TY  - JOUR
A1  - Berberich, Andreas
A1  - Kurz, Andreas
A1  - Reinhard, Sebastian
A1  - Paul, Torsten Johann
A1  - Burd, Paul Ray
A1  - Sauer, Markus
A1  - Kollmannsberger, Philip
T1  - Fourier Ring Correlation and anisotropic kernel density estimation improve deep learning based SMLM reconstruction of microtubules
T2  - Frontiers in Bioinformatics
N2  - 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.
KW  - dSTORM
KW  - deep learning–artificial neural network (DL-ANN)
KW  - single molecule localization microscopy
KW  - microtubule cytoskeleton
KW  - super-resolution
Y1  - 2021
UR  - https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/26168
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-261686
VL  - 1
ER  -