  Semantic segmentation of surgical instruments plays a crucial role in
robot-assisted surgery. However, accurate segmentation of cataract surgical
instruments is still a challenge due to specular reflection and class imbalance
issues. In this paper, an attention-guided network is proposed to segment the
cataract surgical instrument. A new attention module is designed to learn
discriminative features and address the specular reflection issue. It captures
global context and encodes semantic dependencies to emphasize key semantic
features, boosting the feature representation. This attention module has very
few parameters, which helps to save memory. Thus, it can be flexibly plugged
into other networks. Besides, a hybrid loss is introduced to train our network
for addressing the class imbalance issue, which merges cross entropy and
logarithms of Dice loss. A new dataset named Cata7 is constructed to evaluate
our network. To the best of our knowledge, this is the first cataract surgical
instrument dataset for semantic segmentation. Based on this dataset, RAUNet
achieves state-of-the-art performance 97.71% mean Dice and 95.62% mean IOU.
