Medical imaging has witnessed remarkable progress but usually requires a large amount of high-quality annotated data which is time-consuming and costly to obtain. To alleviate this burden, semi-supervised learning has garnered attention as a potential solution. In this paper, we present Meta-Learning for Bootstrapping Medical Image Segmentation (MLB-Seg), a novel method for tackling the challenge of semi-supervised medical image segmentation. Specifically, our approach first involves training a segmentation model on a small set of clean labeled images to generate initial labels for unlabeled data. To further optimize this bootstrapping process, we introduce a per-pixel weight mapping system that dynamically assigns weights to both the initialized labels and the model's own predictions. These weights are determined using a meta-process that prioritizes pixels with loss gradient directions closer to those of clean data, which is based on a small set of precisely annotated images. To facilitate the meta-learning process, we additionally introduce a consistency-based Pseudo Label Enhancement (PLE) scheme that improves the quality of the model's own predictions by ensembling predictions from various augmented versions of the same input. In order to improve the quality of the weight maps obtained through multiple augmentations of a single input, we introduce a mean teacher into the PLE scheme. This method helps to reduce noise in the weight maps and stabilize its generation process. Our extensive experimental results on public atrial and prostate segmentation datasets demonstrate that our proposed method achieves state-of-the-art results under semi-supervision. Our code is available at https://github.com/aijinrjinr/MLB-Seg.

翻译：医学影像领域虽已取得显著进展，但通常需要大量高质量标注数据，其获取过程耗时且成本高昂。为缓解这一负担，半监督学习作为潜在解决方案受到关注。本文提出元学习驱动的医学图像分割自举方法（MLB-Seg），一种应对半监督医学图像分割挑战的新方法。具体而言，该方法首先在少量干净标注图像上训练分割模型，为未标注数据生成初始标签。为优化这一自举过程，我们引入逐像素权重映射系统，动态分配权重至初始标签与模型自预测结果。该权重通过元过程确定，优先选取损失梯度方向与干净数据更接近的像素，其依据来源于少量精确标注图像。为促进元学习过程，我们额外提出基于一致性的伪标签增强（PLE）方案，通过对同一输入的不同增强版本进行集成预测来提升模型自预测质量。为改善单输入多次增强所得权重图的质量，我们在PLE方案中引入平均教师模型。该方法有助于降低权重图中的噪声并稳定其生成过程。在公开心房分割与前列腺分割数据集上的大量实验表明，本方法在半监督条件下取得了最先进的性能。代码发布于https://github.com/aijinrjinr/MLB-Seg。