AUTHOR=Zhao Liang , Ma Jiajun , Shao Yu , Jia Chaoran , Zhao Jingyuan , Yuan Hong TITLE=MM-UNet: A multimodality brain tumor segmentation network in MRI images JOURNAL=Frontiers in Oncology VOLUME=12 YEAR=2022 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.950706 DOI=10.3389/fonc.2022.950706 ISSN=2234-943X ABSTRACT=

The global annual incidence of brain tumors is approximately seven out of 100,000, accounting for 2% of all tumors. The mortality rate ranks first among children under 12 and 10th among adults. Therefore, the localization and segmentation of brain tumor images constitute an active field of medical research. The traditional manual segmentation method is time-consuming, laborious, and subjective. In addition, the information provided by a single-image modality is often limited and cannot meet the needs of clinical application. Therefore, in this study, we developed a multimodality feature fusion network, MM-UNet, for brain tumor segmentation by adopting a multi-encoder and single-decoder structure. In the proposed network, each encoder independently extracts low-level features from the corresponding imaging modality, and the hybrid attention block strengthens the features. After fusion with the high-level semantic of the decoder path through skip connection, the decoder restores the pixel-level segmentation results. We evaluated the performance of the proposed model on the BraTS 2020 dataset. MM-UNet achieved the mean Dice score of 79.2% and mean Hausdorff distance of 8.466, which is a consistent performance improvement over the U-Net, Attention U-Net, and ResUNet baseline models and demonstrates the effectiveness of the proposed model.