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Towards a digital diatom: image processing and deep learning analysis of Bacillaria paradoxa dynamic morphology

View ORCID ProfileBradly Alicea, Richard Gordon, Thomas Harbich, Ujjwal Singh, Asmit Singh, Vinay Varma
doi: https://doi.org/10.1101/2019.12.21.885897
Bradly Alicea
1OpenWorm Foundation, Boston, MA USA ()
2Orthogonal Research and Education Laboratory, Champaign, IL USA ()
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  • ORCID record for Bradly Alicea
  • For correspondence: bradly.alicea@outlook.com balicea@openworm.org bradly.alicea@outlook.com
Richard Gordon
3Gulf Marine Specimen Laboratory, Panacea, FL USA ()
4Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI USA
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  • For correspondence: DickGordonCan@gmail.com
Thomas Harbich
5Weissach im Tal, Germany ()
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  • For correspondence: mail@thomas-harbich.de
Ujjwal Singh
6IIIT Delhi, Delhi, India (, )
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  • For correspondence: ujjwal18113@iiitd.ac.in asmit18025@iiitd.ac.in
Asmit Singh
6IIIT Delhi, Delhi, India (, )
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  • For correspondence: ujjwal18113@iiitd.ac.in asmit18025@iiitd.ac.in
Vinay Varma
7Amrita Vishwa Vidyapeetham University, Coimbatore, India ()
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  • For correspondence: vinay.n.varma189@gmail.com
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Abstract

Recent years have witnessed a convergence of data and methods that allow us to approximate the shape, size, and functional attributes of biological organisms. This is not only limited to traditional model species: given the ability to culture and visualize a specific organism, we can capture both its structural and functional attributes. We present a quantitative model for the colonial diatom Bacillaria paradoxa, an organism that presents a number of unique attributes in terms of form and function. To acquire a digital model of B. paradoxa, we extract a series of quantitative parameters from microscopy videos from both primary and secondary sources. These data are then analyzed using a variety of techniques, including two rival deep learning approaches. We provide an overview of neural networks for non-specialists as well as present a series of analysis on Bacillaria phenotype data. The application of deep learning networks allows for two analytical purposes. Application of the DeepLabv3 pre-trained model extracts phenotypic parameters describing the shape of cells constituting Bacillaria colonies. Application of a semantic model trained on nematode embryogenesis data (OpenDevoCell) provides a means to analyze masked images of potential intracellular features. We also advance the analysis of Bacillaria colony movement dynamics by using templating techniques and biomechanical analysis to better understand the movement of individual cells relative to an entire colony. The broader implications of these results are presented, with an eye towards future applications to both hypothesis-driven studies and theoretical advancements in understanding the dynamic morphology of Bacillaria.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • This version reflects revisions that correct minor mistakes, add additional references, and clarify a few sections of the text (including figures).

  • https://osf.io/ar8c3/

  • https://github.com/devoworm/Digital-Bacillaria

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 05, 2020.
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Towards a digital diatom: image processing and deep learning analysis of Bacillaria paradoxa dynamic morphology
Bradly Alicea, Richard Gordon, Thomas Harbich, Ujjwal Singh, Asmit Singh, Vinay Varma
bioRxiv 2019.12.21.885897; doi: https://doi.org/10.1101/2019.12.21.885897
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Towards a digital diatom: image processing and deep learning analysis of Bacillaria paradoxa dynamic morphology
Bradly Alicea, Richard Gordon, Thomas Harbich, Ujjwal Singh, Asmit Singh, Vinay Varma
bioRxiv 2019.12.21.885897; doi: https://doi.org/10.1101/2019.12.21.885897

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