Pre-Post Interaction Learning for Brain Tumor Segmentation with Missing MRI Modalities

Complete multimodal Magnetic Resonance Imaging (MRI) plays an indispensable role in the task of brain tumor segmentation. However, the issue of missing-modality often arises in clinical practice, leading to a significant decline in the accuracy of segmentation. Current methods exhibit suboptimal performance under such scenarios of severe missing modalities due to either limited synthesis capacity for missing modalities or modality-specific information loss caused by strict alignment in latent space. To address this challenge, we propose a Pre-Post Interaction Learning (PPIL) approach that enhances the model's robustness under severe missing-modality scenarios while maintaining competitive performance when most of the modalities are available. Specifically, separate branches are introduced for each modality to preserve modality-specific information. Meanwhile, a Pre-Interaction component that takes the concatenation of available modalities as input is introduced to capture more inter-modal correlations. Furthermore, a Post-Interaction component is proposed to perceive the importance of all branches and dynamically combine their information, thus mitigating the information loss from the strict latent feature alignment. We validate the effectiveness of PPIL on two benchmark datasets, BraTS2020 and BraTS2018, demonstrating a significant improvement in performance under severe missing-modality scenarios while preserving competitive performance when most modalities are available.