DeepMoCo: graph neural network-based adaptive correction for motion artifacts in fluorescence microscopy

Fluorescence microscopy has emerged as an indispensable tool in neuroscience research, enabling subcellular-resolution imaging of neural circuits and monitoring of brain function in vivo. However, physiological motion artifacts which include both rigid displacements and non-rigid deformations induced by respiration and tissue dynamics, significantly compromise imaging fidelity and quantitative analysis reliability. In this paper, we propose DeepMoCo, an innovative deep learning-based framework that integrates a graph neural network (GNN) to establish spatiotemporal feature correlations between biological motion patterns and imaging artifacts. This architecture enables rapid tracking and adaptive correction of complex motion patterns, achieving 95% improvement in correlation coefficients and 77% enhancement in peak signal-to-noise ratio compared to existing methods. The proposed method presents a robust and universal solution for high-precision in vivo fluorescence imaging studies.