Precise Segmentation of Multiple Organs in CT Volumes Using Learning-Based Approach and Information Theory

@article{Lu2012PreciseSO,
  title={Precise Segmentation of Multiple Organs in CT Volumes Using Learning-Based Approach and Information Theory},
  author={Chao Lu and Yefeng Zheng and Neil Birkbeck and Jingdan Zhang and Timo Kohlberger and Christian Tietjen and Thomas Boettger and James S. Duncan and Shaohua Kevin Zhou},
  journal={Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2012},
  volume={15 Pt 2},
  pages={462-9}
}
In this paper, we present a novel method by incorporating information theory into the learning-based approach for automatic and accurate pelvic organ segmentation (including the prostate, bladder and rectum). We target 3D CT volumes that are generated using different scanning protocols (e.g., contrast and non-contrast, with and without implant in the prostate, various resolution and position), and the volumes come from largely diverse sources (e.g., diseased in different organs). Three key… CONTINUE READING