DeepCEL0 for 2D Single Molecule Localization in Fluorescence Microscopy

TL;DR

DeepCEL0 is a novel deep learning algorithm that enhances high-density molecule localization in fluorescence microscopy, outperforming existing methods in speed and precision by using a regularized loss function with positivity and $ ext{L}_0$ constraints.

Contribution

This work introduces DeepCEL0, a parameter-free deep learning method that incorporates $ ext{L}_0$ regularization for improved molecule localization in high-density SMLM data.

Findings

DeepCEL0 achieves higher localization accuracy than state-of-the-art methods.

The approach is faster and more flexible than existing algorithms.

Validated on both simulated and real microscopy data.

Abstract

In fluorescence microscopy, Single Molecule Localization Microscopy (SMLM) techniques aim at localizing with high precision high density fluorescent molecules by stochastically activating and imaging small subsets of blinking emitters. Super Resolution (SR) plays an important role in this field since it allows to go beyond the intrinsic light diffraction limit. In this work, we propose a deep learning-based algorithm for precise molecule localization of high density frames acquired by SMLM techniques whose $\ell_{2}$-based loss function is regularized by positivity and $\ell_{0}$-based constraints. The $\ell_{0}$ is relaxed through its Continuous Exact $\ell_{0}$ (CEL0) counterpart. The arising approach, named DeepCEL0, is parameter-free, more flexible, faster and provides more precise molecule localization maps if compared to the other state-of-the-art methods. We validate our approach on both simulated and real fluorescence microscopy data.