Automatic Liver Segmentation and Hepatic Fat Fraction Assessment in MRI

Automated assessment of hepatic fat fraction is clinically important. A robust and precise segmentation would enable accurate, objective and consistent measurement of liver fat fraction for disease quantification, therapy monitoring and drug development. However, segmenting the liver in clinical tri...

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Bibliographic Details
Main Authors: Zhennan Yan, Chaowei Tan, Shaoting Zhang, Yan Zhou, Belaroussi, Boubakeur, Hui Jing Yu, Miller, Colin, Metaxas, Dimitris N.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Computational modeling
deformable model
Deformable models
Image segmentation
Liver
Magnetic resonance imaging
MRI
Robustness
Segmentation
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Summary:Automated assessment of hepatic fat fraction is clinically important. A robust and precise segmentation would enable accurate, objective and consistent measurement of liver fat fraction for disease quantification, therapy monitoring and drug development. However, segmenting the liver in clinical trials is a challenging task due to the variability of liver anatomy as well as the diverse sources the images were acquired from. In this paper, we propose an automated and robust framework for liver segmentation and assessment. It uses single statistical atlas registration to initialize a robust deformable model to get fine segmentation. Fat fraction map is computed by using chemical shift based method in the delineated region of liver. This proposed method is validated on 14 abdominal magnetic resonance (MR) volumetric scans. The qualitative and quantitative comparisons show that our proposed method can achieve better segmentation accuracy with less variance comparing with an automatic graph cut method. Experimental results demonstrate the promises of our assessment framework.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2014.565