Effect of structure-based training on 3D localization precision and quality

TL;DR

This paper presents a structural-based training method for CNN algorithms in 3D localization microscopy, significantly improving detection accuracy, precision, and artifact removal compared to traditional methods.

Contribution

It introduces a novel structural-based training approach for CNNs in SMLM, outperforming random-based training in detection, precision, and artifact suppression.

Findings

Enhanced detection rate across SNRs

Improved localization precision

Effective removal of checkerboard artifacts

Abstract

This study introduces a structural-based training approach for CNN-based algorithms in single-molecule localization microscopy (SMLM) and 3D object reconstruction. We compare this approach with the traditional random-based training method, utilizing the LUENN package as our AI pipeline. The quantitative evaluation demonstrates significant improvements in detection rate and localization precision with the structural-based training approach, particularly in varying signal-to-noise ratios (SNRs). Moreover, the method effectively removes checkerboard artifacts, ensuring more accurate 3D reconstructions. Our findings highlight the potential of the structural-based training approach to advance super-resolution microscopy and deepen our understanding of complex biological systems at the nanoscale.