title: Data-driven visual tracking in retinal microsurgery.
abstract: In the context of retinal microsurgery, visual tracking of instruments is a key component of robotics assistance. The difficulty of the task and major reason why most existing strategies fail on in-vivo image sequences lies in the fact that complex and severe changes in instrument appearance are challenging to model. This paper introduces a novel approach, that is both data-driven and complementary to existing tracking techniques. In particular, we show how to learn and integrate an accurate detector with a simple gradient-based tracker within a robust pipeline which runs at framerate. In addition, we present a fully annotated dataset of retinal instruments in in-vivo surgeries, which we use to quantitatively validate our approach. We also demonstrate an application of our method in a laparascopy image sequence.
authors.affiliation: Ecole Polytechnique Fédérale de Lausanne, Switzerland firstname.lastname@epfl.ch
authors.forename: Raphael
authors.initials: R
authors.lastname: Sznitman
authors.forename: Karim
authors.initials: K
authors.lastname: Ali
authors.forename: Rogério
authors.initials: R
authors.lastname: Richa
authors.forename: Russell H
authors.initials: RH
authors.lastname: Taylor
authors.forename: Gregory D
authors.initials: GD
authors.lastname: Hager
authors.forename: Pascal
authors.initials: P
authors.lastname: Fual
mesh_terms: D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008866:Microsurgery / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods*; D015203:Reproducibility of Results; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity; D025321:Surgery, Computer-Assisted / Q000379:methods*; D014741:Video Recording / Q000379:methods