Zhao Liang, Ma Jiajun, Shao Yu, Jia Chaoran, Zhao Jingyuan, Yuan Hong
School of Software Technology, Dalian University of Technology, Dalian, China.
Stem Cell Clinical Research Center, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Front Oncol. 2022 Aug 18;12:950706. doi: 10.3389/fonc.2022.950706. eCollection 2022.
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.
全球脑肿瘤的年发病率约为十万分之七，占所有肿瘤的2%。死亡率在12岁以下儿童中排名第一，在成年人中排名第十。因此，脑肿瘤图像的定位和分割构成了医学研究的一个活跃领域。传统的手动分割方法耗时、费力且主观。此外，单一图像模态提供的信息往往有限，无法满足临床应用的需求。因此，在本研究中，我们采用多编码器和单解码器结构，开发了一种用于脑肿瘤分割的多模态特征融合网络MM-UNet。在所提出的网络中，每个编码器独立地从相应的成像模态中提取低级特征，并且混合注意力块增强这些特征。通过跳跃连接与解码器路径的高级语义融合后，解码器恢复像素级分割结果。我们在BraTS 2020数据集上评估了所提出模型的性能。MM-UNet的平均Dice分数达到79.2%，平均豪斯多夫距离为8.466，与U-Net、注意力U-Net和ResUNet基线模型相比，性能有持续的提升，证明了所提出模型的有效性。
