R2AU-Net: Attention Recurrent Residual Convolutional Neural Network for Multimodal Medical Image Segmentation

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  • Qiang Zuo
    Electronic Engineering College, Heilongjiang University, Harbin 150000, China
  • Songyu Chen
    Electronic Engineering College, Heilongjiang University, Harbin 150000, China
  • Zhifang Wang
    Electronic Engineering College, Heilongjiang University, Harbin 150000, China
  • Liguo Zhang
    editor

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<jats:p>In recent years, semantic segmentation method based on deep learning provides advanced performance in medical image segmentation. As one of the typical segmentation networks, U-Net is successfully applied to multimodal medical image segmentation. A recurrent residual convolutional neural network with attention gate connection (R2AU-Net) based on U-Net is proposed in this paper. It enhances the capability of integrating contextual information by replacing basic convolutional units in U-Net by recurrent residual convolutional units. Furthermore, R2AU-Net adopts attention gates instead of the original skip connection. In this paper, the experiments are performed on three multimodal datasets: ISIC 2018, DRIVE, and public dataset used in LUNA and the Kaggle Data Science Bowl 2017. Experimental results show that R2AU-Net achieves much better performance than other improved U-Net algorithms for multimodal medical image segmentation.</jats:p>

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