A Promising Approach to Quantifying Pteropod Eggs Using Image Analysis and Machine Learning
A newly developed protocol to semi-automate egg counting in Southern Ocean shelled (thecosome) pteropods using image analysis software and machine learning algorithms was developed and tested for accuracy. Preserved thecosome pteropod (Limacina helicina antarctica) egg masses collected from two aust...
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ftdoajarticles:oai:doaj.org/article:86dc8aa539454438b75fd2552df5e483 2023-05-15T13:49:53+02:00 A Promising Approach to Quantifying Pteropod Eggs Using Image Analysis and Machine Learning Christine K. Weldrick 2022-04-01T00:00:00Z https://doi.org/10.3389/fmars.2022.869252 https://doaj.org/article/86dc8aa539454438b75fd2552df5e483 EN eng Frontiers Media S.A. https://www.frontiersin.org/articles/10.3389/fmars.2022.869252/full https://doaj.org/toc/2296-7745 2296-7745 doi:10.3389/fmars.2022.869252 https://doaj.org/article/86dc8aa539454438b75fd2552df5e483 Frontiers in Marine Science, Vol 9 (2022) egg counting egg masses image analysis machine learning pteropods Southern Ocean Science Q General. Including nature conservation geographical distribution QH1-199.5 article 2022 ftdoajarticles https://doi.org/10.3389/fmars.2022.869252 2022-12-31T01:48:22Z A newly developed protocol to semi-automate egg counting in Southern Ocean shelled (thecosome) pteropods using image analysis software and machine learning algorithms was developed and tested for accuracy. Preserved thecosome pteropod (Limacina helicina antarctica) egg masses collected from two austral summer research voyages in East Antarctica were digitally photographed to develop a streamlined approach to enumerate eggs within egg masses using Fiji/ImageJ and the associated machine learning plugin known as Trainable Weka Segmentation. Results from this semi-automated approach were then used to compare with manual egg counts from eggs dissected from egg masses under stereomicroscope. A statistically significant correlation was observed between manual and semi-automated approaches (R2 = 0.92, p < 0.05). There was no significant difference between manual and automated protocols when egg counts were divided by the egg mass areas (mm2) (t(29.6) = 1.98, p = 0.06). However, the average time to conduct semi-automated counts (M = 7.4, SD = 1.2) was significantly less than that for the manual enumeration technique (M = 35.9, SD = 5.7; t(30) = 2.042, p < 0.05). This new approach is promising and, unlike manual enumeration, could allow specimens to remain intact for use in live culturing experiments. Despite some limitations that are discussed, this user-friendly and simplistic protocol can provide the basis for further future development, including the addition of macro scripts to improve reproducibility and through the association with other imaging platforms to enhance interoperability. Furthermore, egg counting using this technique may lead to a relatively unexplored monitoring tool to better understand the responses of a species highly sensitive to multiple stressors connected to climate change. Article in Journal/Newspaper Antarc* Antarctica East Antarctica Limacina helicina Southern Ocean Directory of Open Access Journals: DOAJ Articles Southern Ocean East Antarctica Austral Frontiers in Marine Science 9 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
egg counting egg masses image analysis machine learning pteropods Southern Ocean Science Q General. Including nature conservation geographical distribution QH1-199.5 |
spellingShingle |
egg counting egg masses image analysis machine learning pteropods Southern Ocean Science Q General. Including nature conservation geographical distribution QH1-199.5 Christine K. Weldrick A Promising Approach to Quantifying Pteropod Eggs Using Image Analysis and Machine Learning |
topic_facet |
egg counting egg masses image analysis machine learning pteropods Southern Ocean Science Q General. Including nature conservation geographical distribution QH1-199.5 |
description |
A newly developed protocol to semi-automate egg counting in Southern Ocean shelled (thecosome) pteropods using image analysis software and machine learning algorithms was developed and tested for accuracy. Preserved thecosome pteropod (Limacina helicina antarctica) egg masses collected from two austral summer research voyages in East Antarctica were digitally photographed to develop a streamlined approach to enumerate eggs within egg masses using Fiji/ImageJ and the associated machine learning plugin known as Trainable Weka Segmentation. Results from this semi-automated approach were then used to compare with manual egg counts from eggs dissected from egg masses under stereomicroscope. A statistically significant correlation was observed between manual and semi-automated approaches (R2 = 0.92, p < 0.05). There was no significant difference between manual and automated protocols when egg counts were divided by the egg mass areas (mm2) (t(29.6) = 1.98, p = 0.06). However, the average time to conduct semi-automated counts (M = 7.4, SD = 1.2) was significantly less than that for the manual enumeration technique (M = 35.9, SD = 5.7; t(30) = 2.042, p < 0.05). This new approach is promising and, unlike manual enumeration, could allow specimens to remain intact for use in live culturing experiments. Despite some limitations that are discussed, this user-friendly and simplistic protocol can provide the basis for further future development, including the addition of macro scripts to improve reproducibility and through the association with other imaging platforms to enhance interoperability. Furthermore, egg counting using this technique may lead to a relatively unexplored monitoring tool to better understand the responses of a species highly sensitive to multiple stressors connected to climate change. |
format |
Article in Journal/Newspaper |
author |
Christine K. Weldrick |
author_facet |
Christine K. Weldrick |
author_sort |
Christine K. Weldrick |
title |
A Promising Approach to Quantifying Pteropod Eggs Using Image Analysis and Machine Learning |
title_short |
A Promising Approach to Quantifying Pteropod Eggs Using Image Analysis and Machine Learning |
title_full |
A Promising Approach to Quantifying Pteropod Eggs Using Image Analysis and Machine Learning |
title_fullStr |
A Promising Approach to Quantifying Pteropod Eggs Using Image Analysis and Machine Learning |
title_full_unstemmed |
A Promising Approach to Quantifying Pteropod Eggs Using Image Analysis and Machine Learning |
title_sort |
promising approach to quantifying pteropod eggs using image analysis and machine learning |
publisher |
Frontiers Media S.A. |
publishDate |
2022 |
url |
https://doi.org/10.3389/fmars.2022.869252 https://doaj.org/article/86dc8aa539454438b75fd2552df5e483 |
geographic |
Southern Ocean East Antarctica Austral |
geographic_facet |
Southern Ocean East Antarctica Austral |
genre |
Antarc* Antarctica East Antarctica Limacina helicina Southern Ocean |
genre_facet |
Antarc* Antarctica East Antarctica Limacina helicina Southern Ocean |
op_source |
Frontiers in Marine Science, Vol 9 (2022) |
op_relation |
https://www.frontiersin.org/articles/10.3389/fmars.2022.869252/full https://doaj.org/toc/2296-7745 2296-7745 doi:10.3389/fmars.2022.869252 https://doaj.org/article/86dc8aa539454438b75fd2552df5e483 |
op_doi |
https://doi.org/10.3389/fmars.2022.869252 |
container_title |
Frontiers in Marine Science |
container_volume |
9 |
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1766252480297959424 |