  Image-based tracking of laparoscopic instruments plays a fundamental role in
computer and robotic-assisted surgeries by aiding surgeons and increasing
patient safety. Computer vision contests, such as the Robust Medical Instrument
Segmentation (ROBUST-MIS) Challenge, seek to encourage the development of
robust models for such purposes, providing large, diverse, and annotated
datasets. To date, most of the existing models for instance segmentation of
medical instruments were based on two-stage detectors, which provide robust
results but are nowhere near to the real-time (5 frames-per-second (fps)at
most). However, in order for the method to be clinically applicable, real-time
capability is utmost required along with high accuracy. In this paper, we
propose the addition of attention mechanisms to the YOLACT architecture that
allows real-time instance segmentation of instrument with improved accuracy on
the ROBUST-MIS dataset. Our proposed approach achieves competitive performance
compared to the winner ofthe 2019 ROBUST-MIS challenge in terms of robustness
scores,obtaining 0.313 MI_DSC and 0.338 MI_NSD, while achieving real-time
performance (37 fps)
