EISegNet: Enhancing Instrument Segmentation Network via Dual-View Disparity Estimation.

Abstract

Accurate segmentation of endoscopic instruments is essential in robot-assisted surgery, supporting precis enavigation, enhancing safety, and advancing surgical automation. However, this task is challenging due to factors like complex environments, instrument-tissue similarity, and lighting variations. Instruments, due to their material properties, have distinct depth distributions compared to surrounding tissues. This aspect is often overlooked in monocular video segmentation methods.To address this issue, we propose EISegNet, a multi-task framework that prioritizes instrument segmentation with an auxiliary disparity estimation task. The framework integrates an asymmetric cross-attention mechanism to enhance segmentation performance by fusing features from both tasks. Moreover, by leveraging the geometric properties of motion, EISegNet adapts the stereo disparity estimation strategy for dual-view depth estimation, broadening its applicability to various endoscopic surgeries beyond laparoscopic procedures. Furthermore, EISegNet incorporates a Gaussian-weighted loss function to emphasize edge features, which are particularly challenging for disparity estimation. This function reduces overall loss and improves segmentation accuracy. Extensive cross-dataset experiments demonstrate the superior accuracy and generalization of our method, achieving a 5.97% increase in IoU (Intersection over Union). Qualitative evaluations on clinical datasets further demonstrate the promising performance in real-world scenarios.