  Semantic segmentation in surgical videos is a prerequisite for a broad range
of applications towards improving surgical outcomes and surgical video
analysis. However, semantic segmentation in surgical videos involves many
challenges. In particular, in cataract surgery, various features of the
relevant objects such as blunt edges, color and context variation, reflection,
transparency, and motion blur pose a challenge for semantic segmentation. In
this paper, we propose a novel convolutional module termed as \textit{ReCal}
module, which can calibrate the feature maps by employing region
intra-and-inter-dependencies and channel-region cross-dependencies. This
calibration strategy can effectively enhance semantic representation by
correlating different representations of the same semantic label, considering a
multi-angle local view centering around each pixel. Thus the proposed module
can deal with distant visual characteristics of unique objects as well as
cross-similarities in the visual characteristics of different objects.
Moreover, we propose a novel network architecture based on the proposed module
termed as ReCal-Net. Experimental results confirm the superiority of ReCal-Net
compared to rival state-of-the-art approaches for all relevant objects in
cataract surgery. Moreover, ablation studies reveal the effectiveness of the
ReCal module in boosting semantic segmentation accuracy.
