Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation
Zhongzhen Huang Linda Wei Shaoting Zhang Xiaofan Zhang
Combining images from multi-modalities is beneficial to explore various
information in computer vision, especially in the medical domain. As an
essential part of clinical diagnosis, multi-modal brain tumor segmentation aims
to delineate the malignant entity involving multiple modalities. Although
existing methods have shown remarkable performance in the task, the information
exchange for cross-scale and high-level representations fusion in spatial and
modality are limited in these methods. In this paper, we present a novel
Modality Aware and Shift Mixer that integrates intra-modality and
inter-modality dependencies of multi-modal images for effective and robust
brain tumor segmentation. Specifically, we introduce a Modality-Aware module
according to neuroimaging studies for modeling the specific modality pair
relationships at low levels, and a Modality-Shift module with specific mosaic
patterns is developed to explore the complex relationships across modalities at
high levels via the self-attention. Experimentally, we outperform previous
state-of-the-art approaches on the public Brain Tumor Segmentation (BraTS 2021
segmentation) dataset. Further qualitative experiments demonstrate the efficacy
and robustness of MASM.