  Precise instrument segmentation aid surgeons to navigate the body more easily
and increase patient safety. While accurate tracking of surgical instruments in
real-time plays a crucial role in minimally invasive computer-assisted
surgeries, it is a challenging task to achieve, mainly due to 1) complex
surgical environment, and 2) model design with both optimal accuracy and speed.
Deep learning gives us the opportunity to learn complex environment from large
surgery scene environments and placements of these instruments in real world
scenarios. The Robust Medical Instrument Segmentation 2019 challenge
(ROBUST-MIS) provides more than 10,000 frames with surgical tools in different
clinical settings. In this paper, we use a light-weight single stage instance
segmentation model complemented with a convolutional block attention module for
achieving both faster and accurate inference. We further improve accuracy
through data augmentation and optimal anchor localisation strategies. To our
knowledge, this is the first work that explicitly focuses on both real-time
performance and improved accuracy. Our approach out-performed top team
performances in the ROBUST-MIS challenge with over 44% improvement on both
area-based metric MI_DSC and distance-based metric MI_NSD. We also demonstrate
real-time performance (> 60 frames-per-second) with different but competitive
variants of our final approach.
