, non-injured pancreatic sections, the software demonstrated close agreement with the ground truth in identifying baseline acinar tissue areawith only a difference of 1% ± 0.05% (p1⁄40.21). Within regions of injured tissue, the software reported a difference of 2.5% ± 0.04% in acinar area compared with the pathologist (p1⁄40.47). Surprisingly, on detailed morphological examination, the discrepancy was primarily because the software outlined acini and excluded inter-acinar and luminal white space with greater precision. Conclusion: The findings suggest that the software will be of great potential benefit to both clinicians and researchers in quantifying pancreatic acinar cell flux in the injured and recovering pancreas.
Published Version
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