  Segmentation of surgical instruments is an important problem in
robot-assisted surgery: it is a crucial step towards full instrument pose
estimation and is directly used for masking of augmented reality overlays
during surgical procedures. Most applications rely on accurate real-time
segmentation of high-resolution surgical images. While previous research
focused primarily on methods that deliver high accuracy segmentation masks,
majority of them can not be used for real-time applications due to their
computational cost. In this work, we design a light-weight and highly-efficient
deep residual architecture which is tuned to perform real-time inference of
high-resolution images. To account for reduced accuracy of the discovered
light-weight deep residual network and avoid adding any additional
computational burden, we perform a differentiable search over dilation rates
for residual units of our network. We test our discovered architecture on the
EndoVis 2017 Robotic Instruments dataset and verify that our model is the
state-of-the-art in terms of speed and accuracy tradeoff with a speed of up to
125 FPS on high resolution images.
