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

arXiv: https://arxiv.org/abs/2309.17265 · HTML: https://mlsanity.com/paper/2309.17265/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 1 of 1)

- [Localization of Ultra-dense Emitters with Neural Networks](https://mlsanity.com/paper/2305.05542/) (2023)

## Abstract (arXiv)

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.