基于新型深度全卷积网络的肝脏CT影像三维区域自动分割

doi:10.3969/j.issn.0258-8021.2018.04.001

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Abstract Liver segmentation has important clinical value in liver tumor resection and liver transplantation volume measurement. Because the intensity value of liver and adjacent organs is very close in CT images, the three-dimensional (3D) automated segmentation of the liver region is a challenged task. In order to make the accurate segmentation of liver region, a new deep fully convolutional network (FCN) structure 3DUnet-C2 was proposed. This network made full use of the three-dimensional spatial information of CT image, and combined well the characteristics of shallow and deep layers. In particular, a new network training strategy was proposed. The primary model was obtained by selecting the clear image and intercepting the liver region as a sample. Then the model was leveraged to initialize the network parameters so that the network can converge. Finally, on the basis of the original model, the 3DUnet-C2-CRF model was constructed by using the three-dimensional conditional random field to optimize the liver segmentation boundary. In order to verify the performance of the proposed 3DUnet-C2-CRF on 3D segmentation of liver regions, 100 CT images were chosen from the data set of the ISBI2017 Liver Tumor Segmentation Challenge. The Dice coefficient of the segmentation accuracy of the 3DUnet-C2-CRF model on 20 test images reached 96.9%, which is higher than the Dice coefficient of 3DUnet and Vnet models. Experimental results showed that the 3DUnet-C2-CRF model had better feature expression capability and more generalization performance, which improved the segmentation accuracy of the model.

Key words： liver segmentation deep fully convolutional networks conditional random field

Received: 05 September 2017

PACS:

R318

Corresponding Authors: E-mail: xujung@gmail.com

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	Articles by authors
	Sun Mingjian
	Xu Jun
	Ma Wei
	Zhang Yudong

Cite this article:

Sun Mingjian,Xu Jun,Ma Wei, et al. A New Fully Convolutional Network for 3D Liver Region Segmentation on CT Images[J]. Chinese Journal of Biomedical Engineering, 2018, 37(4): 385-393.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2018.04.001 OR http://cjbme.csbme.org/EN/Y2018/V37/I4/385

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