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 on organ and tumour.

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实现接近人类水平的性能。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，我们提出了一种新颖的鲁棒半监督医学图像分割框架，采用对角层次一致性学习（DiHC-Net）。首先，该框架由多个子模型组成，这些子模型具有相同的多尺度架构，但拥有不同的子层（如上采样层和归一化层）。其次，通过互一致性，以对角层次方式在某一模型的中间和最终预测与其他模型生成的软伪标签之间施加了一种新的一致性正则化。一系列实验验证了我们简单框架的有效性，在公开基准数据集上的器官和肿瘤分割任务中超越了所有现有方法。