Semi-supervised segmentation method based on a small number of labeled left atria

Motivation: The study aims to propose segmentation models and explore the impact of the proposed model on small models and the impact of the proportion and amount of different labeled data on the results. Goal(s): The goal of this study is to achieve better results with less labeled data. Approach: The study builds a model and evaluates the performance of the model on different sizes of left atria labeled data. Results: The method proposed in this study can improve the effect of small models, and reducing the labeled data proportion has a greater impact on the model performance than reducing the training data. Impact: This study provides help for how to improve the medical image segmentation performance of small models, improve the efficiency of manual labeling, and achieve better segmentation results with fewer manual annotations.