Multi-level semantic adaptation for few-shot segmentation on cardiac image sequences

Obtaining manual labels is time-consuming and labor-intensive on cardiac image sequences. Few-shot segmentation can utilize limited labels to learn new tasks. However, it suffers from two challenges: spatial-temporal distribution bias and long-term information bias. These challenges derive from the impact of the time dimension on cardiac image sequences, resulting in serious over-adaptation. In this paper, we propose the multi-level semantic adaptation (MSA) for few-shot segmentation on cardiac image sequences. The MSA addresses the two biases by exploring the domain adaptation and the weight adaptation on the semantic features in multiple levels, including sequence-level, frame-level, and pixel-level. First, the MSA proposes the dual-level feature adjustment for domain adaptation in spatial and temporal directions. This adjustment explicitly aligns the frame-level feature and the sequence-level feature to improve the model adaptation on diverse modalities. Second, the MSA explores the hierarchical attention metric for weight adaptation in the frame-level feature and the pixel-level feature. This metric focuses on the similar frame and the target region to promote the model discrimination on the border features. The extensive experiments demonstrate that our MSA is effective in f ew-shot segmentation on cardiac image sequences with three modalities, i.e. MR, CT, and Echo (e.g. the average Dice is 0.9243), as well as superior to the ten state-of-the-art methods. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes multi-level semantic adaptation (MSA) for few-shot segmentation on cardiac image sequences, addressing spatial-temporal distribution bias and long-term information bias through domain adaptation and weight adaptation at sequence-level, frame-level, and pixel-level to improve model adaptation and border feature discrimination.