Abstract
Limited by the scarcity of training samples and annotations, weakly supervised medical image segmentation often employs data augmentation to increase data diversity, while randomly mixing volumetric blocks has demonstrated strong performance. However, this approach disrupts the inherent anatomical continuity of 3D medical images along orthogonal axes, leading to severe structural inconsistencies and insufficient training in challenging regions, such as small-sized organs, etc. To better comply with and utilize human anatomical information, we propose JanusNet}, a data augmentation framework for 3D medical data that globally models anatomical continuity while locally focusing on hard-to-segment regions. Specifically, our Slice-Block Shuffle step performs aligned shuffling of same-index slice blocks across volumes along a random axis, while preserving the anatomical context on planes perpendicular to the perturbation axis. Concurrently, the Confidence-Guided Displacement step uses prediction reliability to replace blocks within each slice, amplifying signals from difficult areas. This dual-stage, axis-aligned framework is plug-and-play, requiring minimal code changes for most teacher-student schemes. Extensive experiments on the Synapse and AMOS datasets demonstrate that JanusNet significantly surpasses state-of-the-art methods, achieving, for instance, a 4% DSC gain on the Synapse dataset with only 20% labeled data.
Abstract (translated)
由于训练样本和标注的稀缺性，弱监督下的医学图像分割通常采用数据增强来增加数据多样性。随机混合体积块的方法已经展示出了强大的性能，但这种方法会破坏3D医学影像在正交轴上的内在解剖连续性，导致严重的结构不一致，并且对于如小型器官等具有挑战性的区域训练不足。为了更好地符合和利用人体解剖信息，我们提出了JanusNet，这是一个针对3D医疗数据的数据增强框架，它全局建模解剖连续性，同时局部关注难以分割的区域。 具体来说，我们的Slice-Block Shuffle步骤沿随机轴对不同体积中的相同索引切片块进行对齐打乱操作，并且保持了与扰动轴垂直的方向上的解剖背景。同时，Confidence-Guided Displacement步骤使用预测可靠性来替换每个切片内的区块，从而放大来自难以分割区域的信号。 这种双阶段、沿轴线的数据增强框架是即插即用的，对于大多数师生方案几乎不需要代码改动。在Synapse和AMOS数据集上的广泛实验表明，JanusNet显著超越了现有的最佳方法，在只有20%标注数据的情况下，例如在Synapse数据集中达到了4%的DSC增益。
URL
https://arxiv.org/abs/2508.03997
