An enhanced mountain climbing search algorithm to enable fast and accurate autofocusing in high resolution fluorescence microscopy

Accurate and efficient autofocusing is essential for the automation of fluorescence microscopy, but background noise and shallow depth of field at high magnifications make autofocusing particularly challenging. Here, we present a fast and accurate autofocus algorithm to address these challenges. It is highly effective for high-magnification imaging, while performing equally well for low-magnification imaging tasks. The method is based on the mountain climbing search algorithm and yields improvements on autofocusing precision of up to 200-fold over current methods, whilst offering competitive speed and greatly extended search ranges. Our approach is broadly applicable: it demonstrated good stability and reproducibility across magnifications ranging from 20X to 100X, excels in both live cell imaging and high-resolution fixed sample imaging, and it is compatible with various microscopy techniques without the need for fiducial markers or hardware modifications on existing microscopes. To maximise its accessibility, we constructed a user-friendly interface compatible with the widely used Micromanager software. It generalises well across various imaging modalities and hardware platforms, making it particularly suitable for use in high-resolution screening of candidate drugs.

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Publication Details

Journal
Methods and Applications in Fluorescence
Published
2025-08-28
DOI
https://doi.org/10.1088/2050-6120/ae008f
Primary Topic
Image Processing Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

An enhanced mountain climbing search algorithm to enable fast and accurate autofocusing in high resolution fluorescence microscopy

Clemens F. Kaminski, Francesca W. van Tartwijk, Yuetong Jia, Edward Ward et al.
Methods and Applications in Fluorescence
Image Processing Techniques and Applications
article

An enhanced mountain climbing search algorithm to enable fast and accurate autofocusing in high resolution fluorescence microscopy

Clemens F. Kaminski, Francesca W. van Tartwijk, Yuetong Jia, Edward Ward, Yutong Yuan, Yuqing Feng
article en

Abstract

Accurate and efficient autofocusing is essential for the automation of fluorescence microscopy, but background noise and shallow depth of field at high magnifications make autofocusing particularly challenging. Here, we present a fast and accurate autofocus algorithm to address these challenges. It is highly effective for high-magnification imaging, while performing equally well for low-magnification imaging tasks. The method is based on the mountain climbing search algorithm and yields improvements on autofocusing precision of up to 200-fold over current methods, whilst offering competitive speed and greatly extended search ranges. Our approach is broadly applicable: it demonstrated good stability and reproducibility across magnifications ranging from 20X to 100X, excels in both live cell imaging and high-resolution fixed sample imaging, and it is compatible with various microscopy techniques without the need for fiducial markers or hardware modifications on existing microscopes. To maximise its accessibility, we constructed a user-friendly interface compatible with the widely used Micromanager software. It generalises well across various imaging modalities and hardware platforms, making it particularly suitable for use in high-resolution screening of candidate drugs.

Methods and Applications in FluorescenceVol. 13(4)
British Antarctic Survey (GB), University of Cambridge (GB), Bridge University (SS)
Wellcome Trust, Alzheimer’s Research UK, Medical Research Council, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 67%
Image Processing Techniques and Applications
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An enhanced mountain climbing search algorithm to enable fast and accurate autofocusing in high resolution fluorescence microscopy — Clemens F. Kaminski, Francesca W. van Tartwijk, et al. · Methods and Applications in Fluorescence (2025) | TGRS Research Map | TGRS