Robust One-Shot Brain Tissue Segmentation via Patch-wise Contrastive Learning and Dual-Head Variational Autoencoder

Conventional one-shot segmentation methods typically employ either registration methods for label propagation from a reference atlas or utilize synthetically labeled data to augment the training of segmentation networks. However, these approaches often fail to accurately capture anatomical structural information from real images, resulting in suboptimal segmentation performance and limited generalizability across different medical imaging modalities. In this paper, we propose a robust one-shot brain tissue segmentation framework, which requires only a single labeled image and a few unlabeled ones. Our approach features a novel synthesis module based on patch-wise contrastive learning for generating realistic, well-labeled training samples, followed by a dual-head Variational AutoEncoder (VAE) module for the joint learning of reconstruction and segmentation. During the final inference, the well-trained VAE is capable of precisely segmenting new, unseen images. Our method demonstrates versatility across various imaging modalities. Evaluations on two public MRI datasets and one in-house CT dataset reveal that our method achieves superior performance and enhanced generalization capabilities compared to existing state-of-the-art methods.