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@article{Boland:2024:10.1111/jmi.13420,
author = {Boland, MA and Lightley, JPE and Garcia, E and Kumar, S and Dunsby, C and Flaxman, S and Neil, MAA and French, PMW and Cohen, EAK},
doi = {10.1111/jmi.13420},
journal = {Journal of Microscopy},
title = {Model-free machine learning-based 3D single molecule localisation microscopy},
url = {http://dx.doi.org/10.1111/jmi.13420},
year = {2024}
}

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TY  - JOUR
AB  - <jats:title>Abstract</jats:title><jats:p>Single molecule localisation microscopy (SMLM) can provide two-dimensional super-resolved image data from conventional fluorescence microscopes, while three dimensional (3D) SMLM usually involves a modification of the microscope, e.g. to engineer a predictable axial variation in the point spread function. Here we demonstrate a 3D SMLM approach (we call<jats:italic>“easyZloc”</jats:italic>) utilising a lightweight Convolutional Neural Network that is generally applicable, including with “standard” (unmodified) fluorescence microscopes, and which we consider may be practically useful in a high throughput SMLM workflow. We demonstrate the reconstruction of nuclear pore complexes with comparable performance to previously reported methods but with a significant reduction in computational power and execution time. 3D reconstructions of the nuclear envelope and a tubulin sample over a larger axial range are also shown.</jats:p>
AU  - Boland,MA
AU  - Lightley,JPE
AU  - Garcia,E
AU  - Kumar,S
AU  - Dunsby,C
AU  - Flaxman,S
AU  - Neil,MAA
AU  - French,PMW
AU  - Cohen,EAK
DO  - 10.1111/jmi.13420
PY  - 2024///
SN  - 0022-2720
TI  - Model-free machine learning-based 3D single molecule localisation microscopy
T2  - Journal of Microscopy
UR  - http://dx.doi.org/10.1111/jmi.13420
UR  - https://doi.org/10.1101/2024.10.14.618179
ER  -

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