In the field of medical imaging, the U-Net architecture, along with its variants, has established itself as a cornerstone for image segmentation tasks, particularly due to its strong performance when trained on limited datasets. Despite its impressive performance on identically distributed (in-domain) data, U-Nets exhibit a significant decline in performance when tested on data that deviates from the training distribution, out-of-distribution (out-of-domain) data. Current methodologies predominantly address this issue by employing generalization techniques that hinge on various forms of regularization, which have demonstrated moderate success in specific scenarios. This paper, however, ventures into uncharted territory by investigating the implications of utilizing models that are smaller by three orders of magnitude (i.e., x1000) compared to a conventional U-Net. A reduction of this size in U-net parameters typically adversely affects both in-domain and out-of-domain performance, possibly due to a significantly reduced receptive field. To circumvent this issue, we explore the concept of Neural Cellular Automata (NCA), which, despite its simpler model structure, can attain larger receptive fields through recursive processes. Experimental results on two distinct datasets reveal that NCA outperforms traditional methods in terms of generalization, while still maintaining a commendable IID performance.

本研究针对医学图像分割中U-Net架构在训练与测试数据分布不一致时性能显著下降的问题，探讨了使用小型化神经元细胞自动机（NCA）的新方法。实验结果表明，NCA在泛化能力上超越了传统方法，同时保持了良好的独立同分布（IID）表现，展现了其在医学影像领域的应用潜力。

神经元细胞自动机在医学图像分割中的泛化能力：一种稳健且轻量级的方法