Effect of structure-based training on 3D localization precision and
  quality
Armin Abdehkakha Craig Snoeyink
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.