计算机科学
人工智能
分割
图像融合
任务(项目管理)
模式识别(心理学)
实时核磁共振成像
图像(数学)
图像分割
融合
计算机视觉
磁共振成像
放射科
医学
工程类
哲学
系统工程
语言学
作者
Yü Liu,Fuhao Mu,Yu Shi,Xun Chen
标识
DOI:10.1109/lsp.2022.3198594
摘要
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.
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