MM-UNet: A multimodality brain tumor segmentation network in MRI images

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.

Spatial-temporal data-augmentation-based functional brain network analysis for brain disorders identification

“…First , individual differences between different subjects are not fully considered in our scheme, which is an important issue for FBN analysis (Folville et al, 2020 ; Schabdach et al, 2022 ) and an important direction for our future improvements. Besides , since multi-modal data is taking an increasingly important place in brain analysis (Jia and Lao, 2022 ; Zhao et al, 2022 ), the performance of employing one-modal data is limited. Compared with the widely used multi-modal data model (Yu et al, 2021 ), how to adapt the scheme to the multi-modal data type is a key point in our future work.…”