@InProceedings{Dolejsi-SPIE2009,
  IS = { zkontrolovano 01 Feb 2010 },
  UPDATE  = { 2009-11-06 },
  author =      {Dolej{\v s}{\' i}, Martin and Kybic, Jan and Polovin{\v{c}}{\'a}k, 
                 Michal and T{\accent23 u}ma, Stanislav},
  title =       {The {L}ung {TIME}---{A}nnotated Lung Nodule Dataset and Nodule Detection Framework},
  year =        {2009},
  pages =       {72601U-1--72601U-8},
  booktitle =   {Proceedings of SPIE},
  editor =      {Giger, Maryllen L. and Karssemeijer, Nico},
  publisher =   {SPIE},
  address =     {Bellingham, USA},
  ISBN =        {978-0-8194-7511-4},
  volume =      {7260},
  series =      {Medical Imaging 2009: Computer-Aided Diagnosis},
  book_pages =  {1166},
  day =         {7--12},
  month =       {February},
  venue =       {Orlando, USA},
  annote = {The Lung Test Images from Motol Environment (Lung TIME) is
    a new publicly available dataset of thoracic CT scans with
    manually annotated pulmonary nodules. It is larger than other
    publicly available datasets. Pulmonary nodules are lesions in the
    lungs, which may indicate lung cancer. Their early detection
    significantly improves survival rate of patients. Automatic nodule
    detecting systems using CT scans are being developed to reduce
    physicians' load and to improve detection quality. Besides
    presenting our own nodule detection system, in this article, we
    mainly address the problem of testing and comparison of automatic
    nodule detection methods. Our publicly available 157 CT scan
    dataset with 394 annotated nodules contains almost every nodule
    types (pleura attached, vessel attached, solitary, regular,
    irregular) with 2-10mm in diameter, except ground glass opacities
    (GGO). Annotation was done consensually by two experienced
    radiologists. The data are in DICOM format, annotations are
    provided in XML format compatible with the Lung Imaging Database
    Consortium (LIDC). Our computer aided diagnosis system (CAD) is
    based on mathematical morphology and filtration with a subsequent
    classification step. We use Asymmetric AdaBoost classifier. The
    system was tested using TIME, LIDC and ANODE09 databases. The
    performance was evaluated by cross-validation for Lung TIME and
    LIDC, and using the supplied evaluation procedure for ANODE09. The
    sensitivity at chosen working point was 94.27% with 7.57 false
    positives/slice for TIME and LIDC datasets combined, 94.03% with
    5.46 FPs/slice for the Lung TIME, 89.62% sensitivity with 12.03
    FPs/slice for LIDC, and 78.68% with 4,61 FPs/slice when applied
    on ANODE09.},
  keywords =    {Lung TIME, nodules, database},
  authorship =  {40-40-15-5},
  project  =    {GA 102/07/1317, MSM6840770012, NR8314-3/2005},
  psurl =       {[Dolejsi-SPIE2009.pdf]},
  www =         {http://cmp.felk.cvut.cz/~dolejm1/noduledetection/},
}