Discriminative Learning for Anatomical Structure Detection and Segmentation

There is an emerging trend of using machine learning approaches to solve the tasks in medical image analysis. In this chapter, we summarize several discriminative learning methods for detection and segmentation of anatomical structures. In particular, we propose innovative detector structures, namely Probabilistic Boosting Network (PBN) and Marginal Space Learning (MSL), to address the challenges in anatomical structure detection. We also present a regression approach called Shape Regression Machine (SRM) for anatomical structure detection. For anatomical structure segmentation, we propose discriminative formulations, explicit and implicit, that are based on classification, regression and ranking.

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