分割
人工智能
卷积神经网络
残余物
深度学习
水准点(测量)
计算机科学
特征(语言学)
图像分割
模式识别(心理学)
尺度空间分割
循环神经网络
人工神经网络
算法
哲学
语言学
地理
大地测量学
作者
Zahangir Alom,Chris Yakopcic,Mahmudul Hasan,Tarek M. Taha,Vijayan K. Asari
出处
期刊:Journal of medical imaging
[SPIE - International Society for Optical Engineering]
日期:2019-03-27
卷期号:6 (01): 1-1
被引量:390
标识
DOI:10.1117/1.jmi.6.1.014006
摘要
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.
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