Unpaired deep adversarial learning for multi‐class segmentation of instruments in robot‐assisted surgical videos

Abstract

PURPOSE: Image segmentation of instruments in the raw surgical videos is a critical component of intraoperative assistance softwares. Challenges include addressing rendered overlays occluding the instrument while providing pivotal input to instrument tracking frameworks and, train the segmentation process with limited labelled data available from surgical videos. METHOD: The proposed adversarial network, InstruSegNet uses unpaired training (eliminating need for massive paired data) for automated multi-class surgical instrument segmentation in raw surgical videos with complex backgrounds. The proposed method is applied for single/multiple robotic and rigid instruments and optimised on least square Generative Adversarial Networks loss. RESULT: Promising validation has been conducted on the publicly available dataset. Proposed approach for multi-class segmentation of robotic and rigid instruments meets outstanding performance in terms of accuracy and surpasses the existing methods. APPLICATION: This work facilitates segmenting instrument information without manual interventions from raw videos providing means to code surgeon's actions for developing intelligent assistance software.