Articles | Volume 16, issue 6
https://doi.org/10.5194/cp-16-2415-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/cp-16-2415-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
02 Dec 2020
Technical note |
| 02 Dec 2020
| 02 Dec 2020
Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow
Aix Marseille Univ, CNRS, IRD, Coll France, INRAE, CEREGE, Aix-en-Provence, France
Ross Marchant
Aix Marseille Univ, CNRS, IRD, Coll France, INRAE, CEREGE, Aix-en-Provence, France
present address: School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, Australia
Giuseppe Cortese
GNS Science, Lower Hutt, New Zealand
Yves Gally
Aix Marseille Univ, CNRS, IRD, Coll France, INRAE, CEREGE, Aix-en-Provence, France
Thibault de Garidel-Thoron
Aix Marseille Univ, CNRS, IRD, Coll France, INRAE, CEREGE, Aix-en-Provence, France
Luc Beaufort
Aix Marseille Univ, CNRS, IRD, Coll France, INRAE, CEREGE, Aix-en-Provence, France
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Cited
19 citations as recorded by crossref.
- Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks X. Liu et al.
- Semantic segmentation of vertebrate microfossils from computed tomography data using a deep learning approach Y. Hou et al.
- Towards a Fleet of Robots for Orientation, Imaging, and Morphometric Analyses of Planktonic Foraminifera M. Knappertsbusch & J. Eisenecker
- Diversity of polycystine radiolarians in sediment traps from the Ionian, North Aegean and Cretan Seas: A preliminary account T. Danelian et al.
- No dramatic changes observed in subtropical radiolarian plankton assemblages during the Middle Eocene Climatic Optimum (MECO); evidence from the North Atlantic ODP Site 1051 M. Meunier & T. Danelian
- Addressing the “open world”: detecting and segmenting pollen on palynological slides with deep learning J. Feng et al.
- Convolutional neural network application on a new middle Eocene radiolarian dataset V. Carlsson et al.
- Deep learning object detection for fossil diatom counting: assessing the impact of fossil preservation and intraspecific morphological variation S. Ishino et al.
- Merging databases for CNN image recognition, increasing bias or improving results? M. Tetard et al.
- Morphometrics and machine learning discrimination of the middle Eocene radiolarian species Podocyrtis chalara, Podocyrtis goetheana and their morphological intermediates F. Pinto et al.
- New middle Eocene radiolarian species (Rhizaria, Polycystinea) from Blake Nose, subtropical western North Atlantic Ocean M. Meunier & T. Danelian
- The Fossil Frontier: An answer to the 3-billion fossil question I. Martinsen et al.
- Advancing paleontology: a survey on deep learning methodologies in fossil image analysis M. Yaqoob et al.
- A user‐friendly method to get automated pollen analysis from environmental samples B. Gimenez et al.
- TESTING THE BOREAL-TETHYAN SHIFT OF NERINEOID GASTROPODS USING CONVOLUTIONAL NEURAL NETWORKS Y. Leshno Afriat et al.
- Automated detection of microfossil fish teeth from slide images using combined deep learning models K. Mimura et al.
- Artificial intelligence applied to the classification of eight middle Eocene species of the genus Podocyrtis (polycystine radiolaria) V. Carlsson et al.
- Diatom and radiolarian biostratigraphy in the Pliocene sequence of ODP Site 697 (Jane Basin, Atlantic sector of the Southern Ocean) Y. Kato et al.
- Automated identification of fossil benthic foraminifera from the Peruvian margin using convolutional neural networks S. Hayat et al.
19 citations as recorded by crossref.
- Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks X. Liu et al.
- Semantic segmentation of vertebrate microfossils from computed tomography data using a deep learning approach Y. Hou et al.
- Towards a Fleet of Robots for Orientation, Imaging, and Morphometric Analyses of Planktonic Foraminifera M. Knappertsbusch & J. Eisenecker
- Diversity of polycystine radiolarians in sediment traps from the Ionian, North Aegean and Cretan Seas: A preliminary account T. Danelian et al.
- No dramatic changes observed in subtropical radiolarian plankton assemblages during the Middle Eocene Climatic Optimum (MECO); evidence from the North Atlantic ODP Site 1051 M. Meunier & T. Danelian
- Addressing the “open world”: detecting and segmenting pollen on palynological slides with deep learning J. Feng et al.
- Convolutional neural network application on a new middle Eocene radiolarian dataset V. Carlsson et al.
- Deep learning object detection for fossil diatom counting: assessing the impact of fossil preservation and intraspecific morphological variation S. Ishino et al.
- Merging databases for CNN image recognition, increasing bias or improving results? M. Tetard et al.
- Morphometrics and machine learning discrimination of the middle Eocene radiolarian species Podocyrtis chalara, Podocyrtis goetheana and their morphological intermediates F. Pinto et al.
- New middle Eocene radiolarian species (Rhizaria, Polycystinea) from Blake Nose, subtropical western North Atlantic Ocean M. Meunier & T. Danelian
- The Fossil Frontier: An answer to the 3-billion fossil question I. Martinsen et al.
- Advancing paleontology: a survey on deep learning methodologies in fossil image analysis M. Yaqoob et al.
- A user‐friendly method to get automated pollen analysis from environmental samples B. Gimenez et al.
- TESTING THE BOREAL-TETHYAN SHIFT OF NERINEOID GASTROPODS USING CONVOLUTIONAL NEURAL NETWORKS Y. Leshno Afriat et al.
- Automated detection of microfossil fish teeth from slide images using combined deep learning models K. Mimura et al.
- Artificial intelligence applied to the classification of eight middle Eocene species of the genus Podocyrtis (polycystine radiolaria) V. Carlsson et al.
- Diatom and radiolarian biostratigraphy in the Pliocene sequence of ODP Site 697 (Jane Basin, Atlantic sector of the Southern Ocean) Y. Kato et al.
- Automated identification of fossil benthic foraminifera from the Peruvian margin using convolutional neural networks S. Hayat et al.
Saved (final revised paper)
Latest update: 30 Apr 2026
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.
Radiolarians are marine micro-organisms that produce a siliceous shell that is preserved in the...
