IS = { zkontrolovano 28 May 2012 },
  UPDATE  = { 2012-05-28 },
  title = {Automated Segmentation of a Motion Mask to Preserve Sliding 
           Motion in Deformable Registration of Thoracic {CT}},
  year  = {2012},
  month = {February},
  author = {Vandemeulebroucke, Jef and Bernard, Olivier and Rit, Simon and 
            Kybic, Jan and Clarysse, Patrick and Sarrut, David},
  journal = {Medical Physics},
  publisher = {Amer. Assoc. Physicists in Medicine},
  address = {Melville, USA},
  doi = {10.1118/1.3523619},
  if = {2.704},
  issn = {0094-2405},
  project = {GAP202/11/0111},
  authorship = {20-20-20-20-10-10},
  volume = {39},
  number = {2},
  pages  = {1006--1015},
  keywords = {motion estimation, registration, spatio-temporal, CT},
  annote = {Deformable registration generally relies on the assumption
    that the sought spatial transformation is smooth. Breathing motion
    involves sliding motion of the lung with respect to the chest
    wall. In the case of sliding motion, a discontinuity is present in
    the motion field and the smoothness assumption can lead to poor
    matching accuracy. Many authors have proposed alternative
    registration methods to preserve sliding motion, several of which
    rely on prior segmentations. We focus on a particular,
    subanatomical segmentation, called a motion mask, because it is
    advanta- geous for subsequent registration. The motion mask
    separates moving from less-moving regions, conveniently allowing
    to simultaneously estimate the motion for similarly moving
    tissue. We propose an original method for automatically extracting
    a motion mask from a CT image of the thorax. The obtained
    segmentation is useful for any registration method relying on a
    prior segmentation to account for sliding motion.  The method is
    based on the level set framework, which allows to include
    geometric priors in the definition of the motion mask. To improve
    robustness, the original images are simplified and only clear
    anatomical features are retained, with respect to which the
    segmentation is defined. The resulting procedure comes down to a
    monitored level set segmentation of binary images. The method is
    applied to six inhale-exhale image pairs, and produced satisfying
    results for all patients, consistent with respect to patient
    anatomy. We show that the obtained motion masks can facilitate
    deformable registration of the thorax. By preserving the sliding
    motion, the complexity of the spatial transformation can be
    reduced considerably while maintaining matching accuracy.},
  url = {{ftp://cmp.felk.cvut.cz/pub/cmp/articles/kybic/Vandemeulebroucke-MPh2012.pdf}},
  ut_isi = {000300215800046},