Instance Segmentation of Microscopic Foraminifera
1
Department of Mathematics and Statistics, UiT The Arctic University of Norway, 9019 Tromsø, Norway
2
Department of Geosciences, UiT The Arctic University of Norway, 9019 Tromsø, Norway
3
Department of Community Medicine, UiT The Arctic University of Norway, 9019 Tromsø, Norway
*
Author to whom correspondence should be addressed.
Academic Editor: Dibyendu Sarkar
Appl. Sci. 2021, 11(14), 6543; https://doi.org/10.3390/app11146543
Received: 24 May 2021
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Revised: 9 July 2021
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Accepted: 11 July 2021
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Published: 16 July 2021
Foraminifera are single-celled marine organisms that construct shells that remain as fossils in the marine sediments. Classifying and counting these fossils are important in paleo-oceanographic and -climatological research. However, the identification and counting process has been performed manually since the 1800s and is laborious and time-consuming. In this work, we present a deep learning-based instance segmentation model for classifying, detecting, and segmenting microscopic foraminifera. Our model is based on the Mask R-CNN architecture, using model weight parameters that have learned on the COCO detection dataset. We use a fine-tuning approach to adapt the parameters on a novel object detection dataset of more than 7000 microscopic foraminifera and sediment grains. The model achieves a (COCO-style) average precision of
Keywords:
foraminifera; instance segmentation; object detection; deep learning
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MDPI and ACS Style
Johansen, T.H.; Sørensen, S.A.; Møllersen, K.; Godtliebsen, F. Instance Segmentation of Microscopic Foraminifera. Appl. Sci. 2021, 11, 6543. https://doi.org/10.3390/app11146543
AMA Style
Johansen TH, Sørensen SA, Møllersen K, Godtliebsen F. Instance Segmentation of Microscopic Foraminifera. Applied Sciences. 2021; 11(14):6543. https://doi.org/10.3390/app11146543
Chicago/Turabian StyleJohansen, Thomas H., Steffen A. Sørensen, Kajsa Møllersen, and Fred Godtliebsen. 2021. "Instance Segmentation of Microscopic Foraminifera" Applied Sciences 11, no. 14: 6543. https://doi.org/10.3390/app11146543
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