A segment anything model‐guided and match‐based semi‐supervised segmentation framework for medical imaging

作者：Guoping Xu, Xiaoxue Qian, Hua‐Chieh Shao, Jax Luo, Weiguo Lu, You Zhang · 发表于：Medical Physics · 年份：2025 · DOI：10.1002/mp.17785 · 被引用次数：12 · 研究领域：Advanced Neural Network Applications、Medical Image Segmentation Techniques、Domain Adaptation and Few-Shot Learning

BACKGROUND: Semi-supervised segmentation leverages sparse annotation information to learn rich representations from combined labeled and label-less data for segmentation tasks. The Match-based framework, by using the consistency constraint of segmentation results from different models/augmented label-less inputs, is found effective in semi-supervised learning. This approach, however, is challenged by the low quality of pseudo-labels generated as intermediate products for training the network, due to the lack of the ''ground-truth'' reference. PURPOSE: This study aims to leverage the foundation model, segment anything model (SAM), to assist unsupervised learning of Match-based frameworks. Trained with an extremely large dataset, SAM-based methods generalize better than traditional models to various imaging domains, allow it to serve as an assistant to Match-based frameworks to improve the quality of intermediate pseudo-labels for semi-supervised learning. METHODS: We propose SAM-Match, a SAM-guided and Match-based framework for semi-supervised medical image segmentation. Our approach involves two main steps: First, we use pretrained Match-based models to extract high-confidence predictions for prompt generation. Second, these prompts and unlabeled images are input into a fine-tuned SAM-based method to produce high-quality masks as pseudo-labels. And the refined pseudo-labels are further fed back to train the Match-based framework. SAM-Match can be trained in an end-to-end mann...