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

• URL: http://arxiv.org/abs/2309.17265v1
• Date: Fri, 29 Sep 2023 14:17:31 GMT
• Title: Effect of structure-based training on 3D localization precision and quality
• Authors: Armin Abdehkakha, Craig Snoeyink
• Abstract summary: This study introduces a structural-based training approach for CNN-based algorithms in single-molecule localization microscopy. We compare this approach with the traditional random-based training method, utilizing the LUENN package as our AI pipeline. Our findings highlight the potential of the structural-based training approach to advance super-resolution microscopy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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