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BibTex format

@article{Boland:2025:10.1111/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},
pages = {77--87},
title = {Modelfree machine learningbased 3D single molecule localisation microscopy},
url = {http://dx.doi.org/10.1111/jmi.13420},
volume = {299},
year = {2025}
}

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TY  - JOUR
AB  - <jats:title>Abstract</jats:title>                  <jats:p>                    Single molecule localisation microscopy (SMLM) can provide twodimensional superresolved image data from conventional fluorescence microscopes, while three dimensional (3D) SMLM usually involves a modification of the microscope, for example, 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 an actin 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
EP  - 87
PY  - 2025///
SN  - 0022-2720
SP  - 77
TI  - Modelfree machine learningbased 3D single molecule localisation microscopy
T2  - Journal of Microscopy
UR  - http://dx.doi.org/10.1111/jmi.13420
UR  - https://doi.org/10.1111/jmi.13420
VL  - 299
ER  -

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