SER-Diff：用于增量脑肿瘤分割的合成错误重放扩散模型
SER-Diff: Synthetic Error Replay Diffusion for Incremental Brain Tumor Segmentation
AI总结：
针对增量脑肿瘤分割中的灾难性遗忘问题，提出SER-Diff框架，利用冻结教师扩散模型生成并重放合成错误图，结合双损失训练，在多个基准上取得最优分割性能。
摘要：
增量脑肿瘤分割对于必须适应不断演变的临床数据集而无需对所有先前数据重新训练的模型至关重要。然而，灾难性遗忘（模型丢失先前获取的知识）仍然是一个主要障碍。近期采用知识蒸馏的增量学习框架在一定程度上缓解了遗忘问题，但严重依赖生成式重放或辅助存储。与此同时，扩散模型已被证明在细化肿瘤分割方面有效，但尚未在增量学习场景中得到探索。我们提出了合成错误重放扩散（SER-Diff），这是首个将基于扩散的细化与增量学习相统一的框架。SER-Diff利用一个冻结的教师扩散模型从过去任务中生成合成错误图，并在新任务训练期间重放这些错误图。一种结合新数据Dice损失和重放错误知识蒸馏损失的双损失公式确保了适应性和保留性。在BraTS2020、BraTS2021和BraTS2023上的实验表明，SER-Diff持续优于先前方法。它分别取得了95.8%、94.9%和94.6%的最高Dice分数，以及4.4毫米、4.7毫米和4.9毫米的最低HD95值。这些结果表明，SER-Diff不仅缓解了灾难性遗忘，而且在不断演变的数据集上提供了更准确且解剖学上连贯的分割结果。
英文摘要：
Incremental brain tumor segmentation is critical for models that must adapt to evolving clinical datasets without retraining on all prior data. However, catastrophic forgetting, where models lose previously acquired knowledge, remains a major obstacle. Recent incremental learning frameworks with knowledge distillation partially mitigate forgetting but rely heavily on generative replay or auxiliary storage. Meanwhile, diffusion models have proven effective for refining tumor segmentations, but have not been explored in incremental learning contexts. We propose Synthetic Error Replay Diffusion (SER-Diff), the first framework that unifies diffusion-based refinement with incremental learning. SER-Diff leverages a frozen teacher diffusion model to generate synthetic error maps from past tasks, which are replayed during training on new tasks. A dual-loss formulation combining Dice loss for new data and knowledge distillation loss for replayed errors ensures both adaptability and retention. Experiments on BraTS2020, BraTS2021, and BraTS2023 demonstrate that SER-Diff consistently outperforms prior methods. It achieves the highest Dice scores of 95.8\%, 94.9\%, and 94.6\%, along with the lowest HD95 values of 4.4 mm, 4.7 mm, and 4.9 mm, respectively. These results indicate that SER-Diff not only mitigates catastrophic forgetting but also delivers more accurate and anatomically coherent segmentations across evolving datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
