Upon remarkable progress in cardiac image segmentation, contemporary studies dedicate to further upgrading model functionality toward perfection, through progressively exploring the sequentially delivered datasets over time by domain incremental learning. Existing works mainly concentrated on addressing the heterogeneous style variations, but overlooked the critical shape variations across domains hidden behind the sub-disease composition discrepancy. In case the updated model catastrophically forgets the sub-diseases that were learned in past domains but are no longer present in the subsequent domains, we proposed a dual enrichment synergistic strategy to incrementally broaden model competence for a growing number of sub-diseases. The data-enriched scheme aims to diversify the shape composition of current training data via displacement-aware shape encoding and decoding, to gradually build up the robustness against cross-domain shape variations. Meanwhile, the model-enriched scheme intends to strengthen model capabilities by progressively appending and consolidating the latest expertise into a dynamically-expanded multi-expert network, to gradually cultivate the generalization ability over style-variated domains. The above two schemes work in synergy to collaboratively upgrade model capabilities in two-pronged manners. We have extensively evaluated our network with the ACDC and M&Ms datasets in single-domain and compound-domain incremental learning settings. Our approach outperformed other competing methods and achieved comparable results to the upper bound.

中文翻译：

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处理域增量心脏分割数据异质性的双重丰富协同策略


随着心脏图像分割取得显着进展，当代研究致力于通过领域增量学习逐步探索随时间推移顺序交付的数据集，从而进一步将模型功能升级到完美。现有的工作主要集中在解决异质风格变化，但忽视了隐藏在子疾病组成差异背后的跨领域的关键形状变化。如果更新的模型灾难性地忘记了在过去领域中学到的但在后续领域中不再存在的子疾病，我们提出了一种双重富集协同策略，以逐步扩大模型针对越来越多的子疾病的能力。数据丰富方案旨在通过位移感知形状编码和解码来使当前训练数据的形状组成多样化，以逐步建立针对跨域形状变化的鲁棒性。同时，模型丰富方案旨在通过将最新的专业知识逐步附加和整合到动态扩展的多专家网络中来增强模型能力，逐步培养不同风格领域的泛化能力。上述两种方案协同作用，双管齐下协同升级模型能力。我们在单域和复合域增量学习设置中使用 ACDC 和 M&Ms 数据集广泛评估了我们的网络。我们的方法优于其他竞争方法，并取得了与上限相当的结果。