  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.
