ShapeForest: Building Constrained Statistical Shape Models with Decision Trees

Constrained local models (CLM) are frequently used to locate points on deformable objects. They usually consist of feature response images, defining the local update of object points and a shape prior used to regularize the final shape. Due to the complex shape variation within an object class this is a challenging problem. However in many segmentation tasks a simpler object representation is available in form of sparse landmarks which can be reliably detected from images. In this work we propose ShapeForest, a novel shape representation which is able to model complex shape variation, preserves local shape information and incorporates prior knowledge during shape space inference. Based on a sparse landmark representation associated with each shape the ShapeForest, trained using decision trees and geometric features, selects a subset of relevant shapes to construct an instance specific parametric shape model. Hereby the ShapeForest learns the association between the geometric features and shape variability. During testing, based on the estimated sparse landmark representation a constrained shape space is constructed and used for shape initialization and regularization during the iterative shape refinement within the CLM framework. We demonstrate the effectiveness of our approach on a set of medical segmentation problems where our database contains complex morphological and pathological variations of several anatomical structures.