Discriminative boundary detection for model-based heart segmentation in CT images

Segmentation of organs in medical images can be successfully performed with deformable models. Most approaches combine a boundary detection step with some smoothness or shape constraint. An objective function for the model deformation is thus established from two terms: the first one attracts the surface model to the detected boundaries while the second one keeps the surface smooth or close to expected shapes. In this work, we assign locally varying boundary detection functions to all parts of the surface model. These functions combine an edge detector with local image analysis in order to accept or reject possible edge candidates. The goal is to optimize the discrimination between the wanted and misleading boundaries. We present a method to automatically learn from a representative set of 3D training images which features are optimal at each position of the surface model. The basic idea is to simulate the boundary detection for the given 3D images and to select those features that minimize the distance between the detected position and the desired object boundary. The approach is experimentally evaluated for the complex task of full-heart segmentation in CT images. A cyclic cross-evaluation on 25 cardiac CT images shows that the optimized feature training and selection enables robust, fully automatic heart segmentation with a mean error well below 1 mm. Comparing this approach to simpler training schemes that use the same basic formalism to accept or reject edges shows the importance of the discriminative optimization.