CellINR: Implicitly Overcoming Photo-induced Artifacts in 4D Live Fluorescence Microscopy

2025-08-25

AI Summary

In 4D live-cell fluorescence microscopy, prolonged intense illumination induces photobleaching and phototoxicity, introducing artifacts that disrupt structural continuity and impede dynamic quantitative analysis. To address this, we propose CellINR—a novel framework that pioneers the application of implicit neural representations (INRs) to single-sample blind deconvolution optimization. CellINR jointly employs high-frequency spatial mapping and structure-aware enhancement to decouple and reconstruct authentic cellular signals from optical artifacts. We introduce and publicly release the first paired 4D live-cell imaging dataset, enabling rigorous quantitative evaluation. Experiments demonstrate that CellINR significantly outperforms state-of-the-art methods in artifact suppression, 3D structural detail recovery, and temporal coherence across time-lapse sequences. This work establishes a new paradigm for high-fidelity 4D dynamic imaging and facilitates robust downstream biological quantification.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Representation Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

4D live fluorescence microscopy is often compromised by prolonged high intensity illumination which induces photobleaching and phototoxic effects that generate photo-induced artifacts and severely impair image continuity and detail recovery. To address this challenge, we propose the CellINR framework, a case-specific optimization approach based on implicit neural representation. The method employs blind convolution and structure amplification strategies to map 3D spatial coordinates into the high frequency domain, enabling precise modeling and high-accuracy reconstruction of cellular structures while effectively distinguishing true signals from artifacts. Experimental results demonstrate that CellINR significantly outperforms existing techniques in artifact removal and restoration of structural continuity, and for the first time, a paired 4D live cell imaging dataset is provided for evaluating reconstruction performance, thereby offering a solid foundation for subsequent quantitative analyses and biological research. The code and dataset will be public.

Problem

• Overcoming photobleaching and phototoxic artifacts
• Restoring image continuity in microscopy
• Distinguishing true signals from artifacts

Innovation

• Implicit neural representation for artifact removal
• Blind convolution and structure amplification strategies
• Mapping spatial coordinates to high frequency domain