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Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images
Otoliths (ear-stones) in the inner ears of vertebrates containing visible year zones are used extensively to determine fish age. Analysis of otoliths is a time-consuming and difficult task that requires the education of human experts. Human age estimates are inconsistent, as several readings by the...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9635702/ https://www.ncbi.nlm.nih.gov/pubmed/36331956 http://dx.doi.org/10.1371/journal.pone.0277244 |
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author | Martinsen, Iver Harbitz, Alf Bianchi, Filippo Maria |
author_facet | Martinsen, Iver Harbitz, Alf Bianchi, Filippo Maria |
author_sort | Martinsen, Iver |
collection | PubMed |
description | Otoliths (ear-stones) in the inner ears of vertebrates containing visible year zones are used extensively to determine fish age. Analysis of otoliths is a time-consuming and difficult task that requires the education of human experts. Human age estimates are inconsistent, as several readings by the same human expert might result in different ages assigned to the same otolith, in addition to an inherent bias between readers. To improve efficiency and resolve inconsistent results in the age reading from otolith images by human experts, an automated procedure based on convolutional neural networks (CNNs), a class of deep learning models suitable for image processing, is investigated. We applied CNNs that perform image regression to estimate the age of Greenland halibut (Reinhardtius hippoglossoides) with good results for individual ages as well as the overall age distribution, with an average CV of about 10% relative to the read ages by experts. In addition, the density distribution of predicted ages resembles the density distribution of the ground truth. By using k*l-fold cross-validation, we test all available samples, and we show that the results are rather sensitive to the choice of test set. Finally, we apply explanation techniques to analyze the decision process of deep learning models. In particular, we produce heatmaps indicating which input features that are the most important in the computation of predicted age. |
format | Online Article Text |
id | pubmed-9635702 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-96357022022-11-05 Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images Martinsen, Iver Harbitz, Alf Bianchi, Filippo Maria PLoS One Research Article Otoliths (ear-stones) in the inner ears of vertebrates containing visible year zones are used extensively to determine fish age. Analysis of otoliths is a time-consuming and difficult task that requires the education of human experts. Human age estimates are inconsistent, as several readings by the same human expert might result in different ages assigned to the same otolith, in addition to an inherent bias between readers. To improve efficiency and resolve inconsistent results in the age reading from otolith images by human experts, an automated procedure based on convolutional neural networks (CNNs), a class of deep learning models suitable for image processing, is investigated. We applied CNNs that perform image regression to estimate the age of Greenland halibut (Reinhardtius hippoglossoides) with good results for individual ages as well as the overall age distribution, with an average CV of about 10% relative to the read ages by experts. In addition, the density distribution of predicted ages resembles the density distribution of the ground truth. By using k*l-fold cross-validation, we test all available samples, and we show that the results are rather sensitive to the choice of test set. Finally, we apply explanation techniques to analyze the decision process of deep learning models. In particular, we produce heatmaps indicating which input features that are the most important in the computation of predicted age. Public Library of Science 2022-11-04 /pmc/articles/PMC9635702/ /pubmed/36331956 http://dx.doi.org/10.1371/journal.pone.0277244 Text en © 2022 Martinsen et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Martinsen, Iver Harbitz, Alf Bianchi, Filippo Maria Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images |
title | Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images |
title_full | Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images |
title_fullStr | Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images |
title_full_unstemmed | Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images |
title_short | Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images |
title_sort | age prediction by deep learning applied to greenland halibut (reinhardtius hippoglossoides) otolith images |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9635702/ https://www.ncbi.nlm.nih.gov/pubmed/36331956 http://dx.doi.org/10.1371/journal.pone.0277244 |
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