An adaptive tracking algorithm of lung tumors in fluoroscopy using online learned collaborative trackers

  • Authors:

  • Baiyang Liu;Lin Yang;Casimir Kulikowski;Jinghao Zhou;Leiguang Gong;David J. Foran;Salma J. Jabbour;Ning J. Yue

  • Affiliations:

  • Computer Science, Rutgers University, Piscataway, NJ and UMDNJ-Robert Wood Johson Medical School, Piscataway, NJ and UMDNJ-Robert Wood Johson Medical School, Piscataway, NJ;UMDNJ-Robert Wood Johson Medical School, Piscataway, NJ and The Cancer Institute of New Jersey, New Brunswick, NJ;Computer Science, Rutgers University, Piscataway, NJ;The Cancer Institute of New Jersey, New Brunswick, NJ;IBM T. J. Watson Research, Hawthorne, NY;UMDNJ-Robert Wood Johson Medical School, Piscataway, NJ and The Cancer Institute of New Jersey, New Brunswick, NJ;The Cancer Institute of New Jersey, New Brunswick, NJ;The Cancer Institute of New Jersey, New Brunswick, NJ

  • Venue:
  • ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
  • Year:
  • 2010

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Abstract

Accurate tracking of tumor movement in fluoroscopic video sequences is a clinically significant and challenging problem. This is due to blurred appearance, unclear deforming shape, complicate intra- and inter- fractional motion, and other facts. Current offline tracking approaches are not adequate because they lack adaptivity and often require a large amount of manual labeling. In this paper, we present a collaborative tracking algorithm using asymmetric online boosting and adaptive appearance model. The method was applied to track the motion of lung tumors in fluoroscopic sequences provided by radiation oncologists. Our experimental results demonstrate the advantages of the method.