A generalized active shape model for segmentation of liver in low-contrast CT volumes

To improve segmentation of normal/abnormal livers in contrast-enhanced/non-contrast CT image using the Active Shape Model (ASM) algorithm; we introduce a generalized profile model. We also intend to accurately detect boundary of liver where it touches nearby organs with similar intensities.

Initial boundary of a liver in a CT slice is found using an intensity-based technique and it is then represented by a set of points. The profile of a boundary point is represented by a generalized edge model and the parameters of the model are obtained using a non-linear fitting scheme. The estimated parameters are used to classify boundary points into genuine and dubious groups. The genuine points are located as the true border of the liver and the locations of dubious points are refined using smoothed spline interpolation of genuine landmarks. Finally, the liver shape is kept inside the "Allowable Shape Domain" using a Statistical Shape Model.

We applied the proposed method on four sets of CT volumes including low/high-contrast, normal/abnormal and public datasets. We also compared the proposed algorithm to conventional and state-of-the-art liver segmentation methods. We obtained competitive segmentation accuracy with respect to recent researches including enhanced versions of the ASM. Concerning conventional Active Shape and Active Contour models, the proposed method improved Dice measure by at least 0.05 and 0.08 respectively. Regarding the MICCAI dataset, we promoted our score from 68.5 to 72.1.

The proposed method alleviates segmentation problems of conventional ASM including inaccurate point correspondences, generalization ability of the model and sensitivity to initialization. The proposed method is also robust against leakage to nearby organs with similar intensities.