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

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

扫码查看
原文链接
  • NETL
  • NSTL
  • Elsevier
Brain tumors are the most aggressive and mortal cancers, which lead to short life expectancy. A reliable and efficient automatic or semi-automatic segmentation method is significant for clinical practice. In recent years, deep learning-based methods achieve great success in brain tumor segmentation. However, due to the limitation of parameters and computational complexity, there is still much room for improvement in these methods. In this paper, we propose an efficient 3D residual neural network (ERV-Net) for brain tumor segmentation, which has less computational complexity and GPU memory consumption. In ERV-Net, a computation-efficient network, 3D ShuffleNetV2, is firstly utilized as encoder to reduce GPU memory and improve the efficiency of ERV-Net, and then the decoder with residual blocks (Res-decoder) is introduced to avoid degradation. Furthermore, a fusion loss function, which is composed of Dice loss and Cross-entropy loss, is developed to solve the problems of network convergence and data imbalance. Moreover, a concise and effective post-processing method is proposed to refine the coarse segmentation result of ERV-Net. The experimental results on the dataset of multimodal brain tumor segmentation challenge 2018 (BRATS 2018) demonstrate that ERV-Net achieves the best performance with Dice of 81.8%, 91.21% and 86.62% and Hausdorff distance of 2.70 mm, 3.88 mm and 6.79 mm for enhancing tumor, whole tumor and tumor core, respectively. Besides, ERV-Net also achieves high efficiency compared to the state-of-the-art methods.

Brain tumor segmentation3D convolutional neural networkEncoder-decoderEfficiencyLightweightResidual block

Zhou, Xinyu、Li, Xuanya、Hu, Kai、Zhang, Yuan、Chen, Zhineng、Gao, Xieping

展开 >

Xiangtan Univ, Minist Educ, Key Lab Intelligent Comp & Informat Proc, Xiangtan 411105, Peoples R China

Baidu Inc, Beijing 100085, Peoples R China

Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China

Xiangtan Univ, Minist Educ, Key Lab Intelligent Comp & Informat Proc, Xiangtan 411105, Peoples R China|Xiangnan Univ, Coll Med Imaging & Inspect, Chenzhou, Peoples R China

展开 >

2021

10.1016/j.eswa.2021.114566

Expert systems with applications

Expert systems with applications

EISCI
ISSN：0957-4174
年,卷(期)：2021.170(May)
  • 38
  • 53