Voxel-level Siamese Representation Learning for Abdominal Multi-Organ Segmentation. (January 2022) - Lee, Chae Eun; Chung, Minyoung; Shin, Yeong-Gil
Highlights: We propose simple yet effective voxel-level representation learning method for multi-organ segmentation on abdominal CT scans. Our method enforces voxel-level feature relations in the representation space so that we can enhance representation power of the base network We define voxel-level feature relations without using negative samples, which is an efficient method in terms of the computational cost. While using SimSiam method, we neither use a large batch size nor use a momentum encoder, which are typically required for collecting a large amount of negative samples. We propose a multi-resolution context aggregation method that aggregates features from the intermediate layers and the last hidden layer. Using our method, we can train both global and local context features simultaneously. Abstract: Background and Objective: 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. Methods: 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. Results: 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. Conclusion: Our new representation learning method successfully encoded high-level features in the representation space by using a limited dataset, which showed superior accuracy in the medical image segmentation task compared to other contrastive loss-based methods. Moreover, our method can be easily applied to other networks without using additional parameters in the inference.