STFMamba: A Neurocognitive-Inspired Dual-Path State Space Model for Surgical Phase Recognition

Abstract

Surgical phase recognition is critical in computer-assisted surgery. Clinically, surgeons discriminate surgical phases through visuospatial analysis of instrument-tissue interactions. However, existing methods fail to adequately account for the critical role of the visual-neural mechanisms of the surgeons, consequently exhibiting performance limitations in dynamically complex surgical scenarios. Inspired by the information processing mechanism of dual-path visual cognitive neural mechanism, this paper proposes a novel SpatioTemporal Frequency state space modeling method (STFMamba). The method incorporates a frequency-domain balancer designed to capture and balance the low-frequency steady-state features representing tissue backgrounds and the high-frequency transient features representing instrument actions. Concurrently, it employs a dual-path state space model, where information is processed separately through a temporal-frequency path and a temporal-spatial path to decouple local high-frequency dynamic information from global steady-state information. Furthermore, a temporal-frequency guided cross-attention mechanism is introduced to effectively fuse these heterogeneous feature streams. To further enhance model robustness and accuracy, a boundary constraint mechanism is adopted to improve the classification capability for phase boundary frames. This neurocognitively inspired design, which emulates the dual-channel processing strategy of the human visual pathway, substantially enhances the robustness and accuracy of surgical phase recognition. Extensive experiments conducted on the Cholec80, AutoLaparo and CATARACTS datasets validate the superior performance of the proposed method.