Voxel-level Siamese Representation Learning for Abdominal Multi-Organ Segmentation
This work addresses the challenge of medical image segmentation with limited annotations, offering an incremental improvement for abdominal organ analysis.
The paper tackled the problem of abdominal multi-organ segmentation by proposing a voxel-level Siamese representation learning method to leverage limited datasets more comprehensively, resulting in a 2% improvement in Dice score coefficient over existing approaches.
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.