不同的脑肿瘤磁共振成像（MRI）模式提供不同的肿瘤特异性信息。先前的工作通过整合多种 MRI 模式来增强脑肿瘤分割性能。然而，在临床实践中往往无法获得多模态 MRI 数据。不完整的模式会导致肿瘤特异性信息的缺失，从而降低现有模型的性能。为了解决这一问题，人们提出了各种策略将知识从完整模态网络（教师）转移到不完整模态网络（学生）。然而，他们忽略了这样一个事实：脑肿瘤分割是一个需要体素语义关系的结构预测问题。在本文中，我们提出了一种重建不完整关系网络（RIRN），它将体素语义关系知识从教师转移到学生。具体来说，我们提出了两种类型的体素关系来合并结构知识：类相关关系（CRR）和类不可知关系（CAR）。 CRR 将体素分组到不同的肿瘤区域并构建它们之间的关系。 CAR 在所有体素特征之间建立全局关系，补充局部区域间关系。此外，我们使用对抗性学习来协调教师和学生之间的整体结构预测。对 BraTS 2018 和 BraTS 2020 数据集的广泛实验表明，我们的方法优于所有最先进的方法。版权所有 © 2024 Elsevier Ltd。保留所有权利。

Different brain tumor magnetic resonance imaging (MRI) modalities provide diverse tumor-specific information. Previous works have enhanced brain tumor segmentation performance by integrating multiple MRI modalities. However, multi-modal MRI data are often unavailable in clinical practice. An incomplete modality leads to missing tumor-specific information, which degrades the performance of existing models. Various strategies have been proposed to transfer knowledge from a full modality network (teacher) to an incomplete modality one (student) to address this issue. However, they neglect the fact that brain tumor segmentation is a structural prediction problem that requires voxel semantic relations. In this paper, we propose a Reconstruct Incomplete Relation Network (RIRN) that transfers voxel semantic relational knowledge from the teacher to the student. Specifically, we propose two types of voxel relations to incorporate structural knowledge: Class-relative relations (CRR) and Class-agnostic relations (CAR). The CRR groups voxels into different tumor regions and constructs a relation between them. The CAR builds a global relation between all voxel features, complementing the local inter-region relation. Moreover, we use adversarial learning to align the holistic structural prediction between the teacher and the student. Extensive experimentation on both the BraTS 2018 and BraTS 2020 datasets establishes that our method outperforms all state-of-the-art approaches.Copyright © 2024 Elsevier Ltd. All rights reserved.