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Sub-diffraction error mapping for localisation microscopy images
Richard J Marsh1, Ishan Costello1, Mark-Alexander Gorey1
1Randall Centre for Cell & Molecular Biophysics, Guy's Campus, King's College London, London, UK.
Nature Communications
|September 24, 2021
Summary
Assessing super-resolution microscopy image quality is difficult, especially with overlapping emitters. A new method, HAWKMAN, uses Haar wavelet kernel analysis (HAWK) to reliably map and quantify reconstruction biases and artefacts in crowded fields.
Area of Science:
- Super-resolution microscopy
- Image analysis
- Biophysics
Background:
- Assessing localisation microscopy image quality is challenging.
- Emitter overlap in live-cell imaging causes common algorithmic biases and errors.
- Existing methods struggle with high emitter densities.
Purpose of the Study:
- To develop a general approach for assessing the reliability of localisation microscopy data.
- To map and quantify reconstruction bias and artefacts.
- To overcome limitations of intensity-based comparisons.
Main Methods:
- Utilized Haar wavelet kernel analysis (HAWK) to generate an unbiased reference image.
- Applied HAWK to localisation microscopy data analysis.
- Developed the HAWK Method for the Assessment of Nanoscopy (HAWKMAN).
Main Results:
- HAWKMAN enables unbiased mapping and quantification of reconstruction bias and artefacts.
- The method is effective even with significant emitter overlap (crowded fields).
- Structural artefacts can be mapped independently of localisation algorithm nonlinearity.
Conclusions:
- HAWKMAN provides a general and reliable method for assessing localisation microscopy data quality.
- This approach is crucial for accurate interpretation of live-cell imaging data.
- The method enhances the trustworthiness of super-resolution microscopy results.
