Liver shape analysis using partial least squares regression-based statistical shape model: application for understanding and staging of liver fibrosis
Overall scheme for modelling the shape variations associated with liver fibrosis stage and predicting it fromMR images by using PLSR-based shape features (scores)
Abstract
Purpose- Liver shape variations have been considered as feasible indicators of liver fibrosis. However, current statistical shape models (SSM) based on principal component analysis represent gross shape variations without considering the association with the fibrosis stage. Therefore, we aimed at the application of a statistical shape modelling approach using partial least squares regression (PLSR), which explicitly uses the stage as supervised information, for understanding the shape variations associated with the stage as well as predicting it in contrast-enhanced MR images.
Mazen Soufi
Assistant Professor
I’m an assistant professor in the Division of Information Science at NAIST, and I apply data-driven approaches to model disease progression from large-scale databases of medical images.