ArticleBiomedical optics express2026
CC-DenseSTORM: deep learning enables colorimetry camera-based simultaneous two-color single-molecule localization microscopy with dense emitters.
Article in Biomedical optics express, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Advances in Technology and Applications of Optical Sensing and Imaging for Biomedicine: introduction.Biomedical optics express · 2026Article
- NanorulerQA: quantitative quality analysis of dual-color DNA nanorulers via single-molecule photobleaching step counting and spatio-temporal colocalization.Biomedical optics express · 2026Article
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5 authors.
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Abstract
Colorimetry camera-based single-molecule localization microscopy (CC-STORM) employs a simple optical setup to facilitate the simultaneous imaging of two or more targets at the nanoscale, but it suffers from a high data rejection rate. A recently reported deep learning-based algorithm (called CC-DeepSTORM) reduced the data rejection rate of two-color CC-STORM from 70% to 40%, while achieving crosstalk of 1%. However, when applying this algorithm to regions with dense emitters, it faces challenges with structural artifacts and low detection rates. Here, we propose CC-DenseSTORM, featuring an attention-gated standard-convolution U-Net to eliminate structural artifacts and a dual-channel adaptive classification network for robust dye classification. Simulations demonstrate that, even at a high density of 5 emitters/µm
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