Continual learning (CL) methods designed for natural image classification often fail to reach basic quality standards for medical image segmentation. Atlas-based segmentation, a well-established approach in medical imaging, incorporates domain knowledge on the region of interest, leading to semantically coherent predictions. This is especially promising for CL, as it allows us to leverage structural information and strike an optimal balance between model rigidity and plasticity over time. When combined with privacy-preserving prototypes, this process offers the advantages of rehearsal-based CL without compromising patient privacy. We propose Atlas Replay, an atlas-based segmentation approach that uses prototypes to generate high-quality segmentation masks through image registration that maintain consistency even as the training distribution changes. We explore how our proposed method performs compared to state-of-the-art CL methods in terms of knowledge transferability across seven publicly available prostate segmentation datasets. Prostate segmentation plays a vital role in diagnosing prostate cancer, however, it poses challenges due to substantial anatomical variations, benign structural differences in older age groups, and fluctuating acquisition parameters. Our results show that Atlas Replay is both robust and generalizes well to yet-unseen domains while being able to maintain knowledge, unlike end-to-end segmentation methods. Our code base is available under https://github.com/MECLabTUDA/Atlas-Replay.

翻译：持续学习（CL）方法专为自然图像分类设计，但通常难以达到医学图像分割的基本质量标准。基于图谱的分割是医学影像中一种成熟的方法，它融入了感兴趣区域的领域知识，从而产生语义一致的预测。这对持续学习尤其有前景，因为它允许我们利用结构信息，并在模型的刚性与可塑性之间实现随时间推移的最优平衡。当与保护隐私的原型结合时，这一过程既保留了基于回放的持续学习的优势，又不损害患者隐私。我们提出Atlas Replay，一种基于图谱的分割方法，通过图像配准使用原型生成高质量的分割掩膜，即使在训练分布变化时也能保持一致性。我们探讨了所提方法相较于当前最优的持续学习方法，在七个公开前列腺分割数据集上的知识迁移性能。前列腺分割在诊断前列腺癌中起着关键作用，但由于解剖结构的显著变异、老年群体中良性结构差异以及变化的采集参数而面临挑战。我们的结果表明，Atlas Replay既稳健又能很好地泛化到未见领域，同时能够维持知识，这与端到端分割方法不同。我们的代码库可在https://github.com/MECLabTUDA/Atlas-Replay获取。