A Computer-Based Automated Algorithm for Assessing Acinar Cell Loss after Experimental Pancreatitis
| Autor: | Zachary R. Dionise, Gustavo K. Rohde, Cheng Chen, John A. Ozolek, John F. Eisses, Akif Burak Tosun, Amy Davis, Sohail Z. Husain |
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| Rok vydání: | 2014 |
| Předmět: |
Computer and Information Sciences
medicine.medical_specialty Pathology Histology Computer science lcsh:Medicine Endocrine System Acinar Cells Gastroenterology and Hepatology FOS: Medical engineering Automation Mice 03 medical and health sciences 0302 clinical medicine Medicine and Health Sciences medicine Acinar cell Animals Humans lcsh:Science Pancreas Ceruletide 030304 developmental biology 0303 health sciences Multidisciplinary Applied Mathematics lcsh:R Computer based Biology and Life Sciences 90399 Biomedical Engineering not elsewhere classified medicine.disease medicine.anatomical_structure Pancreatitis Automated algorithm Physical Sciences lcsh:Q 030211 gastroenterology & hepatology Radiology Anatomy Pancreatic injury Algorithms Mathematics Research Article |
| Zdroj: | PLoS ONE PLoS ONE, Vol 9, Iss 10, p e110220 (2014) |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0110220 |
| Popis: | The change in exocrine mass is an important parameter to follow in experimental models of pancreatic injury and regeneration. However, at present, the quantitative assessment of exocrine content by histology is tedious and operator-dependent, requiring manual assessment of acinar area on serial pancreatic sections. In this study, we utilized a novel computer-generated learning algorithm to construct an accurate and rapid method of quantifying acinar content. The algorithm works by learning differences in pixel characteristics from input examples provided by human experts. 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 that was then applied to a unique set of high-resolution images in order to validate the process. For baseline, non-injured pancreatic sections, the software demonstrated close agreement with the ground truth in identifying baseline acinar tissue area with only a difference of 1% ± 0.05% (p = 0.21). Within regions of injured tissue, the software reported a difference of 2.5% ± 0.04% in acinar area compared with the pathologist (p = 0.47). 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. 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. |
| Databáze: | OpenAIRE |
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