title: AN ADAPTIVE TRACKING ALGORITHM OF LUNG TUMORS IN FLUOROSCOPY USING ONLINE LEARNED COLLABORATIVE TRACKERS.
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.
authors.affiliation: Computer Science, Rutgers University, Piscataway, NJ 08854.
authors.forename: Baiyang
authors.initials: B
authors.lastname: Liu
authors.forename: Lin
authors.initials: L
authors.lastname: Yang
authors.forename: Casimir
authors.initials: C
authors.lastname: Kulikowski
authors.forename: Jinghao
authors.initials: J
authors.lastname: Zhou
authors.forename: Leiguang
authors.initials: L
authors.lastname: Gong
authors.forename: David J
authors.initials: DJ
authors.lastname: Foran
authors.forename: Salma J
authors.initials: SJ
authors.lastname: Jabbour
authors.forename: Ning J
authors.initials: NJ
authors.lastname: Yue