CAN: Context-assisted full Attention Network for brain tissue segmentation

Zhan Li; Chunxia Zhang; Yongqin Zhang; Xiaofeng Wang; Xiaolong Ma; Hai Zhang; Songdi Wu

doi:10.1016/j.media.2022.102710

CAN: Context-assisted full Attention Network for brain tissue segmentation

Med Image Anal. 2023 Apr:85:102710. doi: 10.1016/j.media.2022.102710. Epub 2022 Dec 21.

Authors

Zhan Li¹, Chunxia Zhang², Yongqin Zhang³, Xiaofeng Wang⁴, Xiaolong Ma², Hai Zhang⁵, Songdi Wu⁶

Affiliations

¹ School of Information Science and Technology, Northwest University, 710127, Xi'an, China. Electronic address: lizhan@nwu.edu.cn.
² School of Information Science and Technology, Northwest University, 710127, Xi'an, China.
³ School of Information Science and Technology, Northwest University, 710127, Xi'an, China. Electronic address: zhangyongqin@nwu.edu.cn.
⁴ School of Information Science and Technology, Northwest University, 710127, Xi'an, China. Electronic address: wangxiaofeng@nwu.edu.cn.
⁵ School of Mathematics, Northwest University, 710127, Xi'an, China.
⁶ The First Affiliated Hospital of Northwest University, 710127, Xi'an, China.

PMID: 36586394
DOI: 10.1016/j.media.2022.102710

Abstract

Brain tissue segmentation is of great value in diagnosing brain disorders. Three-dimensional (3D) and two-dimensional (2D) segmentation methods for brain Magnetic Resonance Imaging (MRI) suffer from high time complexity and low segmentation accuracy, respectively. To address these two issues, we propose a Context-assisted full Attention Network (CAN) for brain MRI segmentation by integrating 2D and 3D data of MRI. Different from the fully symmetric structure U-Net, the CAN takes the current 2D slice, its 3D contextual skull slices and 3D contextual brain slices as the input, which are further encoded by the DenseNet and decoded by our constructed full attention network. We have validated the effectiveness of the CAN on our collected dataset PWML and two public datasets dHCP2017 and MALC2012. Our code is available at https://github.com/nwuAI/CAN.

Keywords: Attention; Convolutional neural network; Medical image; Semantic segmentation.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Brain
Head
Humans
Image Processing, Computer-Assisted* / methods
Magnetic Resonance Imaging / methods
Neural Networks, Computer*