Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs

Highlights • The multi-channel fully convolutional networks is designed. • We segment liver tumors from multiphase contrast-enhanced CT images. • We train one network for each phase of CT images and fuse their high-layer features together. • This method can make full use of the characteristics of di...

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Bibliographic Details
Published in:Artificial intelligence in medicine 2017-11, Vol.83, p.58-66
Main Authors: Sun, Changjian, Guo, Shuxu, Zhang, Huimao, Li, Jing, Chen, Meimei, Ma, Shuzhi, Jin, Lanyi, Liu, Xiaoming, Li, Xueyan, Qian, Xiaohua
Format: Article
Language:English
Subjects:
Artificial Intelligence
Automation
Contrast Media - administration & dosage
Databases, Factual
Feature fusion
Fully convolutional networks
Humans
Internal Medicine
Liver Neoplasms - diagnostic imaging
Multi-channel
Other
Predictive Value of Tests
Radiographic Image Interpretation, Computer-Assisted - methods
Reproducibility of Results
Retrospective Studies
Tomography, X-Ray Computed
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Summary:Highlights • The multi-channel fully convolutional networks is designed. • We segment liver tumors from multiphase contrast-enhanced CT images. • We train one network for each phase of CT images and fuse their high-layer features together. • This method can make full use of the characteristics of different enhancement phases of CT images. • The results showed our model provided greater accuracy and robustness than previous methods.
ISSN:0933-3657
1873-2860
DOI:10.1016/j.artmed.2017.03.008