关键词: 深度学习, 全卷积神经网络, 医学图像分割

Abstract: Abdominal CT images cover problems such as low contrast in adjacent organs and various performance in shape. A liver segmentation model based on fully convolutional neural network is proposed. Firstly, the deep and abstract features of the image are extracted by convolutional neural network. Then interpolated reconstruction is performed through deconvolution operation on the extracted feature map to obtain segmentation results. Due to the simple deconvolution acquiring segmentation results are usually rough. Before deconvolution, it applies characteristics mergence to upper and lower layers, increases the deconvolution-layer amount and reduces deconvolution-step size on the model, then gets accurate segmentation results. Compared to convolution neural network, this model can fully use the spatial information of CT images. Experimental results demonstrate, this model can segment abdominal liver region in CT images and reach much higher accuracy.

Key words: deep learning, fully convolutional neural network, medical image segmentation