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Towards understanding speciation by automated extraction and description of 3D foraminifera stacks

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Author
Zhang, Wenshu
Ezard, Thomas
Searle-Barnes, Alex
Brombacher, Anieke
Katsamenis, Orestis
Nixon, Mark
Date
2020-05-18
Acceptance date
2020
Type
Conference paper
acceptedVersion
Publisher
IEEE
ISSN
2473-3598
Metadata
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Abstract
The sheer volume of 3D data restricts understanding of genetic spe- ciation when analyzing specimens of planktonic foraminifera and so we develop an end-to-end computer vision system to solve and ex- tend 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 signa- ture to describe different types of species. The automated analy- sis by computer vision gives insight that was hitherto unavailable in biological analysis: analyzing shape implies understanding spa- tial 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.
Journal/conference proceeding
2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI);
Citation
Zhang, W., Ezard, T., Searle-Barnes, A., Brombacher, A., Katsamenis, O. and Nixon, M. (2020) 'Towards Understanding Speciation By Automated Extraction And Description Of 3d Foraminifera Stacks', 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) (pp. 30-33). IEEE.
URI
http://hdl.handle.net/10369/11108
DOI
https://doi.org/10.1109/SSIAI49293.2020.9094611
Description
Conference paper presented at 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), 29-31 March 2020, available at: https://doi.org/10.1109/SSIAI49293.2020.9094611.
Rights
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Sponsorship
Cardiff Metropolitan University (Grant ID: Cardiff Metropolian (Internal))
This work was funded by the Natural Environment Research Council award NE/P019269/1. The authors thank μ-VIS X-ray Imaging Centre at University of Southampton for supporting micro-CT scanning of foreminifera.
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