Stable distance regression via spatial-frequency state space model for robot-assisted endomicroscopy.

Purpose: Probe-based confocal laser endomicroscopy (pCLE) is a noninvasive technique that enables the direct visualization of tissue at a microscopic level in real time. One of the main challenges in using pCLE is maintaining the probe within a working range of micrometer scale. As a result, the need arises for automatically regressing the probe-tissue distance to enable precise robotic tissue scanning.

Methods: In this paper, we propose the spatial frequency bidirectional structured state space model (SF-BiS4D) for pCLE probe-tissue distance regression. This model advances traditional state space models by processing image sequences bidirectionally and analyzing data in both the frequency and spatial domains. Additionally, we introduce a guided trajectory planning strategy that generates pseudo-distance labels, facilitating the training of sequential models to generate smooth and stable robotic scanning trajectories. To improve inference speed, we also implement a hierarchical guided fine-tuning (GF) approach that efficiently reduces the size of the BiS4D model while maintaining performance.

Results: The performance of our proposed model has been evaluated both qualitatively and quantitatively using the pCLE regression dataset (PRD). In comparison with existing state-of-the-art (SOTA) methods, our approach demonstrated superior performance in terms of accuracy and stability.

Conclusion: Our proposed deep learning-based framework effectively improves distance regression for microscopic visual servoing and demonstrates its potential for integration into surgical procedures requiring precise real-time intraoperative imaging.