Articles | Volume 16, issue 6
https://doi.org/10.5194/cp-16-2415-2020
https://doi.org/10.5194/cp-16-2415-2020
Technical note
 | 
02 Dec 2020
Technical note |  | 02 Dec 2020

Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow

Martin Tetard, Ross Marchant, Giuseppe Cortese, Yves Gally, Thibault de Garidel-Thoron, and Luc Beaufort

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Cited articles

Abelmann, A:. Radiolarian taxa from Southern Ocean sediment traps (Atlantic sector), Polar Biol., 12, 373–385, 1992. a
Abelmann, A. and Nimmergut, A.: Radiolarians in the Sea of Okhotsk and their ecological implication for paleoenvironmental reconstructions, Deep-Sea Res. Pt. II, 52, 2302–2331, 2005. a
Abelmann, A., Brathauer, U., Gersonde, R., Siegier, R., and Zielinski, U.: Radiolarian-based transfer function for the estimation of sea surface temperatures in the Southern Ocean (Atlantic Sector), Paleoceanography, 14, 410–421, 1999. a
Apostol, L. A., Marquez, E., Gasmen, P., and Solano, G.: Radss: A radiolarian classifier using support vector machines, 7th International Conference on Information, Intelligence, Systems & Applications (IISA), 13–15 July 2016, Chalkidiki, Greece, 2016. a
Beaufort, L. and Dollfus, D.: Automatic recognition of coccoliths by dynamical neural networks, Mar. Micropaleontol., 51, 57–73, 2004. a
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Short summary
Radiolarians are marine micro-organisms that produce a siliceous shell that is preserved in the fossil record and can be used to reconstruct past climate variability. However, their study is only possible after a time-consuming manual selection of their shells from the sediment followed by their individual identification. Thus, we develop a new fully automated workflow consisting of microscopic radiolarian image acquisition, image processing and identification using artificial intelligence.