Brain tumor segmentation in multimodal MRI via pixel-level and feature-level image fusion

Abstract

Brain tumor segmentation in multimodal MRI volumes is of great significance to disease diagnosis, treatment planning, survival prediction and other relevant tasks. However, most existing brain tumor segmentation methods fail to make sufficient use of multimodal information. The most common way is to simply stack the original multimodal images or their low-level features as the model input, and many methods treat each modality data with equal importance to a given segmentation target. In this paper, we introduce multimodal image fusion technique including both pixel-level fusion and feature-level fusion for brain tumor segmentation, aiming to achieve more sufficient and finer utilization of multimodal information. At the pixel level, we present a convolutional network named PIF-Net for 3D MR image fusion to enrich the input modalities of the segmentation model. The fused modalities can strengthen the association among different types of pathological information captured by multiple source modalities, leading to a modality enhancement effect. At the feature level, we design an attention-based modality selection feature fusion (MSFF) module for multimodal feature refinement to address the difference among multiple modalities for a given segmentation target. A two-stage brain tumor segmentation framework is accordingly proposed based on the above components and the popular V-Net model. Experiments are conducted on the BraTS 2019 and BraTS 2020 benchmarks. The results demonstrate that the proposed components on both pixel-level and feature-level fusion can effectively improve the segmentation accuracy of brain tumors.