The rise of data-driven microscopy powered by machine learning
Morgado, Leonor ; Gómez-de-Mariscal, Estibaliz ; Heil, Hannah S LU
and Henriques, Ricardo
(2024)
In Journal of Microscopy
295(2).
p.85-92
- Abstract
Optical microscopy is an indispensable tool in life sciences research, but conventional techniques require compromises between imaging parameters like speed, resolution, field of view and phototoxicity. To overcome these limitations, data-driven microscopes incorporate feedback loops between data acquisition and analysis. This review overviews how machine learning enables automated image analysis to optimise microscopy in real time. We first introduce key data-driven microscopy concepts and machine learning methods relevant to microscopy image analysis. Subsequently, we highlight pioneering works and recent advances in integrating machine learning into microscopy acquisition workflows, including optimising illumination, switching... (More)
Optical microscopy is an indispensable tool in life sciences research, but conventional techniques require compromises between imaging parameters like speed, resolution, field of view and phototoxicity. To overcome these limitations, data-driven microscopes incorporate feedback loops between data acquisition and analysis. This review overviews how machine learning enables automated image analysis to optimise microscopy in real time. We first introduce key data-driven microscopy concepts and machine learning methods relevant to microscopy image analysis. Subsequently, we highlight pioneering works and recent advances in integrating machine learning into microscopy acquisition workflows, including optimising illumination, switching modalities and acquisition rates, and triggering targeted experiments. We then discuss the remaining challenges and future outlook. Overall, intelligent microscopes that can sense, analyse and adapt promise to transform optical imaging by opening new experimental possibilities.
(Less)
- author
- Morgado, Leonor
; Gómez-de-Mariscal, Estibaliz
; Heil, Hannah S
LU
and Henriques, Ricardo
- publishing date
- 2024-08
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Journal of Microscopy
- volume
- 295
- issue
- 2
- pages
- 85 - 92
- publisher
- Wiley-Blackwell
- external identifiers
-
- scopus:85187185195
- pmid:38445705
- ISSN
- 0022-2720
- DOI
- 10.1111/jmi.13282
- language
- English
- LU publication?
- no
- additional info
- © 2024 The Authors. Journal of Microscopy published by John Wiley & Sons Ltd on behalf of Royal Microscopical Society.
- id
- fa311a8f-3692-4f72-b437-9ce221747374
- date added to LUP
- 2025-04-07 16:07:17
- date last changed
- 2025-07-29 12:32:45
@article{fa311a8f-3692-4f72-b437-9ce221747374,
abstract = {{<p>Optical microscopy is an indispensable tool in life sciences research, but conventional techniques require compromises between imaging parameters like speed, resolution, field of view and phototoxicity. To overcome these limitations, data-driven microscopes incorporate feedback loops between data acquisition and analysis. This review overviews how machine learning enables automated image analysis to optimise microscopy in real time. We first introduce key data-driven microscopy concepts and machine learning methods relevant to microscopy image analysis. Subsequently, we highlight pioneering works and recent advances in integrating machine learning into microscopy acquisition workflows, including optimising illumination, switching modalities and acquisition rates, and triggering targeted experiments. We then discuss the remaining challenges and future outlook. Overall, intelligent microscopes that can sense, analyse and adapt promise to transform optical imaging by opening new experimental possibilities.</p>}},
author = {{Morgado, Leonor and Gómez-de-Mariscal, Estibaliz and Heil, Hannah S and Henriques, Ricardo}},
issn = {{0022-2720}},
language = {{eng}},
number = {{2}},
pages = {{85--92}},
publisher = {{Wiley-Blackwell}},
series = {{Journal of Microscopy}},
title = {{The rise of data-driven microscopy powered by machine learning}},
url = {{http://dx.doi.org/10.1111/jmi.13282}},
doi = {{10.1111/jmi.13282}},
volume = {{295}},
year = {{2024}},
}