Towards understanding speciation by automated extraction and description of 3D foraminifera stacks
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 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.
3D Krawtchouk moments, automated shape measurement, foraminifera, machine learning, understanding speciation
30-33
Zhang, Wenshu
cfa69116-bc11-4ca4-8444-07037f4aeab9
Ezard, Thomas
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Searle-Barnes, Alex
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Brombacher, Anieke
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Katsamenis, Orestis
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Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
29 March 2020
Zhang, Wenshu
cfa69116-bc11-4ca4-8444-07037f4aeab9
Ezard, Thomas
a143a893-07d0-4673-a2dd-cea2cd7e1374
Searle-Barnes, Alex
b06df0a2-db5d-44ef-9190-62b0102a1932
Brombacher, Anieke
2a4bbb84-4743-4a36-973b-4ad2bf743154
Katsamenis, Orestis
8553e7c3-d860-4b7a-a883-abf6c0c4b438
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
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)
IEEE, .
(doi:10.1109/SSIAI49293.2020.9094611).
Record type:
Book Section
Abstract
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.
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Published date: 29 March 2020
Venue - Dates:
The Southwest Symposium on Image Analysis and Interpretation, La Fonda on the Plaza, Santa Fe, United States, 2020-03-29 - 2020-03-31
Keywords:
3D Krawtchouk moments, automated shape measurement, foraminifera, machine learning, understanding speciation
Identifiers
Local EPrints ID: 443881
URI: http://eprints.soton.ac.uk/id/eprint/443881
PURE UUID: fe5e9885-088e-4945-9f46-f123ada5581a
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Date deposited: 16 Sep 2020 16:33
Last modified: 17 Mar 2024 03:48
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Author:
Wenshu Zhang
Author:
Thomas Ezard
Author:
Alex Searle-Barnes
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