title: Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials.
abstract: Automated assessment of hepatic fat-fraction is clinically important. A robust and precise segmentation would enable accurate, objective and consistent measurement of hepatic 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 obtain 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 two other atlas-based methods. Experimental results demonstrate the promises of our assessment framework.
authors: CBIM, Rutgers University, Piscataway, NJ, USA.
authors: Zhennan
authors: Z
authors: Yan
authors: Department of Computer Science, UNC Charlotte, Charlotte, NC, USA. Electronic address: rutgers.shaoting@gmail.com.
authors: Shaoting
authors: S
authors: Zhang
authors: CBIM, Rutgers University, Piscataway, NJ, USA.
authors: Chaowei
authors: C
authors: Tan
authors: Chongqing University of Posts & Telecommunications, Chongqing, China.
authors: Hongxing
authors: H
authors: Qin
authors: BioClinica Inc., Newtown, PA, USA.
authors: Boubakeur
authors: B
authors: Belaroussi
authors: BioClinica Inc., Newtown, PA, USA.
authors: Hui Jing
authors: HJ
authors: Yu
authors: BioClinica Inc., Newtown, PA, USA.
authors: Colin
authors: C
authors: Miller
authors: CBIM, Rutgers University, Piscataway, NJ, USA.
authors: Dimitris N
authors: DN
authors: Metaxas
mesh_terms: D000273:Adipose Tissue / Q000473:pathology*; D050154:Adiposity; D000465:Algorithms; D005234:Fatty Liver / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008099:Liver / Q000473:pathology*; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
keywords: Deformable model; Hepatic fat-fraction assessment; Segmentation; Statistical atlas