Pancreas segmentation is challenging due to the small proportion and highly changeable anatomical structure. It motivates us to propose a novel segmentation framework, namely Curriculum Knowledge Switching (CKS) framework, which decomposes detecting pancreas into three phases with different difficulty extent: straightforward, difficult, and challenging. The framework switches from straightforward to challenging phases and thereby gradually learns to detect pancreas. In addition, we adopt the momentum update parameter updating mechanism during switching, ensuring the loss converges gradually when the input dataset changes. Experimental results show that different neural network backbones with the CKS framework achieved state-of-the-art performance on the NIH dataset as measured by the DSC metric.

翻译：胰腺分割因其体积占比小且解剖结构高度多变而具有挑战性。为此，我们提出一种新颖的分割框架，即课程知识切换（CKS）框架，该框架将胰腺检测任务分解为三个难度递进阶段：简单、困难与挑战。该框架从简单阶段逐步切换至挑战阶段，从而渐进式地学习检测胰腺。此外，我们在切换过程中采用动量更新参数更新机制，确保输入数据集变化时损失函数能够平稳收敛。实验结果表明，基于不同神经网络骨干的CKS框架在NIH数据集上均取得了以DSC指标衡量的最优性能。