ERV-Net: An efficient 3D residual neural network for brain tumor segmentation

作者：

Highlights：

• We propose an efficient 3D residual neural network for brain tumor segmentation.

• We propose a fusion loss function based on Dice and Cross-entropy.

• We introduce a concise but effective post-processing method.

• The evaluation is performed on the BRATS 2018 dataset.

• The results demonstrate that our method outperforms the state-of-the-art approaches.

摘要

•We propose an efficient 3D residual neural network for brain tumor segmentation.•We propose a fusion loss function based on Dice and Cross-entropy.•We introduce a concise but effective post-processing method.•The evaluation is performed on the BRATS 2018 dataset.•The results demonstrate that our method outperforms the state-of-the-art approaches.

论文关键词：Brain tumor segmentation,3D convolutional neural network,Encoder-decoder,Efficiency,Lightweight,Residual block

论文评审过程：Received 14 November 2019, Revised 29 November 2020, Accepted 31 December 2020, Available online 5 January 2021, Version of Record 22 January 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.114566