The ability to dynamically extend a model to new data and classes is critical for multiple organ and tumor segmentation. However, due to privacy regulations, accessing previous data and annotations can be problematic in the medical domain. This poses a significant barrier to preserving the high segmentation accuracy of the old classes when learning from new classes because of the catastrophic forgetting problem. In this paper, we first empirically demonstrate that simply using high-quality pseudo labels can fairly mitigate this problem in the setting of organ segmentation. Furthermore, we put forward an innovative architecture designed specifically for continuous organ and tumor segmentation, which incurs minimal computational overhead. Our proposed design involves replacing the conventional output layer with a suite of lightweight, class-specific heads, thereby offering the flexibility to accommodate newly emerging classes. These heads enable independent predictions for newly introduced and previously learned classes, effectively minimizing the impact of new classes on old ones during the course of continual learning. We further propose incorporating Contrastive Language-Image Pretraining (CLIP) embeddings into the organ-specific heads. These embeddings encapsulate the semantic information of each class, informed by extensive image-text co-training. The proposed method is evaluated on both in-house and public abdominal CT datasets under organ and tumor segmentation tasks. Empirical results suggest that the proposed design improves the segmentation performance of a baseline neural network on newly-introduced and previously-learned classes along the learning trajectory.

翻译：动态扩展模型以适配新数据与新类别的能力对多器官及肿瘤分割至关重要。然而，受隐私法规限制，医疗领域中获取既往数据与标注存在困难。这导致在学习新类别时，因灾难性遗忘问题而难以维持旧类别分割精度的重大障碍。本文首先通过实验证明，在器官分割场景下，仅使用高质量伪标签即可在一定程度上缓解该问题。此外，我们提出一种专为持续器官与肿瘤分割设计的创新架构，其计算开销极低。该设计将传统输出层替换为一组轻量级类别特定头（class-specific heads），从而灵活适配新出现的类别。这些头结构可分别对新增类别与已学类别进行独立预测，有效降低持续学习过程中新类别对旧类别的影响。我们进一步将对比语言-图像预训练（CLIP）嵌入融入器官特定头中，借助大规模图文联合训练所得语义信息表征各类别内涵。所提方法在内部及公开腹部CT数据集上，针对器官与肿瘤分割任务进行了评估。实验结果表明，该设计能沿学习轨迹提升基线神经网络在新引入类别与已学类别上的分割性能。