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 au...
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Online Access: | https://doi.org/10.3389/fmars.2022.869252 http://ecite.utas.edu.au/152135 |
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ftunivtasecite:oai:ecite.utas.edu.au:152135 2023-05-15T13:42:41+02:00 A promising approach to quantifying pteropod eggs using image analysis and machine learning Weldrick, CK 2022 application/pdf https://doi.org/10.3389/fmars.2022.869252 http://ecite.utas.edu.au/152135 en eng Frontiers Research Foundation http://ecite.utas.edu.au/152135/1/152135 - A promising approach to quantifying pteropod eggs using image.pdf http://dx.doi.org/10.3389/fmars.2022.869252 Weldrick, CK, A promising approach to quantifying pteropod eggs using image analysis and machine learning, Frontiers in Marine Science, 9 Article 869252. ISSN 2296-7745 (2022) [Refereed Article] http://ecite.utas.edu.au/152135 Biological Sciences Bioinformatics and computational biology Bioinformatic methods development Refereed Article PeerReviewed 2022 ftunivtasecite https://doi.org/10.3389/fmars.2022.869252 2022-11-21T23:17:12Z 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 ( R 2 = 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 (mm 2 ) ( 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 eCite UTAS (University of Tasmania) Austral East Antarctica Southern Ocean Frontiers in Marine Science 9 |
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Open Polar |
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eCite UTAS (University of Tasmania) |
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ftunivtasecite |
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English |
topic |
Biological Sciences Bioinformatics and computational biology Bioinformatic methods development |
spellingShingle |
Biological Sciences Bioinformatics and computational biology Bioinformatic methods development Weldrick, CK A promising approach to quantifying pteropod eggs using image analysis and machine learning |
topic_facet |
Biological Sciences Bioinformatics and computational biology Bioinformatic methods development |
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 ( R 2 = 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 (mm 2 ) ( 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 |
Weldrick, CK |
author_facet |
Weldrick, CK |
author_sort |
Weldrick, CK |
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 Research Foundation |
publishDate |
2022 |
url |
https://doi.org/10.3389/fmars.2022.869252 http://ecite.utas.edu.au/152135 |
geographic |
Austral East Antarctica Southern Ocean |
geographic_facet |
Austral East Antarctica Southern Ocean |
genre |
Antarc* Antarctica East Antarctica Limacina helicina Southern Ocean |
genre_facet |
Antarc* Antarctica East Antarctica Limacina helicina Southern Ocean |
op_relation |
http://ecite.utas.edu.au/152135/1/152135 - A promising approach to quantifying pteropod eggs using image.pdf http://dx.doi.org/10.3389/fmars.2022.869252 Weldrick, CK, A promising approach to quantifying pteropod eggs using image analysis and machine learning, Frontiers in Marine Science, 9 Article 869252. ISSN 2296-7745 (2022) [Refereed Article] http://ecite.utas.edu.au/152135 |
op_doi |
https://doi.org/10.3389/fmars.2022.869252 |
container_title |
Frontiers in Marine Science |
container_volume |
9 |
_version_ |
1766171577571868672 |