title: Instrument tracking via online learning in retinal microsurgery.
abstract: Robust visual tracking of instruments is an important task in retinal microsurgery. In this context, the instruments are subject to a large variety of appearance changes due to illumination and other changes during a procedure, which makes the task very challenging. Most existing methods require collecting a sufficient amount of labelled data and yet perform poorly in handling appearance changes that are unseen in training data. To address these problems, we propose a new approach for robust instrument tracking. Specifically, we adopt an online learning technique that collects appearance samples of instruments on the fly and gradually learns a target-specific detector. Online learning enables the detector to reinforce its model and become more robust over time. The performance of the proposed method has been evaluated on a fully annotated dataset of retinal instruments in in-vivo retinal microsurgery and on a laparoscopy image sequence. In all experimental results, our proposed tracking approach shows superior performance compared to several other state-of-the-art approaches.
authors.forename: Yeqing
authors.initials: Y
authors.lastname: Li
authors.forename: Chen
authors.initials: C
authors.lastname: Chen
authors.forename: Xiaolei
authors.initials: X
authors.lastname: Huang
authors.forename: Junzhou
authors.initials: J
authors.lastname: Huang
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D008866:Microsurgery / Q000295:instrumentation / Q000379:methods*; D009862:Online Systems; D013508:Ophthalmologic Surgical Procedures / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012160:Retina / Q000033:anatomy & histology* / Q000601:surgery*; D012680:Sensitivity and Specificity; D025321:Surgery, Computer-Assisted / Q000379:methods*