Deep-learning-assisted SICM for enhanced real-time imaging of nanoscale biological dynamics

Scanning Ion Conductance Microscopy (SICM) provides high-resolution, nanoscale imaging of living cells, but it is generally limited by a slow scan rate, making it challenging to capture dynamic processes in real time. To tackle this challenge, we propose an integrated data acquisition and computational framework that improves the temporal resolution of SICM by selectively skipping certain scan lines. A partial convolutional neural network (Partial-CNN) model is developed and trained on SICM images and their corresponding masks to reconstruct the complete images from the under-sampled data, ensuring the retention of structural integrity. This approach significantly reduces the image acquisition time (i.e., by 30-60%) without compromising quality, as validated through multiple quantitative metrics. Compared to conventional deep learning methods, the Partial-CNN demonstrates higher accuracy in reconstructing fine details and maintaining consistent height maps across skipped regions. We show that this method provides an increased temporal resolution and retains image fidelity, making it suitable for real-time dynamic SICM imaging and improving the smart scanning microscopy applications in time-resolved biological imaging.