Towards understanding speciation by automated extraction and description of 3D foraminifera stacks
The sheer volume of 3D data restricts understanding of genetic speciation when analyzing specimens of planktonic foraminifera and so we develop an end-to-end computer vision system to solve and extend this. The observed fossils are planktonic foraminifera, which are single-celled organisms that live...
Published in: | 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) |
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ftsouthampton:oai:eprints.soton.ac.uk:443881 2023-08-27T04:11:35+02:00 Towards understanding speciation by automated extraction and description of 3D foraminifera stacks Zhang, Wenshu Ezard, Thomas Searle-Barnes, Alex Brombacher, Anieke Katsamenis, Orestis Nixon, Mark 2020-03-29 https://eprints.soton.ac.uk/443881/ English eng Institute of Electrical and Electronics Engineers Inc. Zhang, Wenshu, Ezard, Thomas, Searle-Barnes, Alex, Brombacher, Anieke, Katsamenis, Orestis and Nixon, Mark (2020) Towards understanding speciation by automated extraction and description of 3D foraminifera stacks. In, 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, 2020-March) The Southwest Symposium on Image Analysis and Interpretation (29/03/20 - 31/03/20) Institute of Electrical and Electronics Engineers Inc., pp. 30-33. (doi:10.1109/SSIAI49293.2020.9094611 <http://dx.doi.org/10.1109/SSIAI49293.2020.9094611>). Book Section PeerReviewed 2020 ftsouthampton https://doi.org/10.1109/SSIAI49293.2020.9094611 2023-08-03T22:24:50Z The sheer volume of 3D data restricts understanding of genetic speciation when analyzing specimens of planktonic foraminifera and so we develop an end-to-end computer vision system to solve and extend this. The observed fossils are planktonic foraminifera, which are single-celled organisms that live in vast numbers in the world's oceans. Each foram retains a complete record of its size and shape at each stage along its journey through life. In this study, a variety of individual foraminifera are analyzed to study the differences among them and compared with manually labelled ground truth. This is an approach which (i) automatically reconstructs individual chambers for each specimen from image sequences, (ii) uses a shape signature to describe different types of species. The automated analysis by computer vision gives insight that was hitherto unavailable in biological analysis: analyzing shape implies understanding spatial arrangement and this is new to the biological analysis of these specimens. By processing datasets of 3D samples containing 9GB of points, we show that speciation can indeed now be analyzed and that automated analysis from morphological features leads to new insight into the origins of life. Book Part Planktonic foraminifera University of Southampton: e-Prints Soton 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) 30 33 |
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University of Southampton: e-Prints Soton |
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English |
description |
The sheer volume of 3D data restricts understanding of genetic speciation when analyzing specimens of planktonic foraminifera and so we develop an end-to-end computer vision system to solve and extend this. The observed fossils are planktonic foraminifera, which are single-celled organisms that live in vast numbers in the world's oceans. Each foram retains a complete record of its size and shape at each stage along its journey through life. In this study, a variety of individual foraminifera are analyzed to study the differences among them and compared with manually labelled ground truth. This is an approach which (i) automatically reconstructs individual chambers for each specimen from image sequences, (ii) uses a shape signature to describe different types of species. The automated analysis by computer vision gives insight that was hitherto unavailable in biological analysis: analyzing shape implies understanding spatial arrangement and this is new to the biological analysis of these specimens. By processing datasets of 3D samples containing 9GB of points, we show that speciation can indeed now be analyzed and that automated analysis from morphological features leads to new insight into the origins of life. |
format |
Book Part |
author |
Zhang, Wenshu Ezard, Thomas Searle-Barnes, Alex Brombacher, Anieke Katsamenis, Orestis Nixon, Mark |
spellingShingle |
Zhang, Wenshu Ezard, Thomas Searle-Barnes, Alex Brombacher, Anieke Katsamenis, Orestis Nixon, Mark Towards understanding speciation by automated extraction and description of 3D foraminifera stacks |
author_facet |
Zhang, Wenshu Ezard, Thomas Searle-Barnes, Alex Brombacher, Anieke Katsamenis, Orestis Nixon, Mark |
author_sort |
Zhang, Wenshu |
title |
Towards understanding speciation by automated extraction and description of 3D foraminifera stacks |
title_short |
Towards understanding speciation by automated extraction and description of 3D foraminifera stacks |
title_full |
Towards understanding speciation by automated extraction and description of 3D foraminifera stacks |
title_fullStr |
Towards understanding speciation by automated extraction and description of 3D foraminifera stacks |
title_full_unstemmed |
Towards understanding speciation by automated extraction and description of 3D foraminifera stacks |
title_sort |
towards understanding speciation by automated extraction and description of 3d foraminifera stacks |
publisher |
Institute of Electrical and Electronics Engineers Inc. |
publishDate |
2020 |
url |
https://eprints.soton.ac.uk/443881/ |
genre |
Planktonic foraminifera |
genre_facet |
Planktonic foraminifera |
op_relation |
Zhang, Wenshu, Ezard, Thomas, Searle-Barnes, Alex, Brombacher, Anieke, Katsamenis, Orestis and Nixon, Mark (2020) Towards understanding speciation by automated extraction and description of 3D foraminifera stacks. In, 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI). (Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, 2020-March) The Southwest Symposium on Image Analysis and Interpretation (29/03/20 - 31/03/20) Institute of Electrical and Electronics Engineers Inc., pp. 30-33. (doi:10.1109/SSIAI49293.2020.9094611 <http://dx.doi.org/10.1109/SSIAI49293.2020.9094611>). |
op_doi |
https://doi.org/10.1109/SSIAI49293.2020.9094611 |
container_title |
2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) |
container_start_page |
30 |
op_container_end_page |
33 |
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1775354527669551104 |