Morphology exploration of pollen using deep learning latent space
Morphology exploration of pollen using deep learning latent space
The structure of pollen has evolved depending on its local environment, competition, and ecology. As pollen grains are generally of size 10–100 microns with nanometre-scale substructure, scanning electron microscopy is an important microscopy technique for imaging and analysis. Here, we use style transfer deep learning to allow exploration of latent w-space of scanning electron microscope images of pollen grains and show the potential for using this technique to understand evolutionary pathways and characteristic structural traits of pollen grains.
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Zervas, Michael N.
1840a474-dd50-4a55-ab74-6f086aa3f701
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
2 January 2023
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Zervas, Michael N.
1840a474-dd50-4a55-ab74-6f086aa3f701
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
Grant-Jacob, James, Zervas, Michael N. and Mills, Benjamin
(2023)
Morphology exploration of pollen using deep learning latent space.
IOP SciNotes, [044602].
(doi:10.1088/2633-1357/acadb9).
Abstract
The structure of pollen has evolved depending on its local environment, competition, and ecology. As pollen grains are generally of size 10–100 microns with nanometre-scale substructure, scanning electron microscopy is an important microscopy technique for imaging and analysis. Here, we use style transfer deep learning to allow exploration of latent w-space of scanning electron microscope images of pollen grains and show the potential for using this technique to understand evolutionary pathways and characteristic structural traits of pollen grains.
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IOP_SciNotes_Evolution_Submitted_Corrected_Final_Accepted_18112022_Accepted
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Grant-Jacob_2022_IOP_SciNotes_3_044602
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Accepted/In Press date: 21 December 2022
e-pub ahead of print date: 2 January 2023
Published date: 2 January 2023
Identifiers
Local EPrints ID: 473719
URI: http://eprints.soton.ac.uk/id/eprint/473719
ISSN: 2633-1357
PURE UUID: 77fea6f3-948f-450c-bd2d-137cddd18baf
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Date deposited: 30 Jan 2023 19:12
Last modified: 17 Mar 2024 03:22
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