SF-Net: A Multi-Task Model for Brain Tumor Segmentation in Multimodal MRI via Image Fusion

Abstract

Automatic segmentation of brain tumor regions from multimodal MRI scans is of great clinical significance. In this letter, we propose a “Segmentation-Fusion” multi-task model named SF-Net for brain tumor segmentation. In comparison to the widely-used multi-task model that adds a variational autoencoder (VAE) decoder to reconstruct the input data, using image fusion as an additional regularization for feature learning helps to achieve more sufficient fusion of multimodal features, which is beneficial to the multimodal image segmentation problem. To further improve the performance of the multi-task model, an uncertainty-based approach that can adaptively adjust the loss weights of different tasks during the training process is introduced for model training. Experimental results on the BraTS 2020 benchmark demonstrate that the proposed method can achieve higher segmentation accuracy than the VAE-based approach. In addition, as the by-product of the multi-task model, the image fusion results obtained are of high quality on the brain tumor regions. The source code of the proposed method is available at https://github.com/yuliu316316/SF-Net.