How many specimens make a sufficient training set for automated three-dimensional feature extraction?
Deep learning has emerged as a robust tool for automating feature extraction from three-dimensional images, offering an efficient alternative to labour-intensive and potentially biased manual image segmentation methods. However, there has been limited exploration into the optimal training set sizes,...
Published in: | Royal Society Open Science |
---|---|
Main Authors: | , , , , , |
Other Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
The Royal Society
2024
|
Subjects: | |
Online Access: | http://dx.doi.org/10.1098/rsos.240113 https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.240113 https://royalsocietypublishing.org/doi/full-xml/10.1098/rsos.240113 |
id |
crroyalsociety:10.1098/rsos.240113 |
---|---|
record_format |
openpolar |
spelling |
crroyalsociety:10.1098/rsos.240113 2024-09-15T18:31:03+00:00 How many specimens make a sufficient training set for automated three-dimensional feature extraction? Mulqueeney, James M. Searle-Barnes, Alex Brombacher, Anieke Sweeney, Marisa Goswami, Anjali Ezard, Thomas H. G. Natural Environment Research Council Leverhulme Trust European Research Council 2024 http://dx.doi.org/10.1098/rsos.240113 https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.240113 https://royalsocietypublishing.org/doi/full-xml/10.1098/rsos.240113 en eng The Royal Society http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ Royal Society Open Science volume 11, issue 6 ISSN 2054-5703 journal-article 2024 crroyalsociety https://doi.org/10.1098/rsos.240113 2024-08-19T04:24:54Z Deep learning has emerged as a robust tool for automating feature extraction from three-dimensional images, offering an efficient alternative to labour-intensive and potentially biased manual image segmentation methods. However, there has been limited exploration into the optimal training set sizes, including assessing whether artficial expansion by data augmentation can achieve consistent results in less time and how consistent these benefits are across different types of traits. In this study, we manually segmented 50 planktonic foraminifera specimens from the genus Menardella to determine the minimum number of training images required to produce accurate volumetric and shape data from internal and external structures. The results reveal unsurprisingly that deep learning models improve with a larger number of training images with eight specimens being required to achieve 95% accuracy. Furthermore, data augmentation can enhance network accuracy by up to 8.0%. Notably, predicting both volumetric and shape measurements for the internal structure poses a greater challenge compared with the external structure, owing to low contrast differences between different materials and increased geometric complexity. These results provide novel insight into optimal training set sizes for precise image segmentation of diverse traits and highlight the potential of data augmentation for enhancing multivariate feature extraction from three-dimensional images. Article in Journal/Newspaper Planktonic foraminifera The Royal Society Royal Society Open Science 11 6 |
institution |
Open Polar |
collection |
The Royal Society |
op_collection_id |
crroyalsociety |
language |
English |
description |
Deep learning has emerged as a robust tool for automating feature extraction from three-dimensional images, offering an efficient alternative to labour-intensive and potentially biased manual image segmentation methods. However, there has been limited exploration into the optimal training set sizes, including assessing whether artficial expansion by data augmentation can achieve consistent results in less time and how consistent these benefits are across different types of traits. In this study, we manually segmented 50 planktonic foraminifera specimens from the genus Menardella to determine the minimum number of training images required to produce accurate volumetric and shape data from internal and external structures. The results reveal unsurprisingly that deep learning models improve with a larger number of training images with eight specimens being required to achieve 95% accuracy. Furthermore, data augmentation can enhance network accuracy by up to 8.0%. Notably, predicting both volumetric and shape measurements for the internal structure poses a greater challenge compared with the external structure, owing to low contrast differences between different materials and increased geometric complexity. These results provide novel insight into optimal training set sizes for precise image segmentation of diverse traits and highlight the potential of data augmentation for enhancing multivariate feature extraction from three-dimensional images. |
author2 |
Natural Environment Research Council Leverhulme Trust European Research Council |
format |
Article in Journal/Newspaper |
author |
Mulqueeney, James M. Searle-Barnes, Alex Brombacher, Anieke Sweeney, Marisa Goswami, Anjali Ezard, Thomas H. G. |
spellingShingle |
Mulqueeney, James M. Searle-Barnes, Alex Brombacher, Anieke Sweeney, Marisa Goswami, Anjali Ezard, Thomas H. G. How many specimens make a sufficient training set for automated three-dimensional feature extraction? |
author_facet |
Mulqueeney, James M. Searle-Barnes, Alex Brombacher, Anieke Sweeney, Marisa Goswami, Anjali Ezard, Thomas H. G. |
author_sort |
Mulqueeney, James M. |
title |
How many specimens make a sufficient training set for automated three-dimensional feature extraction? |
title_short |
How many specimens make a sufficient training set for automated three-dimensional feature extraction? |
title_full |
How many specimens make a sufficient training set for automated three-dimensional feature extraction? |
title_fullStr |
How many specimens make a sufficient training set for automated three-dimensional feature extraction? |
title_full_unstemmed |
How many specimens make a sufficient training set for automated three-dimensional feature extraction? |
title_sort |
how many specimens make a sufficient training set for automated three-dimensional feature extraction? |
publisher |
The Royal Society |
publishDate |
2024 |
url |
http://dx.doi.org/10.1098/rsos.240113 https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.240113 https://royalsocietypublishing.org/doi/full-xml/10.1098/rsos.240113 |
genre |
Planktonic foraminifera |
genre_facet |
Planktonic foraminifera |
op_source |
Royal Society Open Science volume 11, issue 6 ISSN 2054-5703 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1098/rsos.240113 |
container_title |
Royal Society Open Science |
container_volume |
11 |
container_issue |
6 |
_version_ |
1810472648249966592 |