  Robot-assisted surgery is an emerging technology which has undergone rapid
growth with the development of robotics and imaging systems. Innovations in
vision, haptics and accurate movements of robot arms have enabled surgeons to
perform precise minimally invasive surgeries. Real-time semantic segmentation
of the robotic instruments and tissues is a crucial step in robot-assisted
surgery. Accurate and efficient segmentation of the surgical scene not only
aids in the identification and tracking of instruments but also provided
contextual information about the different tissues and instruments being
operated with. For this purpose, we have developed a light-weight cascaded
convolutional neural network (CNN) to segment the surgical instruments from
high-resolution videos obtained from a commercial robotic system. We propose a
multi-resolution feature fusion module (MFF) to fuse the feature maps of
different dimensions and channels from the auxiliary and main branch. We also
introduce a novel way of combining auxiliary loss and adversarial loss to
regularize the segmentation model. Auxiliary loss helps the model to learn
low-resolution features, and adversarial loss improves the segmentation
prediction by learning higher order structural information. The model also
consists of a light-weight spatial pyramid pooling (SPP) unit to aggregate rich
contextual information in the intermediate stage. We show that our model
surpasses existing algorithms for pixel-wise segmentation of surgical
instruments in both prediction accuracy and segmentation time of
high-resolution videos.
