Data-Driven visual tracking in retinal microsurgery

Authors:
Raphael Sznitman;Karim Ali;Rogério Richa;Russell H. Taylor;Gregory D. Hager;Pascal Fua
Affiliations:
École Polytechnique Fédérale de Lausanne, Switzerland;École Polytechnique Fédérale de Lausanne, Switzerland;The Johns Hopkins University, Baltimore;The Johns Hopkins University, Baltimore;The Johns Hopkins University, Baltimore;École Polytechnique Fédérale de Lausanne, Switzerland
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2012

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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.