Dual Prototypical Self-Supervised Learning for One-shot Medical Image Segmentation

Ziyuan Zhao, Zhi Qing Ng, Zhongyao Cheng, Jiahao Wang, Xulei Yang, Hanry Yu, Cuntai Guan

Abstract: Medical image segmentation using deep learning typically requires a large quantity of well-annotated data. However, the acquisition of pixel-level annotations is arduous and expensive, often requiring the expertise of experienced medical professionals. Recent advancements in few-shot learning can help address label scarcity in medical image segmentation by leveraging a small amount of labeled data. Most research on prototypical learning for medical image segmentation involves prototypes averaged across the entire class, ignoring fine details. In this work, we propose a novel dual prototype network and introduce part prototypes to supplement local prototypes for extracting fine-grained features and enhancing model performance in one-shot medical image segmentation. Extensive experiments on the CHAOS dataset demonstrate the effectiveness of the proposed method on one-shot segmentation, outperforming other methods by a significant margin.Clinical relevance— The proposed method can address the problem of prototypical self-supervised learning in the one-shot medical image segmentation scenario, saving annotation costs in clinical practice.