Annotation scarcity has become a major obstacle for training powerful
deep-learning models for medical image segmentation, restricting their
deployment in clinical scenarios. To address it, semi-supervised learning by
exploiting abundant unlabeled data is highly desirable to boost the model
training. However, most existing works still focus on limited medical tasks and
underestimate the potential of learning across diverse tasks and multiple
datasets. Therefore, in this paper, we introduce a \textbf{Ver}satile
\textbf{Semi}-supervised framework (VerSemi) to point out a new perspective
that integrates various tasks into a unified model with a broad label space, to
exploit more unlabeled data for semi-supervised medical image segmentation.
Specifically, we introduce a dynamic task-prompted design to segment various
targets from different datasets. Next, this unified model is used to identify
the foreground regions from all labeled data, to capture cross-dataset
semantics. Particularly, we create a synthetic task with a cutmix strategy to
augment foreground targets within the expanded label space. To effectively
utilize unlabeled data, we introduce a consistency constraint. This involves
aligning aggregated predictions from various tasks with those from the
synthetic task, further guiding the model in accurately segmenting foreground
regions during training. We evaluated our VerSemi model on four public
benchmarking datasets. Extensive experiments demonstrated that VerSemi can
consistently outperform the second-best method by a large margin (e.g., an
average 2.69\% Dice gain on four datasets), setting new SOTA performance for
semi-supervised medical image segmentation. The code will be released.

通过整合各种任务到一个统一模型并利用大量未标记数据来进行半监督医学图像分割，我们介绍了一个名为 VerSemi 的全新的多功能半监督框架，实验证明它在公共基准数据集上始终可以通过较大的边际优势（例如，在四个数据集上平均达到 2.69％的 Dice 增益）超越其他方法，为半监督医学图像分割设定了新的最佳表现。