Effect of structure-based training on 3D localization precision and quality (2309.17265v1)
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.
- Boyd, N.; Jonas, E.; Babcock, H.; Recht, B. DeepLoco: fast 3D localization microscopy using neural networks. BioRxiv 2018, 267096
- Abdehkakha, A.; Snoeyink, C. Localization of Ultra-dense Emitters with Neural Networks. arXiv preprint arXiv:2305.05542 2023,
- Byth, K.; Ripley, B. On sampling spatial patterns by distance methods. Biometrics 1980, 279–284
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.