While computer vision has proven valuable for medical image segmentation, its application faces challenges such as limited dataset sizes and the complexity of effectively leveraging unlabeled images. To address these challenges, we present a novel semi-supervised, consistency-based approach termed the data-efficient medical segmenter (DEMS). The DEMS features an encoder-decoder architecture and incorporates the developed online automatic augmenter (OAA) and residual robustness enhancement (RRE) blocks. The OAA augments input data with various image transformations, thereby diversifying the dataset to improve the generalization ability. The RRE enriches feature diversity and introduces perturbations to create varied inputs for different decoders, thereby providing enhanced variability. Moreover, we introduce a sensitive loss to further enhance consistency across different decoders and stabilize the training process. Extensive experimental results on both our own and three public datasets affirm the effectiveness of DEMS. Under extreme data shortage scenarios, our DEMS achieves 16.85\% and 10.37\% improvement in dice score compared with the U-Net and top-performed state-of-the-art method, respectively. Given its superior data efficiency, DEMS could present significant advancements in medical segmentation under small data regimes. The project homepage can be accessed at https://github.com/NUS-Tim/DEMS.

翻译：尽管计算机视觉在医学图像分割领域已展现出重要价值，但其应用仍面临数据集规模有限以及有效利用未标注图像复杂性等挑战。为应对这些挑战，本文提出一种新颖的半监督、基于一致性的方法，称为数据高效医学分割器（DEMS）。DEMS采用编码器-解码器架构，并整合了所开发的在线自动增强器（OAA）与残差鲁棒性增强（RRE）模块。OAA通过多种图像变换对输入数据进行增强，从而增加数据集的多样性以提升泛化能力。RRE模块则通过丰富特征多样性并引入扰动，为不同解码器生成多样化输入，从而提供更强的变异性。此外，我们引入了一种敏感损失函数，以进一步增强不同解码器间的一致性并稳定训练过程。在自有数据集及三个公开数据集上的大量实验结果验证了DEMS的有效性。在极端数据稀缺场景下，相较于U-Net与当前最优方法，我们的DEMS在Dice分数上分别实现了16.85%与10.37%的提升。凭借其卓越的数据效率，DEMS有望在小数据场景下的医学分割领域取得显著进展。项目主页可通过https://github.com/NUS-Tim/DEMS访问。