Medical image segmentation, which is essential for many clinical applications, has achieved almost human-level performance via data-driven deep learning technologies. Nevertheless, its performance is predicated upon the costly process of manually annotating a vast amount of medical images. To this end, we propose a novel framework for robust semi-supervised medical image segmentation using diagonal hierarchical consistency learning (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, with mutual consistency, a novel consistency regularisation is enforced between one model's intermediate and final prediction and soft pseudo labels from other models in a diagonal hierarchical fashion. A series of experiments verifies the efficacy of our simple framework, outperforming all previous approaches on public Left Atrium (LA) dataset.

翻译：医学图像分割是许多临床应用不可或缺的技术，且已通过数据驱动的深度学习技术达到近乎人类水平的性能。然而，其性能依赖于对大量医学图像进行人工标注的高成本过程。为此，我们提出一种基于对角线分层一致性学习（DiHC-Net）的鲁棒半监督医学图像分割新框架。首先，该框架包含多个子模型，这些子模型共享相同的多尺度架构，但具有不同的子层（如上采样层和归一化层）。其次，通过互一致性约束，以对角线分层方式对某一模型的中间及最终预测结果与其他模型生成的软伪标签施加新的一致性正则化。一系列实验验证了我们简单框架的有效性，其在公开的左心房（LA）数据集上超越了所有先前方法。