Accurate segmentation of organs at risk in the head and neck is essential for radiation therapy, yet deep learning models often fail on small, complexly shaped organs. While hybrid architectures that combine different models show promise, they typically just concatenate features without exploiting the unique strengths of each component. This results in functional overlap and limited segmentation accuracy. To address these issues, we propose a high uncertainty region-guided multi-architecture collaborative learning (HUR-MACL) model for multi-organ segmentation in the head and neck. This model adaptively identifies high uncertainty regions using a convolutional neural network, and for these regions, Vision Mamba as well as Deformable CNN are utilized to jointly improve their segmentation accuracy. Additionally, a heterogeneous feature distillation loss was proposed to promote collaborative learning between the two architectures in high uncertainty regions to further enhance performance. Our method achieves SOTA results on two public datasets and one private dataset.

翻译：头颈部危及器官的精确分割对于放射治疗至关重要，然而深度学习模型在处理体积小、形状复杂的器官时常常表现不佳。虽然融合不同模型的混合架构展现出潜力，但它们通常只是简单拼接特征，未能充分利用各组件的独特优势。这导致了功能重叠和有限的分割精度。为解决这些问题，我们提出了一种用于头颈部多器官分割的高不确定性区域引导多架构协同学习模型。该模型利用卷积神经网络自适应地识别高不确定性区域，并针对这些区域，联合使用 Vision Mamba 和可变形 CNN 来共同提升其分割精度。此外，我们提出了一种异构特征蒸馏损失，以促进两种架构在高不确定性区域内的协同学习，从而进一步提升性能。我们的方法在两个公开数据集和一个私有数据集上均取得了最先进的结果。