用图像配准生成更准确的伪标签，提升少样本医学图像分割效果
A Unified Framework for Semi-Supervised Image Segmentation and Registration
- 结合图像配准生成几何合理的伪标签
- 仅用1%标注数据即达优秀分割性能
- 适合标注稀缺的医疗影像场景
半监督学习通过利用标注与未标注数据，在医学图像分割中表现出高效性，因全量标注耗时且成本高。传统方法主要从未标注数据中提取特征并学习数据分布以增强模型训练。本文提出一种新方法，引入图像配准模型生成未标注数据的伪标签，使伪标签具备更好的几何一致性，从而提升模型训练效果。在2D脑部数据集上的实验表明，即使仅使用1%的标注数据，该方法仍表现优异，显著优于传统半监督分割方法（如教师-学生模型），尤其在标注比例极低的情况下优势明显。代码已开源：https://github.com/ruizhe-l/UniSegReg。
原文摘要 · Abstract (English)
Semi-supervised learning, which leverages both annotated and unannotated data, is an efficient approach for medical image segmentation, where obtaining annotations for the whole dataset is time-consuming and costly. Traditional semi-supervised methods primarily focus on extracting features and learning data distributions from unannotated data to enhance model training. In this paper, we introduce a novel approach incorporating an image registration model to generate pseudo-labels for the unannotated data, producing more geometrically correct pseudo-labels to improve the model training. Our method was evaluated on a 2D brain data set, showing excellent performance even using only 1\% of the annotated data. The results show that our approach outperforms conventional semi-supervised segmentation methods (e.g. teacher-student model), particularly in a low percentage of annotation scenario. GitHub: https://github.com/ruizhe-l/UniSegReg.
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