Discriminative Learning for Anatomical Structure Detection and Segmentation

  • S. Kevin Zhou
  • Jingdan Zhang
  • Yefeng Zheng


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.


Ground Truth Right Ventricle Image Patch Right Atrium Binary Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Siemens Corporation, Corporate ResearchPrincetonUSA

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