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：动态扩展模型以适应新数据和类别的能力对多器官和肿瘤分割至关重要。然而，由于隐私法规的限制，在医学领域中访问先前数据和标注存在困难。这给从新类别学习时保持旧类别的高分割精度带来了重大障碍，原因在于灾难性遗忘问题。本文首先通过实验证明，在器官分割场景中，仅使用高质量伪标签即可在一定程度上缓解这一问题。此外，我们提出了一种专为连续器官与肿瘤分割设计的创新架构，其计算开销极低。我们的设计将传统输出层替换为一套轻量级、类别特定的预测头，从而灵活适应新出现的类别。这些预测头能独立对新引入和先前学习的类别进行预测，有效减少持续学习过程中新类别对旧类别的影响。我们进一步提出将对比语言-图像预训练（CLIP）嵌入整合到器官特定预测头中。这些嵌入蕴含了各类别的语义信息，并基于大规模图像-文本协同训练获得。所提方法在内部和公开腹部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，该设计能提升基线神经网络在新引入和先前学习类别上沿学习轨迹的分割性能。