Multi-Temporal Frames Projection for Dynamic Processes Fusion in Fluorescence Microscopy

TL;DR

This paper introduces a novel computational framework that fuses multiple time-resolved fluorescence microscopy frames into a single high-quality image, improving analysis of dynamic biological samples by reducing noise and variability.

Contribution

The proposed method uniquely combines explainable techniques from various computer vision fields to enhance image quality and biological information preservation in fluorescence microscopy.

Findings

44% average increase in cell count compared to previous methods

Effective in handling dynamic, heterogeneous, and complex biological samples

Applicable to other imaging domains requiring multi-temporal image fusion

Abstract

Fluorescence microscopy is widely employed for the analysis of living biological samples; however, the utility of the resulting recordings is frequently constrained by noise, temporal variability, and inconsistent visualisation of signals that oscillate over time. We present a unique computational framework that integrates information from multiple time-resolved frames into a single high-quality image, while preserving the underlying biological content of the original video. We evaluate the proposed method through an extensive number of configurations (n = 111) and on a challenging dataset comprising dynamic, heterogeneous, and morphologically complex 2D monolayers of cardiac cells. Results show that our framework, which consists of a combination of explainable techniques from different computer vision application fields, is capable of generating composite images that preserve and enhance the quality and information of individual microscopy frames, yielding 44% average increase in cell count compared to previous methods. The proposed pipeline is applicable to other imaging domains that require the fusion of multi-temporal image stacks into high-quality 2D images, thereby facilitating annotation and downstream segmentation.