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Biology subjects

Babcock, H. P.

Publications and source records attributed to Babcock, H. P..

2 recordsLinked to original sources
bioRxiv2018

Pixel Quantum Efficiency Differences and Variance Stabilization for sCMOS Single Molecule Localization Microscopy Data Analysis

Optimal analysis of single molecule localization microscopy (SMLM) data acquired with a CMOS camera requires compensation for single pixel differences in gain, offset and readout noise. For some CMOS cameras we found that it is also necessary to compensate for pixel differences in sensitivity or relative quantum efficiency (RQE). We present the modifications to the original sCMOS analysis algorithm necessary to correct for these RQE differences. We also discuss the use of the Anscombe transform (AT) for variance stabilization. Removing the variance dependence on the mean allows simpler least squares fitting approaches to achieve the Cramer-Rao bound on the mixed Poisson and Gaussian distributed data typically acquired with an sCMOS camera.

biophysics↗
bioRxiv2018

DeepLoco: Fast 3D Localization Microscopy Using Neural Networks

Single-molecule localization super-resolution microscopy (SMLM) techniques like STORM and PALM have transformed cellular microscopy by substantially increasing spatial resolution. In this paper we introduce a new algorithm for a critical part of the SMLM process: estimating the number and locations of the fluorophores in a single frame. Our algorithm can analyze a 20000-frame experimental 3D SMLM dataset in about one second -- substantially faster than real-time and existing algorithms. Our approach is straightforward but very different from existing algorithms: we train a neural network to minimize the Bayes risk under a generative model for single SMLM frames. The neural network maps a frame directly to a collection of fluorophore locations, which we compare to the ground truth using a novel loss function. While training the neural network takes several hours, it only has to be done once for a given experimental setup. After training, localizing fluorophores in new images is extremely fast -- orders of magnitude faster than existing algorithms. Faster recovery opens the door to real-time calibration and accelerated acquisition, and future work could tackle more complicated optical systems and more realistic simulators.

biophysics↗