Scientific Reports· 2026Q1
Advanced background removal methods in single molecule localization microscopy using scattering networks and SVD
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- Q1SCImago
- 2026year
Short summary
A novel neural network approach combined with Singular Value Decomposition (SVD) significantly improves background removal in single-molecule localization microscopy (SMLM), reducing artifacts like false positives and merged PSFs.
AI-generated from the title and abstract; the full text is not read.
Key points
- Two refined methods (SVD filters and frequency-based filters) are proposed for SMLM background removal.
- A novel neural network approach is introduced to enhance background removal capabilities.
- The methods effectively mitigate artifacts including false positives, false negatives, merged PSFs, and spurious localizations.
- Evaluations using the Jaccard index (JI) demonstrate improved localization performance.
AI-generated from the title and abstract; the full text is not read.
Abstract
Accurate background estimation is a key challenge in single-molecule localization microscopy (SMLM), as it directly affects the quality of molecular localization and sample reconstruction. A coarse separation between background and relevant signal can often be obtained by contrasting spatial or temporal characteristics of the raw input. In this paper, we propose and compare two refined methods aimed at separating two types of background: (1) those where the variation over time occurs at a slower rate than the signal, and (2) backgrounds with distinctive spatial features. Filters that take advantage of Singular Value Decomposition (SVD) effectively address the first type of background, while the second can be managed using frequency-based filters. We introduce a novel approach based on neural networks to enhance background removal. Comparative evaluations using the Jaccard index (JI) demonstrate that the two methods improve localization performance, showing effective mitigation of background artifacts such as false positives or negatives, merged PSFs, and spurious localizations in SMLM data.
The authors' abstract, as published at the source. Scientific Reports, 2026 · DOI ↗
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BiophysicsBiochemistry, Genetics and Molecular Biology