Recurrent residual U-Net for medical image segmentation
Recurrent residual U-Net for medical image segmentation
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DOI：
10.1117/1.jmi.6.1.014006
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发表时间：
2019-01-01
影响因子：
2.4
通讯作者：
Asari, Vijayan K.
中科院分区：
文献类型：
--
作者：
Alom, Md Zahangir;Yakopcic, Chris;Asari, Vijayan K.
基于深度学习（DL）的语义分割方法在过去几年中一直提供最先进的性能。更具体地说，这些技术已成功地应用于医学图像分类，分割和检测任务。一种DL技术，U-Net，已经成为这些应用中最流行的技术之一。本文提出了一种递归U-Net模型和一种递归残差U-Net模型，分别命名为RU-Net和R2 U-Net。所提出的模型利用了U-Net、残差网络和递归卷积神经网络的强大功能。使用这些建议的架构进行分割任务有几个优点。首先，残差单元有助于训练深度架构。其次，使用递归残差卷积层进行特征积累，确保了分割任务的更好特征表示。第三，它允许我们设计更好的U-Net架构，具有相同数量的网络参数，具有更好的医学图像分割性能。在三个基准数据集上对所提出的模型进行了测试，例如视网膜图像中的血管分割、皮肤癌分割和肺部病变分割。实验结果显示，与等效模型相比，分割任务的性能上级，包括一种称为SegNet、U-Net和残差U-Net的全连接卷积神经网络的变体。(C)2019年，摄影光学仪器工程师协会（SPIE）
Deep learning (DL)-based semantic segmentation methods have been providing state-of-the-art performance in the past few years. More specifically, these techniques have been successfully applied in medical image classification, segmentation, and detection tasks. One DL technique, U-Net, has become one of the most popular for these applications. We propose a recurrent U-Net model and a recurrent residual U-Net model, which are named RU-Net and R2U-Net, respectively. The proposed models utilize the power of U-Net, residual networks, and recurrent convolutional neural networks. There are several advantages to using these proposed architectures for segmentation tasks. First, a residual unit helps when training deep architectures. Second, feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks. Third, it allows us to design better U-Net architectures with the same number of network parameters with better performance for medical image segmentation. The proposed models are tested on three benchmark datasets, such as blood vessel segmentation in retinal images, skin cancer segmentation, and lung lesion segmentation. The experimental results show superior performance on segmentation tasks compared to equivalent models, including a variant of a fully connected convolutional neural network called SegNet, U-Net, and residual U-Net. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)