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.

中文翻译：

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R2AU-Net：用于多模态医学图像分割的注意力循环残差卷积神经网络

近年来，基于深度学习的语义分割方法在医学图像分割中提供了先进的性能。作为典型的分割网络之一，U-Net 成功应用于多模态医学图像分割。本文提出了一种基于U-Net的具有注意力门连接的循环残差卷积神经网络（R2AU-Net）。它通过用循环残差卷积单元替换 U-Net 中的基本卷积单元来增强整合上下文信息的能力。此外，R2AU-Net 采用注意力门而不是原始的跳过连接。在本文中，实验是在三个多模式数据集上进行的：ISIC 2018、DRIVE 以及 LUNA 和 Kaggle Data Science Bowl 2017 中使用的公共数据集。