Image enhancement network based on fiber topology-preserving multi-module fusion for neuron reconstruction.

The rapid development of optical labeling and imaging technologies has enabled scientists to capture three-dimensional images of mammalian brains at the single-neuron level. However, it has also brought about numerous technical challenges. In neuronal images, fibers exhibit lower fluorescence intensity than cell bodies and are densely packed, making it difficult to distinguish fiber signals from background noise or resolve connectivity accurately. While sparse high-brightness labeling and sensitive imaging technologies have partially addressed these issues, they have not been fundamentally resolved. This study focused on data post-processing and proposed an image enhancement method using a fiber topology-preserving multi-module fusion network. By incorporating a self-attention mechanism and a topology-preserving loss function, the method enhanced the signal-to-noise ratio and continuity of neuronal fibers. Applied to the three-dimensional reconstruction of complex fiber structures, it significantly improved the performance of existing algorithms, offering an effective technical approach for precise neuronal fiber structure analysis.