title: Real-time markerless lung tumor tracking in fluoroscopic video: Handling overlapping of projected structures.
abstract: OBJECTIVE
  Fluoroscopic imaging is a well-suited technique for online visualization of tumor motion in the thoracic region. Template-based approaches for tumor tracking in such images are commonly used. However, overlapping of different structures, mainly bones, can lead to limited visibility of the projected tumor shape, which in turn can negatively affect the performance of the tracking method. In this study, a method based on multiple-template matching was developed, providing fast and robust detection of tumor motion even under the influence of occurring tumor overlaps.
  
  
  METHODS
  A cohort of 14 patients with varying tumor sizes and locations was investigated. Image data from eight of these patients were used for evaluation. Based on the requirement of tumor visibility, the remaining datasets did not qualify for tracking. Generation of multiple templates was improved by implementation of an algorithm for automated selection of reference images containing the most characteristic tumor appearances. Various measures were taken to ensure real-time capability of the algorithm. A prematching step was introduced in order to reduce dispensable comparison operations by selecting the most appropriate template. Subsequent matching was further optimized by using prior knowledge about likely tumor motion to effectively limit necessary matching tasks.
  
  
  RESULTS
  Tracking accuracy of the developed multiple-template method was compared with that of single-template. Mean errors of the multiple-template approach were 0.6 ± 0.6 mm in left-right and 0.9 ± 0.9 mm in superior-inferior direction in the isocenter plane. The single-template approach achieved mean errors of 0.7 ± 0.7 mm in left-right and 1.5 ± 1.3 mm in superior-inferior direction. These results derive from evaluation against manual tumor tracking performed by four expert observers. Computational times needed for tumor detection in a single fluoroscopic frame ranged between 1 and 29 ms depending on the tumor size and motion amplitude.
  
  
  CONCLUSIONS
  This study shows that in case of tumor overlapping with dense structures, multiple-template tracking provides more accurate results than a single-template approach. The developed algorithm shows promising results in terms of suitability for real-time application and robustness against frequently changing overlapping.
authors.affiliation: Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg D-69120, Germany.
authors.forename: Hendrik
authors.initials: H
authors.lastname: Teske
authors.affiliation: Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg D-69120, Germany.
authors.forename: Paul
authors.initials: P
authors.lastname: Mercea
authors.affiliation: Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg D-69120, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Schwarz
authors.affiliation: Department of Radiation Oncology, University Hospital Heidelberg, Im Neuenheimer Feld 400, Heidelberg D-69120, Germany.
authors.forename: Nils H
authors.initials: NH
authors.lastname: Nicolay
authors.affiliation: Department of Radiation Oncology, University Hospital Heidelberg, Im Neuenheimer Feld 400, Heidelberg D-69120, Germany.
authors.forename: Florian
authors.initials: F
authors.lastname: Sterzing
authors.affiliation: Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg D-69120, Germany and Faculty of Medical Informatics, Heilbronn University, Max-Planck-Strasse 39, Heilbronn D-74081, Germany.
authors.forename: Rolf
authors.initials: R
authors.lastname: Bendl
mesh_terms: D000465:Algorithms; D015331:Cohort Studies; D005260:Female; D005471:Fluoroscopy / Q000379:methods*; D006801:Humans; D008168:Lung / Q000473:pathology; D008175:Lung Neoplasms / Q000473:pathology*; D008297:Male; D010363:Pattern Recognition, Automated; D013997:Time Factors; D014741:Video Recording / Q000379:methods