Unsupervised Multimodal Supervoxel Merging Towards Brain Tumor Segmentation

Abstract

Automated brain tumor segmentation is challenging given the tumor’s variability in size, shape, and image intensity. This paper focuses on the fusion of multimodal information coming from different Magnetic Resonance (MR) imaging sequences. We argue it is important to exploit all the modality complementarity to better segment and later determine the aggressiveness of tumors. However, simply concatenating the multimodal data as channels of a single image generates a high volume of redundant information. Therefore, we propose a supervoxel-based approach that regroups pixels sharing perceptually similar information across the different modalities to produce a single coherent oversegmentation. To further reduce redundant information while keeping meaningful borders, we include a variance constraint and a supervoxel merging step. Our experimental validation shows that the proposed merging strategy produces high-quality clustering results useful for brain tumor segmentation. Indeed, our method reaches an ASA score of 0.712 compared to 0.316 for the monomodal approach, indicating that the supervoxels accommodate well tumor boundaries. Our approach also improves by 11.5% the Global Score (GS), showing clusters effectively group pixels similar in intensity and texture.