  Recent works in medical image segmentation have actively explored various
deep learning architectures or objective functions to encode high-level
features from volumetric data owing to limited image annotations. However, most
existing approaches tend to ignore cross-volume global context and define
context relations in the decision space. In this work, we propose a novel
voxel-level Siamese representation learning method for abdominal multi-organ
segmentation to improve representation space. The proposed method enforces
voxel-wise feature relations in the representation space for leveraging limited
datasets more comprehensively to achieve better performance. Inspired by recent
progress in contrastive learning, we suppressed voxel-wise relations from the
same class to be projected to the same point without using negative samples.
Moreover, we introduce a multi-resolution context aggregation method that
aggregates features from multiple hidden layers, which encodes both the global
and local contexts for segmentation. Our experiments on the multi-organ dataset
outperformed the existing approaches by 2% in Dice score coefficient. The
qualitative visualizations of the representation spaces demonstrate that the
improvements were gained primarily by a disentangled feature space.
