  Semantic Segmentation plays a pivotal role in many applications related to
medical image and video analysis. However, designing a neural network
architecture for medical image and surgical video segmentation is challenging
due to the diverse features of relevant classes, including heterogeneity,
deformability, transparency, blunt boundaries, and various distortions. We
propose a network architecture, DeepPyramid+, which addresses diverse
challenges encountered in medical image and surgical video segmentation. The
proposed DeepPyramid+ incorporates two major modules, namely "Pyramid View
Fusion" (PVF) and "Deformable Pyramid Reception," (DPR), to address the
outlined challenges. PVF replicates a deduction process within the neural
network, aligning with the human visual system, thereby enhancing the
representation of relative information at each pixel position. Complementarily,
DPR introduces shape- and scale-adaptive feature extraction techniques using
dilated deformable convolutions, enhancing accuracy and robustness in handling
heterogeneous classes and deformable shapes. Extensive experiments conducted on
diverse datasets, including endometriosis videos, MRI images, OCT scans, and
cataract and laparoscopy videos, demonstrate the effectiveness of DeepPyramid+
in handling various challenges such as shape and scale variation, reflection,
and blur degradation. DeepPyramid+ demonstrates significant improvements in
segmentation performance, achieving up to a 3.65% increase in Dice coefficient
for intra-domain segmentation and up to a 17% increase in Dice coefficient for
cross-domain segmentation. DeepPyramid+ consistently outperforms
state-of-the-art networks across diverse modalities considering different
backbone networks, showcasing its versatility.
