A-QCF-Net：一种自适应四元数交叉融合网络用于从无配对数据集进行多模态肝肿瘤分割
A-QCF-Net: An Adaptive Quaternion Cross-Fusion Network for Multimodal Liver Tumor Segmentation from Unpaired Datasets
AI总结：
A-QCF-Net通过自适应四元数交叉融合网络，实现从无配对CT和MRI数据集中的肝肿瘤分割，提升分割精度并验证模型的临床实用性。
摘要：
多模态医学影像提供了互补信息，这对于准确勾勒病理学特征至关重要，但深度学习模型的发展受到不同模态配对且空间对齐的大数据集稀缺的限制。本文通过提出自适应四元数交叉融合网络（A-QCF-Net）来解决这一根本限制，该网络从完全分离且无配对的CT和MRI队列中学习单一统一的分割模型。该架构利用四元数神经网络的参数效率和表达能力来构建共享特征空间。其核心是自适应四元数交叉融合（A-QCF）模块，这是一个数据驱动的注意力模块，能够实现两个流之间的双向知识转移。通过学习动态调节信息流，A-QCF模块使网络能够交换抽象的模态特定专业知识，例如CT中提供的锐利解剖边界信息和MRI中提供的微妙软组织对比度。这种相互交换正则化并丰富了两个流的特征表示。我们通过在无配对的LiTS（CT）和ATLAS（MRI）数据集上联合训练单一模型来验证该框架。联合训练的模型在CT上实现了76.7%的肿瘤Dice分数，在MRI上实现了78.3%的分数，显著超过了强大的单模态nnU-Net基线，分别高出5.4%和4.7%。此外，使用Grad-CAM和Grad-CAM++进行的全面可解释性分析证实，模型正确聚焦于相关的病理结构，确保学习到的表示具有临床意义。这为解锁医疗领域常见的大量无配对影像档案提供了稳健且临床可行的范式。
英文摘要：
Multimodal medical imaging provides complementary information that is crucial for accurate delineation of pathology, but the development of deep learning models is limited by the scarcity of large datasets in which different modalities are paired and spatially aligned. This paper addresses this fundamental limitation by proposing an Adaptive Quaternion Cross-Fusion Network (A-QCF-Net) that learns a single unified segmentation model from completely separate and unpaired CT and MRI cohorts. The architecture exploits the parameter efficiency and expressive power of Quaternion Neural Networks to construct a shared feature space. At its core is the Adaptive Quaternion Cross-Fusion (A-QCF) block, a data driven attention module that enables bidirectional knowledge transfer between the two streams. By learning to modulate the flow of information dynamically, the A-QCF block allows the network to exchange abstract modality specific expertise, such as the sharp anatomical boundary information available in CT and the subtle soft tissue contrast provided by MRI. This mutual exchange regularizes and enriches the feature representations of both streams. We validate the framework by jointly training a single model on the unpaired LiTS (CT) and ATLAS (MRI) datasets. The jointly trained model achieves Tumor Dice scores of 76.7% on CT and 78.3% on MRI, significantly exceeding the strong unimodal nnU-Net baseline by margins of 5.4% and 4.7% respectively. Furthermore, comprehensive explainability analysis using Grad-CAM and Grad-CAM++ confirms that the model correctly focuses on relevant pathological structures, ensuring the learned representations are clinically meaningful. This provides a robust and clinically viable paradigm for unlocking the large unpaired imaging archives that are common in healthcare.
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