Details

Autor(en) / Beteiligte

• Zhennan Yan
• Chaowei Tan
• Shaoting Zhang
• Yan Zhou
• Belaroussi, Boubakeur
• Hui Jing Yu
• Miller, Colin
• Metaxas, Dimitris N.

Titel

Automatic Liver Segmentation and Hepatic Fat Fraction Assessment in MRI

Ist Teil von

• 2014 22nd International Conference on Pattern Recognition, 2014, p.3280-3285

Ort / Verlag

IEEE

Erscheinungsjahr

2014

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• 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.