Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:685-688. doi: 10.1109/EMBC.2017.8036917.
In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner. The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning. Experimental results show that the proposed iterative deep learning framework is able to yield excellent medical image segmentation performances for various medical images. The effectiveness of the proposed method has been proved by comparing with other state-of-the-art medical image segmentation methods.
在本文中，我们提出了一种使用迭代深度学习框架的新型医学图像分割方法。我们将迭代学习方法与编码器-解码器网络相结合以改善分割结果，这使得能够以迭代方式精确地定位感兴趣区域（ROI），包括医学图像中复杂的形状或详细的纹理。所提出的迭代深度卷积编码器-解码器网络由两条主要路径组成：卷积编码器路径和带有迭代学习的卷积解码器路径。实验结果表明，所提出的迭代深度学习框架能够为各种医学图像产生出色的医学图像分割性能。通过与其他最新的医学图像分割方法进行比较，证明了所提方法的有效性。
