通过高不确定区域引导，让两种模型协同分割头颈区小器官，提升精度。
HUR-MACL: High-Uncertainty Region-Guided Multi-Architecture Collaborative Learning for Head and Neck Multi-Organ Segmentation
- 用卷积网络识别分割困难区域，针对性调用两种模型协同处理。
- 在两个公开和一个私有数据集上达到当前最优性能。
- 适合需要精准分割小而复杂器官的医学影像任务。
头颈部器官危及组织的精确分割对放疗至关重要，但深度学习模型在小而形状复杂的器官上常表现不佳。尽管混合架构结合不同模型具有一定潜力，但通常仅简单拼接特征，未充分挖掘各组件优势，导致功能重叠，分割精度有限。为此，本文提出一种高不确定区域引导的多架构协同学习（HUR-MACL）模型，用于头颈部多器官分割。该模型通过卷积神经网络自适应识别高不确定区域，并在这些区域中联合使用Vision Mamba与可变形卷积网络（Deformable CNN）以提升分割准确性。此外，提出异构特征蒸馏损失函数，促进两架构在高不确定区域中的协同学习，进一步优化性能。实验表明，该方法在两个公开数据集和一个私有数据集上均取得当前最优结果。
原文摘要 · Abstract (English)
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.
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