2025/01/09 by Mingyang Chen, Luhong Jin, Chen, Mingyang +8
Biochemistry, Genetics and Molecular Biology · #Artificial Intelligence (cs.AI) #Cell Image Analysis Techniques #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Subcellular Processes (q-bio.SC) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2501.05490
openalex publication_date 2025/01/09 · openalex created_date 2025/01/14 · openalex updated_date 2026/07/28
Live-cell imaging of multiple subcellular structures is essential for understanding subcellular dynamics. However, the conventional multi-color sequential fluorescence microscopy suffers from significant imaging delays and limited number of subcellular structure separate labeling, resulting in substantial limitations for real-time live-cell research applications. Here, we present the Adaptive Explainable Multi-Structure Network (AEMS-Net), a deep-learning framework that enables simultaneous prediction of two subcellular structures from a single image. The model normalizes staining intensity and prioritizes critical image features by integrating attention mechanisms and brightness adaptation layers. Leveraging the Kolmogorov-Arnold representation theorem, our model decomposes learned features into interpretable univariate functions, enhancing the explainability of complex subcellular morphologies. We demonstrate that AEMS-Net allows real-time recording of interactions between mitochondria and microtubules, requiring only half the conventional sequential-channel imaging procedures. Notably, this approach achieves over 30% improvement in imaging quality compared to traditional deep learning methods, establishing a new paradigm for long-term, interpretable live-cell imaging that advances the ability to explore subcellular dynamics.