Abstract

Info

Title:: LRAE-Unet: a lightweight network for fully automatic segmentation of brain tumor from MRI

Author(s):: LIN Jiahao; WANG Yu; XIAO Hongbing; SUN Mei; School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China

Keywords:: Keywords: brain tumor LRAE-Unet lightweight residual module lightweight self-attention module average pooling module

Abstract:: Abstract: A lightweight residual attention enhanced Unet (LRAE-Unet) is designed for the fully automatic brain tumor segmentation. LRAE-Unet uses lightweight residual module to solve the problems of gradient disappearance and network degradation when the network layers increases, lightweight self-attention module to suppress the irrelevant areas and highlight the significant features of specific local areas, and enhanced average pooling module with a larger field of perception to reduce the space of feature map, save computing resources and avoid over-fitting. The experiment on BraTS 2019 dataset shows that the proposed method has a Dice similarity coefficient of 91.24%, 88.64% and 88.32% in the segmentations of the whole tumor, tumor core and enhanced tumor, which proves its feasibility and effectiveness for brain tumor segmentation.