  Semantic segmentation in cataract surgery has a wide range of applications
contributing to surgical outcome enhancement and clinical risk reduction.
However, the varying issues in segmenting the different relevant instances make
the designation of a unique network quite challenging. This paper proposes a
semantic segmentation network termed as DeepPyram that can achieve superior
performance in segmenting relevant objects in cataract surgery videos with
varying issues. This superiority mainly originates from three modules: (i)
Pyramid View Fusion, which provides a varying-angle global view of the
surrounding region centering at each pixel position in the input convolutional
feature map; (ii) Deformable Pyramid Reception, which enables a wide deformable
receptive field that can adapt to geometric transformations in the object of
interest; and (iii) Pyramid Loss that adaptively supervises multi-scale
semantic feature maps. These modules can effectively boost semantic
segmentation performance, especially in the case of transparency,
deformability, scalability, and blunt edges in objects. The proposed approach
is evaluated using four datasets of cataract surgery for objects with different
contextual features and compared with thirteen state-of-the-art segmentation
networks. The experimental results confirm that DeepPyram outperforms the rival
approaches without imposing additional trainable parameters. Our comprehensive
ablation study further proves the effectiveness of the proposed modules.
