A lightweight hierarchical feature fusion network for surgical instrument segmentation in internet of medical things

Abstract

Minimally invasive surgeries (MIS) enhance patient outcomes but pose challenges such as limited visibility, complex hand-eye coordination, and manual endoscope control. The rise of the Internet of Medical Things (IoMT) and telesurgery further demands efficient and lightweight solutions. To address these limitations, we propose a novel lightweight hierarchical feature fusion network (LHFF-Net) for surgical instrument segmentation. LHFF-Net integrates high-, mid-, and low-level encoder features through three novel modules: the multiscale feature aggregation (MFA) module which can capture fine-grained and coarse features across scales, the enhanced spatial attention (ESA) module, prioritizing critical spatial regions, and the enhanced edge module (EEM), refining boundary delineation. The proposed model was evaluated on two benchmark datasets, Kvasir-Instrument and UW-Sinus-Surgery, achieving mean Dice coefficients (mDC) of 97.87 % and 88.83 %, respectively, along with mean intersection over union (mIOU) scores of 95.87 % and 84.33 %. These results highlight LHFF-Net's ability to deliver high segmentation accuracy while maintaining computational efficiency with only 2.2 million parameters. This combination of performance and efficiency makes LHFF-Net a robust solution for IoMT applications, enabling real-time telesurgery and driving innovations in healthcare.