@InProceedings{sofka:isbi10,
  author =	 {Michal Sofka and Krist\'{o}f Ralovich and Neil Birkbeck
              and Jigndan Zhang and S.Kevin Zhou},
  title =	 {Integrated Detection Network (IDN) for Pose and Boundary
              Estimation in Medical Images},
  booktitle =    {Proceedings of the 8th International Symposium on
                  Biomedical Imaging (ISBI 2011)},
  year =         2011,
  month =        {30~Mar -- 2~Apr},
  address =      {Chicago, IL},                  
  abstract =	 {The expanding role of complex object detection
                  algorithms introduces a need for flexible architectures
				  that simplify interfacing with machine learning
				  techniques and offer easy-to-use training and detection
				  procedures. To address this need, the Integrated Detection
				  Network (IDN) proposes a conceptual design for rapid
				  prototyping of object and boundary detection systems.
                  The IDN uses a strong spatial prior present in the medical
				  imaging domain and a large annotated database of images to
				  train robust detectors. The best detection hypotheses are
				  propagated throughout the detection network using sequential
				  sampling techniques. The effectiveness of the IDN is
				  demonstrated on two learning-based algorithms: (1) automatic
				  detection of fetal brain structures in ultrasound volumes,
				  and (2) liver boundary detection in MRI volumes. Modifying
				  the detection pipeline is simple and allows for immediate
				  adaptation to the variations of the desired algorithms. Both
				  systems achieved low detection error (3.09 and 4.20 mm for
				  two brain structures and 2.53 mm for boundary).}
}