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 benchmark dataset covering organ and tumour.

翻译：医学图像分割是许多临床应用中不可或缺的技术，通过数据驱动的深度学习技术已接近人类水平表现。然而，其性能高度依赖于对大量医学图像进行人工标注的高成本过程。为此，我们提出了一种基于对角分层一致性学习（DiHC-Net）的鲁棒半监督医学图像分割新框架。该框架首先包含多个子模型，它们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，通过相互一致性约束，以对角分层方式强制实现一个模型的中间及最终预测与其他模型生成的软伪标签之间的一致性正则化。一系列实验验证了该简洁框架的有效性，在涵盖器官和肿瘤的公开基准数据集上，其性能超越了所有先前方法。