Bibliographic Details
| Title: |
Fourier Ring Correlation and Anisotropic Kernel Density Estimation Improve Deep Learning Based SMLM Reconstruction of Microtubules |
| Authors: |
Andreas Berberich, Andreas Kurz, Sebastian Reinhard, Torsten Johann Paul, Paul Ray Burd, Markus Sauer, Philip Kollmannsberger |
| Source: |
Frontiers in Bioinformatics, Vol 1 (2021) |
| Publisher Information: |
Frontiers Media S.A., 2021. |
| Publication Year: |
2021 |
| Collection: |
LCC:Computer applications to medicine. Medical informatics |
| Subject Terms: |
dSTORM, deep learning–artificial neural network (DL-ANN), single molecule localization microscopy, microtubule cytoskeleton, super-resolution, Computer applications to medicine. Medical informatics, R858-859.7 |
| More Details: |
Single-molecule super-resolution microscopy (SMLM) techniques like dSTORM can reveal biological structures down to the nanometer scale. The achievable resolution is not only defined by the localization precision of individual fluorescent molecules, but also by their density, which becomes a limiting factor e.g., in expansion microscopy. Artificial deep neural networks can learn to reconstruct dense super-resolved structures such as microtubules from a sparse, noisy set of data points. This approach requires a robust method to assess the quality of a predicted density image and to quantitatively compare it to a ground truth image. Such a quality measure needs to be differentiable to be applied as loss function in deep learning. We developed a new trainable quality measure based on Fourier Ring Correlation (FRC) and used it to train deep neural networks to map a small number of sampling points to an underlying density. Smooth ground truth images of microtubules were generated from localization coordinates using an anisotropic Gaussian kernel density estimator. We show that the FRC criterion ideally complements the existing state-of-the-art multiscale structural similarity index, since both are interpretable and there is no trade-off between them during optimization. The TensorFlow implementation of our FRC metric can easily be integrated into existing deep learning workflows. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2673-7647 |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fbinf.2021.752788/full; https://doaj.org/toc/2673-7647 |
| DOI: |
10.3389/fbinf.2021.752788 |
| Access URL: |
https://doaj.org/article/cbcaefb45310429db6323f8f4c313dd6 |
| Accession Number: |
edsdoj.bcaefb45310429db6323f8f4c313dd6 |
| Database: |
Directory of Open Access Journals |
