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arXiv · 2601.10392

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

Hassan Eshkiki·Sarah Costa·Mostafa Mohammadpour·Farinaz Tanhaei·Christopher H. George·Fabio Caraffini
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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.

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Hassan Eshkiki, Sarah Costa, Mostafa Mohammadpour, Farinaz Tanhaei, Christopher H. George, Fabio Caraffini. 2026-01-15. Multi-Temporal Frames Projection for Dynamic Processes Fusion in Fluorescence Microscopy. https://arxiv.org/abs/2601.10392

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