dorsal/arxiv
View SchemaMulti-Temporal Frames Projection for Dynamic Processes Fusion in Fluorescence Microscopy
| Authors | Hassan Eshkiki, Sarah Costa, Mostafa Mohammadpour, Farinaz Tanhaei, Christopher H. George, Fabio Caraffini |
|---|---|
| Categories | |
| ArXiv ID | 2601.10392vv1 |
| URL | https://arxiv.org/abs/2601.10392 |
| License | http://creativecommons.org/licenses/by/4.0/ |
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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"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.",
"arxiv_id": "2601.10392",
"authors": [
"Hassan Eshkiki",
"Sarah Costa",
"Mostafa Mohammadpour",
"Farinaz Tanhaei",
"Christopher H. George",
"Fabio Caraffini"
],
"categories": [
"cs.CV"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Multi-Temporal Frames Projection for Dynamic Processes Fusion in Fluorescence Microscopy",
"url": "https://arxiv.org/abs/2601.10392",
"version": "v1"
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