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）方法针对自然图像分类设计，但在医学图像分割中往往无法达到基本质量标准。基于图谱的分割作为医学影像领域的一种成熟方法，通过融入感兴趣区域的领域知识，生成语义连贯的预测结果。这对持续学习尤为有利，因为它允许我们利用结构信息，在模型刚性与可塑性之间随时间实现最优平衡。当与保护隐私的原型相结合时，该方法既保留了基于回放的持续学习的优势，又不损害患者隐私。我们提出了Atlas Replay，一种基于图谱的分割方法，通过图像配准利用原型生成高质量分割掩膜，即使训练分布发生变化也能保持一致性。我们探讨了所提方法在七个公开前列腺分割数据集上的知识迁移能力，并与当前最先进的持续学习方法进行了比较。前列腺分割在诊断前列腺癌中至关重要，但由于显著的解剖变异、老年群体中良性的结构差异以及变化的采集参数，这一任务面临挑战。我们的结果表明，与端到端分割方法不同，Atlas Replay既鲁棒性强，又能良好泛化到未见过的领域，同时保持知识记忆能力。我们的代码库可在https://github.com/MECLabTUDA/Atlas-Replay获取。