Supervised methods for perfect segmentation in medical images

We pose the problem of perfect segmentation for regions with ambiguous boundaries. We design machine learning classifiers to identify boundaries and build these into an interactive contouring framework. Experiments using synthetic and multiple sclerosis (MS) textures show the success of the classifiers. Experiments using the contouring tool reveal significant improvement in accuracy and inter/intra-operator variability over freehand delineation in synthetic images. We do not see the same improvement for MS lesions, which are small and their true boundaries undefined. The approach goes some way toward achieving perfect segmentation and extends naturally to other medical applications.