  Fully-automatic execution is the ultimate goal for many Computer Vision
applications. However, this objective is not always realistic in tasks
associated with high failure costs, such as medical applications. For these
tasks, semi-automatic methods allowing minimal effort from users to guide
computer algorithms are often preferred due to desirable accuracy and
performance. Inspired by the practicality and applicability of the
semi-automatic approach, this paper proposes a novel deep neural network
architecture, namely SideInfNet that effectively integrates features learnt
from images with side information extracted from user annotations. To evaluate
our method, we applied the proposed network to three semantic segmentation
tasks and conducted extensive experiments on benchmark datasets. Experimental
results and comparison with prior work have verified the superiority of our
model, suggesting the generality and effectiveness of the model in
semi-automatic semantic segmentation.
