Stacked Multiscale Feature Learning for Domain Independent Medical Image Segmentation.

In this work we propose a feature-based segmentation approach that is domain independent. While most existing approaches are based on application-specific hand-crafted features, we propose a framework for learning features from data itself at multiple scales and depth. Our features can be easily integrated into classifiers or energy-based segmentation algorithms. We test the performance of our proposed method on two MICCAI grand challenges, obtaining the top score on VESSEL12 and competitive performance on BRATS2012.