A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis.
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 panc...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4208778?pdf=render |
| _version_ | 1818065903097479168 |
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| author | John F Eisses Amy W Davis Akif Burak Tosun Zachary R Dionise Cheng Chen John A Ozolek Gustavo K Rohde Sohail Z Husain |
| author_facet | John F Eisses Amy W Davis Akif Burak Tosun Zachary R Dionise Cheng Chen John A Ozolek Gustavo K Rohde Sohail Z Husain |
| author_sort | John F Eisses |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T14:59:17Z |
| format | Article |
| id | doaj.art-31afd64f8b3c40f398822777b5785bd7 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T14:59:17Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-31afd64f8b3c40f398822777b5785bd72022-12-22T01:44:13ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01910e11022010.1371/journal.pone.0110220A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis.John F EissesAmy W DavisAkif Burak TosunZachary R DioniseCheng ChenJohn A OzolekGustavo K RohdeSohail Z HusainThe 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.http://europepmc.org/articles/PMC4208778?pdf=render |
| spellingShingle | John F Eisses Amy W Davis Akif Burak Tosun Zachary R Dionise Cheng Chen John A Ozolek Gustavo K Rohde Sohail Z Husain A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis. PLoS ONE |
| title | A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis. |
| title_full | A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis. |
| title_fullStr | A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis. |
| title_full_unstemmed | A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis. |
| title_short | A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis. |
| title_sort | computer based automated algorithm for assessing acinar cell loss after experimental pancreatitis |
| url | http://europepmc.org/articles/PMC4208778?pdf=render |
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