Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy

TL;DR

This paper introduces a deep learning system that assesses the quality of super-resolution microscopy images, aiming to match expert evaluations and improve non-expert usability.

Contribution

A novel deep neural network model for quantifying super-resolution microscopy image quality based on expert-provided scores.

Findings

The neural network's quality predictions align well with human expert assessments.

The approach demonstrates potential but also reveals some limitations.

User study validates the model's effectiveness in real-world scenarios.

Abstract

With super-resolution optical microscopy, it is now possible to observe molecular interactions in living cells. The obtained images have a very high spatial precision but their overall quality can vary a lot depending on the structure of interest and the imaging parameters. Moreover, evaluating this quality is often difficult for non-expert users. In this work, we tackle the problem of learning the quality function of super- resolution images from scores provided by experts. More specifically, we are proposing a system based on a deep neural network that can provide a quantitative quality measure of a STED image of neuronal structures given as input. We conduct a user study in order to evaluate the quality of the predictions of the neural network against those of a human expert. Results show the potential while highlighting some of the limits of the proposed approach.