Medical image segmentation, which is essential for many clinical applications, has achieved almost human-level performance via data-driven deep learning techniques. Nevertheless, its performance is predicated on the costly process of manually annotating a large amount of medical images. To this end, we propose a novel framework for robust semi-supervised medical image segmentation using diagonal hierarchical consistency (DiHC-Net). First, it is composed of multiple sub-models with identical multi-scale architecture but with distinct sub-layers, such as up-sampling and normalisation layers. Second, a novel diagonal hierarchical consistency is enforced between one model's intermediate and final prediction and other models' soft pseudo labels in a diagonal hierarchical fashion. Experimental results verify the efficacy of our simple framework, outperforming all previous approaches on public Left Atrium (LA) dataset.

翻译：医学图像分割对许多临床应用至关重要，通过数据驱动的深度学习技术已接近人类水平性能。然而，其性能依赖于手动标注大量医学图像的昂贵过程。为此，我们提出了一种新颖的鲁棒半监督医学图像分割框架，采用对角分层一致性（DiHC-Net）。首先，该框架由多个具有相同多尺度架构但子层（如上采样和归一化层）不同的子模型组成。其次，以一种对角分层方式，强制一个模型的中间和最终预测与其他模型的软伪标签之间保持新颖的对角分层一致性。实验结果验证了我们简单框架的有效性，在公开的左心房（LA）数据集上超越了所有现有方法。